Many of the educational conversations about artificial intelligence are focused on the direct instructional process. However, AI can be harnessed for creating instructional materials including worksheets, problem sets, quizzes, and in this particular case, presentation materials.

AI cannot replace a presenter’s knowledge, judgment, or personal connection with an audience, but it can reduce the time spent on repetitive tasks and provide useful support throughout the presentation process. Educators and students can use AI to brainstorm ideas, organize information, improve slide design, rehearse delivery, and make content more accessible.

The most effective approach is to treat AI as a creative assistant in the creation of materials. Human users should provide the purpose, audience, and subject expertise, then review and refine everything the tool produces.

With that balance in mind, here are several ways AI can improve presentations.

1. Start With a Clear Plan

A strong presentation begins with a clear objective. Before opening a slide-design tool, ask what the audience should understand, remember, or do by the end. AI can help turn a broad topic into a focused presentation goal and suggest an appropriate structure. In the case of instructional slide decks, you should look to the student learning objectives (SLOs).

For example, a teacher might ask an AI assistant to propose an outline for a 10-minute presentation on renewable energy for middle school students given a specific set of SLOs. The assistant could suggest an opening question, a brief explanation of key concepts, examples of renewable technologies, and a concluding activity. The teacher can then adjust the outline to match the curriculum, students’ prior knowledge, and the available time.

AI can also help identify gaps in a draft outline. Asking, “What important concept might a beginner need before learning this topic?” may reveal that the presentation requires additional context or a simpler explanation. This type of questioning can help presenters build a more logical sequence instead of treating slides as a collection of disconnected facts.

2. Brainstorm Examples and Attention-Grabbing Openings

The opening moments of a presentation are important to establish relevance and encourage the audience to pay attention. AI can generate possible hooks, including thought-provoking questions, short scenarios, analogies, historical moments, or real-world problems. In traditional courses, it can be asked to tie into the previous class session’s topic to help make an effective transition.

For a lesson about data privacy, an AI tool might suggest beginning with a fictional scenario in which a student receives an advertisement related to a recent online search. The presenter can use that scenario to introduce the idea of personal data and digital tracking.

AI-generated examples should be checked carefully, especially if any include statistics, current events, or claims about real people and organizations.

3. Improve Slide Structure and Reduce Text

One of the most common presentation problems is overcrowded slides. AI can review slide text and recommend ways to shorten paragraphs, group related ideas, or separate a complex slide into two or more focused ones.

Instead of placing an entire explanation on a screen, presenters can use AI to identify the main point and convert supporting details into speaker notes. A slide might contain a concise heading, three key terms, and a simple diagram, while the presenter provides the full explanation aloud. This design keeps the audience focused on the speaker rather than distracting them with dense blocks of text.

AI can also suggest transitions between sections. A transition such as, “Now that we understand how the problem occurs, let’s examine how it can be solved,” helps audiences follow the argument and understand why the next topic matters.

4. Create Visuals That Support the Message

Presentation software with AI features can recommend layouts, color combinations, icons, charts, and images. These tools may also transform a list of ideas into a visual timeline, process diagram, or comparison table. Such features are useful when a presenter has strong content but limited design experience.

However, visual design should serve communication rather than decoration. Presenters should choose images and graphics that clarify an idea, show relationships, or create an appropriate emotional response. An attractive but irrelevant image can be distracting. AI-generated images should be reviewed for accuracy, context, and potential bias, and presenters should follow copyright and school or organizational policies.

When using charts, AI can help select a suitable format. A trend over time might work best as a line chart, while a comparison of categories may be clearer as a bar chart. The presenter still needs to verify the data, label the chart accurately, and avoid implying conclusions that the data does not support.

5. Adapt Content for Different Learners

AI can help make presentations more inclusive by creating alternate explanations of the same concept. A presenter might request a plain-language definition, an analogy, a step-by-step explanation, or a version appropriate for a particular reading level. This is especially useful in classrooms with varied levels of background knowledge.

AI can also assist with translation, captions, transcripts, and speaker notes. These features may help multilingual learners and audience members who benefit from reading as they listen. Automated captions and translations should be reviewed because errors can occur with names, technical vocabulary, accents, and specialized terminology.

AI can also help presenters adapt an opening for different audiences. A technical explanation may be appropriate for professionals but confusing for high school students. By describing the audience and the desired tone, a presenter can request several versions and select the one that feels most natural for each group of learners.

Another useful application is creating a glossary of important terms. Presenters can introduce the glossary before the lesson or provide it as a handout. Defining unfamiliar language helps students focus on the underlying ideas instead of trying to infer every term from context.

6. Practice Delivery With an AI Coach

A well-designed presentation can still lose impact if the delivery is rushed, too quiet, or difficult to follow. AI-powered rehearsal tools can analyze a practice recording and provide feedback on pacing, filler words, pauses, pronunciation, and sometimes eye contact or vocal variety.

Presenters can use this feedback to set specific goals. For example, they might practice slowing down during complex explanations, pausing after an important question, or replacing repeated phrases such as “um” and “basically.” Students can also rehearse answers to likely questions by asking an AI tool to act as an audience member with different levels of knowledge.

AI feedback should be viewed as one source of information, not a final judgment. Natural communication does not require a perfectly uniform speaking style. A presenter’s personality, enthusiasm, and interaction with the audience remain key parts of an effective presentation.

One of my graduate students has AI generate a second version of each of his instructional videos. He gives the students the opportunity to view either set of videos in his flipped classroom. The majority choose the AI-generated videos, because these tend to be shorter. We are not sure yet if there will be achievement differences at the end of the course based on which set of videos students viewed.

7. Generate Questions and Interactive Activities

AI can help turn a passive slide deck into an interactive experience. Presenters can ask an AI assistant to create quick polls, discussion prompts, comprehension checks, case studies, or exit-ticket questions based on the presentation content.

For a presentation on climate science, for example, the presenter might pause after explaining a feedback loop and ask the audience to predict what would happen if one factor changed. For a business presentation, participants might compare two possible solutions and defend their choice. These activities encourage audiences to process information rather than simply receive it.

Questions generated by AI should be reviewed for clarity and difficulty. A good question should connect directly to the presentation’s objective and give the audience enough information to respond meaningfully.

8. Check Accuracy and Protect Trust

AI can produce incorrect, outdated, or unsupported information. It may invent citations or present uncertain claims with confidence. Every statistic, quotation, date, and factual statement should be verified using reliable sources before it appears in a presentation. Human agency and human judgment need to be exercised at each step in the development process.

Presenters should protect private information. Sensitive student, customer, or organizational data should never be entered into an AI system unless the tool has been approved for that use and appropriate safeguards are in place. When AI has made a significant contribution to a presentation, it may be appropriate to acknowledge that assistance according to local policies.

A Presentation Partner

AI can improve presentations by helping plan more effectively, design clearer slides, create useful visuals, adapt content, practice delivery, and engage audiences. Its greatest value is not producing a finished deck with a single prompt; it is supporting the many decisions that make a presentation understandable and memorable.

The best workflow combines AI’s speed with human expertise. When used thoughtfully, AI gives presenters more time to focus on what technology cannot replace: insight, empathy, judgment, and meaningful communication. I have found that AI is good about identifying places in a presentation where I chunked information by making assumptions that might not be clear to a novice about steps in a process.

The author used MS Copilot to assist in the development of this article.

All the focus on AI cheating is eroding trust between teachers and students. That’s one of the conclusions of a new report from the Center for Digital Thriving at Harvard that sampled more than one thousand educators and included interviews with dozens of students.

Teachers are worried about students using AI to cheat, while students are worried about being accused of cheating and that their teachers are using AI in ways they shouldn’t, explains Dr. Beck Tench, a co-author of the AI education policy report.

“The suspicion that students have of teachers is that they are using AI to grade their papers or create their lesson plans or write their recommendation letters and all of that stuff, too,” Tench says.

All this suspicion creates an atmosphere that is not conducive to learning.

“Students and teachers are both entering the classroom with a kind of two-way suspicion, suspecting each other,” Tench says. “And that's eroding trust, which is eroding what they need in order to teach and learn.”

Fortunately, there are ways to move beyond this distrust that Tench and her co-authors share in the report.

“We think it's really important for adults to approach young people with curiosity and empathy," she says. "So we have, in the report, three mindset shifts we recommend exploring in order to approach students with more empathy and curiosity.”

1. Moving From Assuming To Asking

“Instead of assuming students are using AI for everything, ask what they're using AI for,” Tench says. This is a simple mindset shift that can have a big impact, she adds.

AI has a tendency to make students invisible in many different ways, from hiding what they’ve learned if they’re using the tools in submitting papers to hiding their passions and interests if they’re turning to a chatbot instead of a teacher with questions.

Additionally, it can make student achievement invisible, as Tench says that some students dial back their best work because they’re afraid it will trigger an AI detection tool.

“All of these invisibilities, you can tap into, you can make them visible by having conversations about the technology,” Tench says. “The technology itself is making those things invisible, and the conversation about the technology makes it visible. And so if you talk about how you're using the tools, if you talk about why you feel pressured to hide your use of the tools, if you talk about all the different ways a tool can be used and whether or not this one is or isn't okay, you start to uncover the things that the tool is hiding.”

2. Moving From Centering The Technology to Centering The Student

Once again, this involves a small shift in the types of questions you ask. So instead of a technology-centered question such as, “What are your feelings about AI?” Tench recommends student-centered questions such as, “What’s important to you right now? What do you care about? And, is technology making that easier or harder?”

As part of this, it's helpful for students to realize that they are in the same boat as their classmates and their teachers.

“We consider our use of technology as sort of an individual habit, and an individual set of goals and experiences,” Tench says. “And the reality is that it’s very hard to change a specific tech behavior, as an individual.”

AI chatbots, such as smartphones and social media before, have become such an important aspect of society that it's hard for students to opt out of using these on an individual level. But Tench says that discussing what students value as a group can help you decide what limits to place on technology.

She uses a phone-free family dinner as an example. By saying, “We all care about connections, so we are going to put our phones in a stack at dinner so that none of us are looking at our phones,” is "a heck of a lot easier to do than every individual person trying to avoid a phone during dinner.”

3. Moving From 'Us Versus Them' To 'Us And Them'

“It's true that everything that students are experiencing with these tools, adults are also experiencing with these tools,” Tench says. “If we can realize this is a problem that we can tackle together and are each part of that equation, both the young people and the adults have something to contribute to our collective way forward, we're gonna have a more positive relationship as we approach whatever that solution is.”

Doing this involves recognizing the vital elements that young people bring to the conversation and approaching them with genuine interest.

“To be curious about what they experience, and what they think will help us all navigate toward a greater future, instead of basically moving into a stance of being very protective and controlling of their tech behaviors,” Tench says. “That creates an 'us versus them,' a situation where you lay down a rule that they work around and do anyway, right? But when you include young people on the policies, on the thinking around what's happening, on the collective imagining of a better future, that is a much more productive way to go about confronting some of these issues. Because we're all facing this reality. It’s not just young people. So having them help in creating a better future, is better for them, and it's better for us.”

Sponsored by Incident IQ and moderated by Jamie Gahunia, Director of Product Marketing at Incident IQ, this webinar featured insights from Eduardo "Eddie" Coello Amado, Technology Asset Manager at the School District of Osceola County, Chris Warden, K-12 tech leader and podcast host, and Jason Martin, Chief Technology Officer at Incident IQ.

The discussion explored the intersection of shrinking operational budgets, understaffed IT teams, and the strategic deployment of AI to optimize support workflows in K-12 school districts.

Operating in high-volume environments—such as Osceola County's support network of 60+ schools, 66,000 students, and 200,000 tracked assets—creates significant friction. A primary pain point identified by the panel is the "invisible workload" caused by vague, incomplete support requests (e.g., tickets stating merely "it doesn't work").

Investigating unrouted or poorly documented tickets leads to constant back-and-forth communication between technicians and teaching staff, accumulating 15 to 30 hours of lost productivity baseline per issue and potentially extending resolution timelines by days or weeks. Because classroom technical interruptions directly halt student learning, minimizing resolution time is paramount to educational success.

WATCH THIS WEBINAR ON-DEMAND

Key Takeaways

The discussion highlighted critical strategies and architectural considerations for successfully integrating AI into school district operations:

Address Skepticism with Phased Rollouts: K-12 leaders often approach AI with grounded caution. Successful adoption requires piloting tools internally with Tier I through Tier IV support technicians first. Gathering feedback, refining internal knowledge base documentation, and testing natural language intake across varying technical skill levels ensures system reliability before exposing AI interfaces directly to teachers and administrative staff.

Enforce Strict Data Privacy & Security Governance: Protecting sensitive student data (and being FERPA/COPPA compliant) and HR data is the primary hurdle for district leadership. Vendors must guarantee zero data retention policies in which AI processes inputs in memory without using district data to train external large language models. Architecture must enforce role-based permission boundaries, automatic data-masking algorithms for personal identifiable information, and complete digital audit logging ("chat replay") to maintain transparency.

Optimize Intake and Deflection via AI Ticket Assistants: AI tools transform user descriptions into structured issue categories without requiring end-users to navigate complex dropdown menus. Diagnostic prompts narrow down hardware or software faults automatically, while integrated knowledge base articles surface quick self-service fixes that deflect routine tickets before reaching technician queues.

Streamline Technician & Administrative Workflows: Future AI capabilities extend beyond ticket intake to include automated summary generation, step-by-step resolution guides, natural language database queries ("Ask" search bar), single-sentence bulk action execution, and multi-week strategic forecasting agents for fleet refresh budgeting.

Maintain Clear Communication and The Human in the Loop: AI serves as an operational force multiplier rather than a total replacement for human staff. District IT departments must actively communicate the purpose of AI initiatives to alleviate fears, set expectations, and ensure humans retain review authority over automated plans before execution.

Artificial intelligence was used in the transcription of this event.

From an early age, children are asked what they want to be when they grow up. They are told that the classes they take and the skills they develop will prepare them for that future career. By middle and high school, conversations shift toward college, technical programs, internships, and the specific skills employers are seeking.

When I was in school, I remember being part of these conversations. In my own classroom, I have engaged with students in similar ways, curious about what they were thinking about for their future and wanting to support them.

But with so many changes in technology–especially with AI accelerating that by creating new roles are emerging, evolving existing jobs, and supporting tasks that once required significant time–how do we prepare students for jobs that do not exist yet?

AI is Changing Tasks, Not Just Job Titles

One of the most frequent questions that comes up in conversations I've had about AI and the future of work is, "Which jobs will AI replace?" It's an important question, but we also need to consider how AI is evolving the tasks involved in any job.

For many professions, AI will start by changing individual responsibilities long before it completely eliminates an entire occupation. Students in my classroom and I have discussed which jobs might be replaced, and why or why not. We have even explored Will Robots Take My Job? and then analyzed each job to determine why the results showed a high likelihood.

Some of the professions we discussed included teachers, attorneys, healthcare professionals, engineers, marketers, and software developers. Many in these fields will likely continue working in their professions, although how they do their work may change substantially.

Some tasks may become automated, while others will become faster. New responsibilities may emerge because AI-generated information needs to be evaluated, decisions must be made about its use, and people affected by those decisions need to be considered. Problems will also arise that technology cannot solve on its own.

With these considerations, students need to continue thinking about the relationship between people and technology, rather than simply learning how to operate technology.

Some questions I ask students include:

• What does AI do well?
• Where does it struggle?
• When does a person need to intervene, and why?
• What happens when the output is wrong?
• Who is responsible for the final decision?

These are career-readiness questions that help students develop technology skills, discernment, critical thinking, and reflection.

Human Skills Are Still Important

As technology advances, the thought may be that developing technical skills will matter more than human skills, but I believe the opposite is true. With so much technology in our world, communication, curiosity, creativity, collaboration, adaptability, empathy, critical thinking, and judgment will become even more valuable because of AI.

Here are some examples: AI can generate a polished response, but we still need to determine whether that response is useful and accurate. It can generate dozens of ideas within seconds, but deciding which idea is worth pursuing requires judgment. AI can summarize information, but knowing what questions to ask about that information remains important.

While technology is capable of automating parts of a process, having a true understanding of everything and everyone impacted by the process and how to adjust is critical. And something that is still an essential human quality.

Students need opportunities to develop these capabilities, and that means tasking them with problems that do not always have one correct answer. It includes asking them to do more than just answer, but to provide an explanation for their decisions, to collaborate, to revise their ideas and respond to feedback, and to work through any challenges that arise rather than backing away from any source of difficulty.

It's also necessary to create learning experiences that move students beyond simply consuming information and involve them in designing, building, questioning, creating, and finding problems to solve, and then presenting solutions for these.

Students Need AI Literacy, Not Just AI Skills

AI literacy is now essential for career readiness. Students need to know how to write and evaluate prompts, and understand that AI-generated output may be inaccurate or biased. They also need to know how to stay safe and what information should not be shared with an AI platform.

When working with AI, they need to consider intellectual property, transparency, privacy, and responsible use. And what I consider to be very important, developing the judgment to decide when and if AI should be used at all.

As students enter the workplace, they may not always be provided with clear guidelines about when AI is appropriate, as they might be in school, and may need to make those decisions by themselves. If we teach students how to use AI without helping them think critically about its use, we are teaching only part of what they will need to succeed.

Career Exploration Needs Some Change, Too

When I was in high school, career exploration traditionally introduced students to existing professions. That continues to be important, however, for jobs that don’t exist yet, it can be tough for students to imagine the possibilities.

After thinking more about this, I think we need to push student career thinking with some “what-ifs” to explore their interests. They need to consider how AI might change a potential career, what new responsibilities might be required, and which parts of that career require uniquely human strengths.

Another option is to task students with brainstorming what problems might need to be solved five or ten years from now. Engaging them in challenge-based or problem-based learning opportunities might be a good way to spark these conversations and exploration into how the world of work is evolving.

Some ideas to involve students could be having them interview professionals about how their work has been impacted by AI and the changes they are experiencing now. Students can also conduct research on emerging or fast-growing fields of work, redesign a current job description by predicting how it might change in ten years, or identify problems in their communities and come up with ideas about the different types of careers that may develop to address these.

Over the past ten years, I have shifted the questions I use in my own classroom. Rather than asking, “What do you want to be?” I ask, “What problems do you want to help solve?” I then push students to see if technology can be a part of the solution. These questions have led to interesting discussions about existing careers and some that have not been invented yet.

Make Room for Uncertainty

Students come to our classrooms with questions, concerns, and sometimes doubts about their own knowledge. One of the most important things we can model is that we do not have all the answers about what comes next.

While there are people who research and provide insights about the future workforce, predictions about AI, employment, and the essential workforce skills will continue to change as technology evolves. Knowing that can help us guide students to be prepared for change in general and be comfortable when it happens. Developing adaptability and resiliency skills will be highly beneficial.

We should also be creating experiences in which students must iterate because the first solution does not work. We need to provide opportunities to explore new tools, try new strategies, collaborate, receive feedback, adjust, and try again. Students need to understand and embrace productive struggle, and realize that mistakes are often the starting point for learning.

Preparing Students for Work and Beyond

Once they enter the workforce, students will likely work across multiple roles, organizations, and perhaps even entirely different fields over the course of their lives. In our highly digital world, technologies will continue to change, some of the skills in demand today will evolve, and new ones will emerge. And because of all these changes, career readiness is no longer about simply preparing students to have the skills necessary for their first job, but instead being prepared and willing to keep learning after that.

Certain careers may require specific technical knowledge, but any job will also require adaptability, creativity, curiosity, communication, critical thinking, ethical judgment, and the confidence to be faced with something unfamiliar and be able to figure out what to do next.

AI has changed the approach to career readiness, but it has also made schools aware of the need to shift the focus and the conversation. While we may not be able to predict exactly which jobs will be available for our students, we can prepare them to confidently enter an ever-changing world with the skills and mindset to navigate whatever work offers them.

A new question to consider as we think about career readiness is: “Are we preparing students to keep learning when the job, and the world around it, changes?”

November 30 marks the four-year anniversary of ChatGPT’s launch. In that time as a writing professor, I’ve seen much of the work I do change dramatically, and not for the better. The undergraduate courses I teach are riddled with AI writing, and I’m not alone.

The New York Times reported this summer that between two-thirds and 90 percent of high school and college students are using AI for schoolwork. A recently published study of 77 students by researchers at Virginia Tech found that only six students refrained from using AI to help them write, and nearly half of the students used AI and asked it to write their entire essays.

Individual teachers and institutions have come up with creative ways to overcome AI challenges by designing assessments that are inherently difficult for AI to ace, but collectively, the field of education, and higher education in particular, has not done enough to prevent AI misuse when it comes to writing. Instead, by and large, education has treated the problem of AI writing as an abstract and unconquerable evil, such as human suffering.

But the challenge of AI-generated writing in the classroom has always seemed like a fixable issue to me, if only there was a greater, widespread commitment to fighting it. Regardless of what you think of AI use overall, the idea that instructors should be able to ensure their students are actually writing what they say they are writing shouldn’t be controversial. Nor should we give up on trying to suppress the flood of AI writing.

Here are some steps institutions could take unilaterally or collectively to push back the ever-rising tide of AI.

1. Embrace AI Watermarks

AI watermarks act as a kind of digital fingerprint on AI work. The technology already exists to implement these, and it's likely soon be required for most major AI platforms, particularly to comply with the European Union Artificial Intelligence Act, which requires watermarking.

Educators and education leaders should loudly applaud these efforts and be prepared for when watermarks are rolled out in the U.S. Consequently, schools and universities should issue clear guidance for students and teachers on how AI watermarking will be tested by educators as well as what the penalties and procedures will be once an AI watermark is detected in a student's work.

2. Use Effective AI Detection Tools

AI detection tools emerged early as an obvious hedge against inappropriate AI use for writing. The problem is that many of these were initially terrible and resulted in false positives and nightmare accusations for students, which caused many in the field to immediately dismiss them. That was a mistake.

With the right procedures in place, AI detection tools can be utilized fairly. For example, Pangram has very high accuracy rates according to independent studies, and the company says its rate of falsely identifying a human-generated document as AI-generated is roughly 1 in 24,000. These numbers should continue to be assessed. If those rates hold up, there’s no reason this or other effective AI detection tools can't be used by educators.

3. Put Collective Pressure on Tech Companies

AI technology is evolving and no response to it in the school setting is perfect . . . for the corporations creating the platforms! Not so much for students and educators.

So far, the vast majority of AI misuse in school involves only a few platforms. Still, these solution providers need to take more responsibility in addressing what they’ve done to traditional writing assignments in schools.

If a new smartphone was released that could hack into school data centers and change student grades, educators would be outraged and demand these tools be altered to protect student learning. And yet, this is essentially what is happening with AI writing.

As educators, we should demand tech companies lead the way in ensuring their products don’t obliterate academic integrity. I’m not naive enough to think these types of efforts would actually be effective, but there’s something to collectively and symbolically letting the tech world know that they are causing problems and that we can fix these problems with their help.

4. Greater Emphasis on Originality in Writing

If there’s one bright side to the surge in AI-generated generic writing, it’s that it has demonstrated just how vital creativity in writing is. We should have been fostering this more long before the AI revolution, but it’s become increasingly more critical now.

Writing assignments should not be used merely as ways for students to demonstrate their learning, but as opportunities for students to show their creativity, as well as take conversations and ideas further and in new directions than previously. We should start weighing originality and unique research and perspectives more strongly in our rubrics.

Additionally, we should be unabashed about giving lower marks for work that either was written by AI or sounds as though it could've been written by AI. This might be somewhat subjective, but no more so than many aspects of writing we grade, such as tone or strength of argument.

By centering creativity and originality in our approach to writing and grading, we’ll be establishing a foundation for human writing that can hopefully help stand up to the current generation of AI tools and subsequent iterations. I don’t think we’ll ever return entirely to the pre-ChatGPT days of student writing, but with some of the strategies discussed here, we can get a whole lot closer.

Google Search has fully integrated AI through a feature called AI Overviews. For some time now, if you phrase your query as a question such as, “When is the next train from Brewster to Grand Central?” the tool will tell you the times the train is leaving . . . except I wouldn't trust that information.

According to one recent analysis conducted for The New York Times, Google AI Summaries are accurate about 9 out of 10 times, which tracks with my experience. Whenever I ask a series of questions around a subject I'm well-versed in, I almost always find minor errors and often notice outright falsehoods, not to mention the aforementioned inaccuracies with train schedule-related questions in my area.

This led me to realize that Google’s AI summaries can be a great tool for teaching AI literacy and giving students tangible insights into AI hallucinations, limitations, sourcing, and more.

To this end, I’ve designed a short assignment. I ask students to use Google’s AI Summary tool for a subject they know well. Then they have to ask it to provide a bio of them or information on their school or the time of their local train or when their favorite sports team is playing. I require that students keep querying until they find falsehoods, which can sometimes take a little time. Then I ask them to verify the sources and explain why and how AI got it wrong.

Though I’ve only recently implemented it, I find this AI exercise enforces the lessons discussed below.

1. Google AI Overviews (And AI Outputs In General) Need Verification

This is an obvious lesson, and one that students already know in theory, but in practice, I find they don’t apply it. Students, and many people in general, will insist something is correct because AI told them, even though they know AI gets things wrong frequently.

Forcing students to confront AI accuracy with this assignment can cut through some of the cognitive dissonance. It’s harder to dismiss AI mistakes when those mistakes are about you or something you know very well, such as your town or school.

2. Source Verification

When I asked Google what nonfiction author David Grann’s next book is, it said he is writing a new book about the Nobel Oil empire. When I clicked on the source to verify this, I was led to Grann’s social media page. As I explored, I realized he was not working on these books but had posted about books on these topics from other authors.

Having students go through a similar process with source verification provided by Google’s AI summary can teach them not only to better spot AI falsehoods but also about source verification more broadly. Even with human-generated work, sometimes when you go back to a cited source, you find it didn’t frame the claim the same way or, in some cases, that the author is making claims the source material can’t support.

Therefore, this type of fact-checking is a valuable skill for AI verification and beyond.

3. AI's Inner Workings

The mistakes this type of tool makes can hint at larger problems and concerns with AI. Any student who knows how to use the internet can look up train times and report back on arrivals and departures.

Powerful AI tools know how to search the internet with an efficiency beyond what any human could dream of achieving, yet these also can’t be trusted with the same simple task. This happens because AI engines don’t verify facts the way a human might. Instead, most current-generation models predict phrases and statements through pattern recognition. This has led some to refer to them as “stochastic parrots,” though that terminology is sometimes derided, correctly, I believe, as an oversimplification.

Either way, the fact that many AI models can’t be trusted to perform a relatively simple task is a good starting point for discussions about the strengths and limitations of these tools, and a direct example of how something about how these operate very differently than humans.

4. Learning When And How To Use AI

Despite all this, I’m not advocating for students to avoid using Google’s AI Overview or some other AI tool. When Google's AI Overview works, it can be a helpful. In fact, one of the main AI uses I’ve found personally useful is as an alterative to a search engine.

Google AI Overviews often get me the information I’m looking for quicker than a traditional search might, and I like seeing what it comes up with before I embark on a traditional search. I’ve just learned not to trust it and always to verify what it says, even on small, seemingly trivial items, such as when the next train is arriving at the station.

Finding these uses and learning the limitations of AI is something important for students to realize as well. Whether we like it or not, we're all interacting with AI content frequently, and becoming more skilled at critically absorbing that content is a necessity for everyone.

Engaging in this type of conversation with students has helped me better focus my own AI use, and I hope has done the same for them. Now, I just need someone to help me figure out my local train schedule.

AI watermarking could soon provide a way for educators to tell whether students have used AI or not.

To comply with the European Union Artificial Intelligence Act, tech companies will have to watermark content generated through their AI tools. Google is already watermarking text created through its products, and Anthropic will start doing so with similar techniques. OpenAI is expected to develop watermarking methods for its products as well.

Sarah Fisher, a lecturer in philosophy at Cardiff University in Wales, believes this is all a step in the right direction inside and beyond the classroom, though she acknowledges the new technology won’t be perfect.

“As a society of humans, we place value on what humans produce,” she says.

What Is AI Watermarking?

Historically, watermarking has involved some type of visible marker of authenticity.

“You watermark your banknotes, and you know that they're not counterfeit, or hopefully not,” Fisher says.

AI watermarking works somewhat differently, though the end goal is the same. Claude’s watermarking system will make the chatbot follow specific statistical rules for choosing words with the same meaning. Human readers won’t notice anything, but someone with the watermark key will be able to see if the string of words Claude uses is consistent with the watermark, Fisher explains.

“So, if I'm a student, and I go to my ChatGPT, or my Claude or whatever, and I say, 'Write me a hundred words about X, Y, or Zed.' I wouldn't necessarily see a watermark in what gets produced, but if I then submitted that to an online submission system, then presumably, at some stage, we'll have some software that would be able to tell that it was generated by ChatGPT or Claude or whatever.”

Why Could AI Watermarks Be Good News For Teachers?

Fisher believes being better able to tell what is generated by AI vs. humans will be helpful both for educators and society at large, for a few different reasons.

“For some categories of content, it's really obvious,” she says. “If we're talking about especially audiovisual stuff, these deepfakes are so insidious, we really want to know whether what we're seeing before our eyes is something that really did happen.”

Identifying AI text is also important, but in different ways.

“When we get a piece of text, it's not like looking at a deepfake," Fisher says. "We already have to assess whether what we're being told is true or false. And yet, I think it still can be really, really important to know whether that bit of text came from a human or a machine.”

Fisher adds, “One reason is informational or epistemic. These Chatbots tend to make some signature errors that humans don't make and vice versa. So it's just useful to know what to look out for.”

Another factor is just the value of protecting human work. “I've heard a lot of people saying things such as they just don't want to read a story that's written by an AI," says Fisher. "Even if it was the same words as a human produced, something's missing.”

Discerning human-generated work can be particularly important in the classroom setting.

“As educators, we want to know what our students are producing," Fisher says. "We don’t really care about the end products that much. We care about them going through the process. It's not like we're filling a demand for essays in the world.”

Will AI Watermarks Slow The Tide of AI In The Classroom and Beyond?

Fisher believes the answer to this question is yes, but only to a point.

“I think it might help a bit. I don't think it's going to be the silver bullet that solves this problem, and maybe there just never is a solution to this problem,” she says. “There's always a small minority of students who will find a way to cheat. And that's unfortunate, but we should never expect to get it down to zero. But I think the watermarking might help with some of the most egregious cases, where if a student literally has generated a whole long-form piece of text and taken it straight from Claude, say, it seems like in the future the watermark would be pretty good at telling the educator, ‘Look, this is extremely likely to have been generated by Claude,’ and it's very unlikely that we'd get false positives through that.”

What Are Some Limitations To AI Watermarking

It’s already certain AI watermarks won’t catch all AI-generated content.

“There might be rogue systems which aren't doing the watermarking,” Fisher says. “We know that the big companies are trying to do it, but these tools are proliferating, and so if a student has used one that doesn't have a watermarking feature, then that may not get caught.”

Additionally, existing watermarking techniques are less good at catching short passages of AI-generated text.

“Companies are being quite open about the fact that it might be possible to edit text in a way that takes out the watermarks,” Fisher says.

Though she adds, “I don't know how worried to be about that, actually, because once a student's gone to that much trouble, maybe they've done the work by then.”

Future of AI Watermarking

Some critics of AI watermarking worry that since it won’t be perfect, it will lead society to a false sense of security and could potentially make it more likely to fall for AI-generated content, Fisher says. Another criticism is that past efforts to label traditional misinformation on social media have had mixed results, and some think the same thing will be true about labeling AI.

Fisher doesn’t dismiss these concerns, but believes on the whole, AI watermarks will be a good thing.

“I'm reasonably optimistic that this is a positive step in the right direction,” she says. “It's helpful for us all to hold humans and AI apart for now while we figure out exactly what these things are and how we're going to integrate them into all of our social practices and our institutions.”

The Army Software Factory (ASWF) is a first-of-its-kind, Soldier-led tech initiative in which the U.S. Army is partnering with higher education to redefine workforce readiness and support the tech innovators of tomorrow. A humming hive of activity located on the campus of Austin Community College in the heart of Texas, the facility is part software development hub, part college career training and learning hub, and all Army.

“It’s amazing what you can do when your students are used to taking orders, said Dr. Garrett Groves, Vice Chancellor for Strategic Initiatives for Austin Community College, when speaking to attendees of Tech & Learning’s recent Texas Regional Leadership Summit.

Touting that it is “By Soldiers, for Soldiers,” “the program solves Army problems with Soldier-developed solutions.” said U.S. Army COL Jason Zuniga, Director of the Army Software Factory, U.S. Army Transformation and Training Command.

“Our No. 1 goal is to better equip Soldiers,” added Steven Ruh, Chief of Community Outreach & Engagements for the U.S. Army Transformation and Training Command. “How do we have the competitive advantage?”

To that end, in a unique partnership with Austin Community College, ASWF trains Soldiers and students in modern software and platform engineering, user design, and product management. The program offers credentialing, academic credit, and accelerated pathways to Bachelor's degrees. Thanks to its ongoing success, the institution and the Army recently signed a 10-year Inter-Governmental Support Agreement extension.

ASWF is also a true collaborative innovation center where developers from both the military and civilian worlds come together to create digital tools, platforms, and apps specifically designed for Army use. The key tasks are to rapidly build mission-specific software, conduct responsible cybersecurity, and train Soldiers in commercial best practices. Projects, which can range from improving tactical maneuvers to digitizing military archives for better planning, are sent to ASWF for consideration from any unit in the Army. Each is chosen in the belief that creating a solution will help “to dominate and win on battlefields.”

“In the Army, traditional software engineering roles and disciplines didn’t exist,” said Zuniga. “We had to be able to generate the pipeline to train Soldiers in these areas.”

Soldiers who are selected for a two-year assignment at ASWF choose either the platform engineer, software developer, product manager, or UI/UX designer track.

One project successfully developed at ASWF on display during the visit involved tracking flight data within a 10-kilometer radius to deconflict air space between drone aircraft and manned helicopters to ensure both can operate safely. Another tackled the issue of the high attrition rate of the Delayed Entry Program (DEP). When a future Soldier enters the DEP but doesn’t ship to Basic Combat Training on time, the Army sees an increase in recruiting costs and a reduction in training efficiency. For this, the ASWF gave a full makeover to its outdated DEP Soldier portal, which now includes more dynamic and engaging elements, including gamified activities.

While on site, Tech & Learning summit attendees got to see Soldiers and students working together on various Army-focused projects and hear real stories of how software developed at ASWF has been deployed in the field.

“How Might We Fail”

ASWF’s philosophy is "seeking aptitude over skills," including how teachable soft skills are integrated into a high-stakes environment. That includes learning how to fail, and during the visit, attendees got a hands-on peek into how that process is managed at ASWF.

Given the stakes for much of military operations, failure is typically not an option. However, at ASWF, the idea of productive failure has been embraced. Attendees were asked to brainstorm what they thought of when they hear the word “failure,” and the responses were then organized into groups to help explore and inform the innovation process.

Software design thrives on the process of finding mistakes and flaws during each iteration and then improving. At ASWF, it’s a three-step process:

1. Go fast and expect failure
2. Reframe failure as learning
3. Incentivize honesty and retrospection, not perfection

By better understanding this process and how failure in the design process can lead to a better end result, it can help the Army be more responsive, move quicker to solve problems, and find success.

Solving Soldier – And The World’s – Problems

In addition to hearing more about the ASWF’s history, mission, organization, and operations, attendees were also able to learn about the many career and educational opportunities available to students through Army enlistment, including the advantages of ROTC and JROTC programs. Army representatives from the Austin area explained how they have cultivated strong working relationships with the local school districts to better facilitate students who are interested in pursuing any of the options available.

Ultimately, ASWF embodies the Army’s longtime motto of “Be All You Can Be!” And then some.

“We teach our students and Soldiers to collaborate and work in teams,” said Zuniga. “We don’t say, ‘Go out and solve the world’s problems by yourself.’”

Ray-Ban Meta glasses are a type of wearable tech that can take photos, record, play music or podcasts, capture audio, and pair with AI so you can do a lot of things hands-free. The more expensive Ray-Ban Meta Display glasses also feature an on-lens display meant for tasks such as giving you text updates without blocking your view of the real world.

I recently tried the basic model (without the display) and found that these offer some compelling lesson planning and video creator capabilities for teachers. These also have potential for students and could aid in translation, accessibility, and more, but the price of the tech--the third generation starts around $450 per pair--is currently prohibitive in many education environments. Significant privacy issues are also a concern with the devices.

Here’s a closer look at the Ray-Ban Meta glasses and what educators should know.

What Are Ray-Ban Meta Glasses?

Ray-Ban Meta glasses have also been referred to as AI glasses or smart glasses. These offer a chance to put your phone and much of what it can do in POV mode, allowing you to capture high-resolution photos and videos that show what you see. These also function as a sleek headset with good calling and listening features. More advanced models also offer displays. Different styles of sunglass are available, such as aviators.

The Ray-Ban Meta glasses are a rare piece of VR or augmented reality AR tech that seems to be a success. Ray-Ban’s parent company said that more than 7 million pairs of the VR glasses were sold last year, more than tripling sales numbers from the year before.

What Are Some Ways Educators Can Use Ray-Ban Meta Glasses?

The main use I see for Ray-Ban Meta glasses right now are as an alternative to a wearable camera such as a GoPro. If you’re an educator who films a lot of lessons for students, maybe as part of a flipped classroom setup, these glasses could be a worthy investment. It’s really cool to just film what you’re seeing, and I could imagine using this for lab prep, or to illustrate a lesson. You could also give your students a more immersive quick video tour of a historic site.

Influencers have embraced these glasses for activities such as cooking demos, but for those of us who are not would-be celebrities, these might be better used to provide a helpful way to film practical demonstrations.

Accessibility Potential

In addition to making videos, Ray-Ban Meta glasses seem to offer a lot of potential for accessibility in education. A translation feature worked really well in my tests of Spanish-to-English, but it still provided a text-based translation. The person you’re speaking to’s voice is captured by the glasses, but the translation of what they say appears on your phone. This was convenient but not that big of an improvement over a phone-based translation app.

That said, it’s easy to imagine future generations of this technology adding more robust translation features: it would be nice to have the translated text appear on the glasses themselves, for instance.

Even right now, students could, in theory, use the glasses to photograph slides and info written on the whiteboard or chalkboard. For those with vision impairments, the screen display version of the glasses could allow them to see text up close. However, the technology is still too much in its infancy for these uses to feel really practical today.

The cost is also prohibitive, and most importantly, my concerns about the privacy features of these glasses prevent me from recommending these for student deployment.

Privacy Concerns

The privacy issues with Meta Ray-Ban glasses are significant both inside and outside of the classroom. The New York Times reports that restaurants have banned the glasses, and there are reported incidents of these being used to harass people at gyms.

Some users figured out how to disable the red light that turns on when the glasses are recording; in response, Meta announced that it had permanently disabled the recording abilities of thousands of device owners who had tampered with the glasses in this way. This has led the devices to earn the unflattering nickname of "pervert glasses."

None of this has stopped Meta from moving ahead with the device, though they now offer a record-only version of the glasses. Nor is it preventing other companies from releasing similar devices. Google has a new smart glasses tool in the works, and there are rumors Apple does as well.

In a school setting, however, all these privacy issues would be compounded. On top of inappropriate recordings, there are legitimate concerns about how the technology works with Meta’s AI tech and how student data and safety is addressed. Those are always worries with student tech, but a wearable device that sees and hears everything a student does makes these already vital concerns even more pressing.

Bottom Line: Ray-Ban Meta Glasses And Education

Right now Ray-Ban Meta Glasses are a neat, if somewhat gimmicky, device that would be good for educators looking to make POV educational videos.

Beyond that, these have a lot of potential but are not ready for primetime just yet from a tech capability standpoint. When you add in the significant and quite serious privacy concerns, these aren’t devices I see being handed out in a school anytime soon.

That said, the questions around privacy and smart glasses are ones society will need to deal with in the coming years and, as such, it's worth familiarizing yourself with the devices and discussing these with your students, even if Ray-Ban Meta glasses are something you’d personally never wear.

Every week, the headlines seem to bring a new iteration of alarm regarding generative artificial intelligence in education. From fears of widespread cheating and the death of student writing to warnings about cognitive atrophy, algorithmic surveillance, and the erosion of intellectual independence, the public dialogue is rife with anxiety.

When headlines sound like an impending apocalypse, educational leaders are left standing at the crossroads of community trepidation and educational reality.

Yet, for educational leaders navigating this disruption, the first and most critical leadership act is not reaching for digital blocking software. Rather, leaders should make sure they obtain a historical perspective and consider the history of concerns over technological change. By contextualizing the modern media panic within the history of drama and panic over technological change, leaders can help to guide their institutions though the rough and fluid waters as generative AI grows to maturity within the educational arena.

The Familiar Cycle of Innovation Panic

Every time a paradigm-shifting technology enters human culture, widespread resistance follows. Media narratives tend to frame emerging tools as existential threats to the intellect, but history tells a strikingly repetitive story.

Around 370 BCE, as recorded in Plato’s Phaedrus, Socrates lamented the invention of writing itself, warning that it would “create forgetfulness in the minds of those who learn to use it, because they will not exercise their memory; they will trust to the external written characters and not remember of themselves.”

While traditionalists worried that inscribed speech would hollow out human cognition, advocates such as Isocrates and Athenian democratic institutions recognized writing as an indispensable tool for composition, transparency, and civic progress. The modern concerns with cognitive atrophy seem oddly attuned to Socrates’s arguments against the alphabet.

Centuries later, similar patterns unfolded:

• In 1913, Thomas Edison proclaimed that printed books would be obsolete within ten years, completely replaced by motion pictures.
• By the 1920s and 1930s, cultural commentary warned that the radio would disturb adolescents’ excitable minds and turn education into “a matter of pressing the button.”
• In the mid-1970s, mathematics educators sounded the alarm that pocket calculators would erode basic arithmetic and ensure children would be helpless whenever batteries went dead.
• In the 1980s, historians and critics warned that microcomputers were vastly oversold and posed an unfounded distraction to traditional pedagogy.

Outside classroom walls, leaders routinely dismissed technologies that reshaped the globe, from Western Union calling the telephone an inferior toy in 1876, to hardware executives dismissing personal computing and the internet in the late 20th century.

When educational leaders view today's headlines about generative AI through this lens, panic softens in perspective. AI is not an unprecedented end to human intellect; it is the latest chapter in our long history of renegotiating the boundaries between cognitive labor and cognitive tools.

Moving From Reactive Bans to Guided Literacy

When generative tools such as large language models emerged, an instinctive reaction across many school districts was simple: block and ban. However, as panels at leadership forums such as during Tech & Learning’s Texas Regional Leadership Summit have made clear, a restrictive approach solves nothing.

Generally, blanket restrictions simply widen the digital divide and force students to experiment in unguided, shadow environments. Instead of isolating classrooms from emerging technologies, educational leaders must step forward to guide students toward ethical, real-world innovation. As Ethan Mollick notes in Co-Intelligence, homework cannot be preserved by pretending large language models do not exist; students must learn to treat AI as a tireless collaborator and thought partner while educators raise academic performance baselines.

Reframing Assessment: The Learning Process Over the Final Product

To address concerns about academic integrity and student agency, schools must fundamentally rethink what they measure as assessment of learning. Educational leaders must guide instruction away from task-completion models and the practice of grading outputs. The entire learning process will need to become more reflective and interactive. Students will need to be asked to consider how they are learning and how successful the use of generative AI tools are within the scope of each assignment.

When assignments measure only a polished five-paragraph essay or a completed worksheet, an AI model will always offer an enticing shortcut. Educators need to prioritize the learning and design process, feedback loops, and deeper conceptual understanding, and AI becomes a catalyst rather than a cheat code. As Salman Khan argues in Brave New Words, generative models can serve as personalized, one-on-one tutors and instructional assistants, addressing Bloom's classic two-sigma problem by supporting deliberate practice.

Simultaneously, district policies must heed the cautious recommendations of the U.S. Department of Education and international bodies such as UNESCO. Leaders must maintain a strict "human-in-the-loop" approach, ensuring that algorithmic models do not usurp cognitive agency or institutionalize evaluation biases. All processes need to enforce the central need for human agency and judgement within the instructional or creative process.

The Path Forward for Leadership

Headlines will continue to oscillate between techno-dystopian terror and commercial hype. The task for education leaders will be to remain grounded.

By understanding the historic rhythm of educational technology panics, leaders will be able to more effectively guide their institutions through this time of rapid change.

The Point Where I Used to Stop

For most of my life, I assumed coding belonged to other people. It was for developers, programmers, and those who understood what all those brackets and symbols meant. I had taken AP Computer Science in high school, but that experience only reinforced the idea that coding was not for me. I remember making it through “Hello, World!” before becoming lost somewhere between functions, variables, and whatever came next. Whether “Hello, World!” was an assignment, a function, or simply a warning sign, I am still not entirely sure.

After that, I stayed away from coding. I could appreciate what technology made possible, and I had dabbled with formulas and macros, but I still did not see myself as someone who could build anything with code.

What I have never been good at accepting, though, is an inefficient process. When I run into a repetitive administrative task that feels more complicated than it should be, I usually start looking for a better way to do it. I search the web, try different features, look for extensions or add-ons, and, more recently, ask AI for ideas.

Quite often, I eventually reach the same answer: the solution involves code. That is usually where I stop. I still cannot sit down with a blank screen and independently create a Google Apps Script, Google’s tool for using code to automate tasks across apps such as Sheets, Docs, Drive, and Gmail.

What I can do is identify a frustrating process, explain what I want to happen, follow clear directions, and keep asking questions when something does not work. As I learned during this experiment, that combination can be enough to get surprisingly far.

One day, I wondered why I was stopping at that point. If AI could help me find the solution, could it also help me write the code? That question became an experiment.

The Annoying Problem That Got Me to Try

My experiment grew out of a task that was not especially difficult. It was simply annoying.

Whenever I needed to share materials with a principal, colleague, or student, I typically would create a folder, name it, grant access, copy the link, send it, and then keep track of where everything was located. Doing that once was manageable. Doing it over and over was the kind of small administrative task that quietly eats away at your time.

Normally, I would have searched for a tool that could help. This time, I described the entire problem to Gemini instead. I explained that I wanted to start with a spreadsheet containing names and email addresses, create an individual folder for each person, assign the appropriate access, and have the folder link automatically written back into the spreadsheet for my reference.

Gemini generated the code, but the code by itself was not what made the experiment possible for me. It also showed me where to open Apps Script, where to paste the code, what I needed to change, and what to click.

When an error appeared, I copied the message into Gemini and asked what it meant. When the script did something I did not expect, I described what happened and asked it to fix the problem. I did not suddenly understand programming; I simply kept following the directions and asking the next question.

Eventually, the tool worked. I could identify a main folder, add names and email addresses to a spreadsheet, and run the script. It created an individual folder for each person, assigned access, and placed the link back into the spreadsheet. The same basic idea could also be adapted to create individual Google Docs, Sheets, or Slides from a template.

Of course, there are other ways to solve a problem such as this. Extensions, add-ons, and tools in the Google Workspace Marketplace may already do some or all of these things, and sometimes may even be the better solution.

What was different this time was that I was not trying to fit my workflow into someone else’s tool. I started with the workflow I wanted and built the tool around it. I could decide what information appeared in the spreadsheet, how folders were named, what the script did, and what information it returned to me.

For a developer, this may be an ordinary project. For me, it represented a significant shift. I had moved from thinking, “There must be a tool for this somewhere,” to realizing, “Maybe I can build the tool I actually want.”

Before using it with real recipients, however, I tested it with sample accounts, checked the folder locations, and verified the permissions. A mistake in an email address or sharing setting could give access to the wrong person, so the ability to automate something did not remove the need to check my work. I also asked a friend who teaches computer science to review both the tool and the code to make sure I was not missing anything important, and I made sure everything aligned with my organization’s policies before considering it ready to use.

Once I Built One Tool, I Started Seeing Other Possibilities

Getting the first script to work changed the way I looked at other repetitive tasks. Instead of immediately accepting a cumbersome process, I wondered whether I could build something to make it easier.

One example involved receiving a large master spreadsheet containing information that needed to be separated and distributed to different people. The manual process required filtering the file, copying the correct rows, creating separate documents, reviewing each one, and preparing the corresponding emails.

I asked Gemini whether a script could handle some of those steps. Together, we developed a tool that could separate the spreadsheet into recipient-specific files and prepare the corresponding emails. Rather than spending my time repeatedly filtering, copying, and creating files, I could spend more of it reviewing the results.

This example also showed me where the limits of my experimentation needed to be. An incorrect filter could place the wrong information in a file or send it to the wrong recipient. I would avoid using an AI-generated tool such as this with student-level data because the consequences of an error could be significant. I would limit it to information that could be safely distributed and that complied with my organization’s policies.

I also preferred to have the script create email drafts rather than automatically send anything. That gave me the opportunity to check the recipient, attachment, subject line, and message before anything left my account. Saving time was useful, but not at the expense of judgment.

Another experiment came from a similar frustration. I often work from spreadsheets containing names, roles, schools, and email addresses, so I wondered whether the spreadsheet itself could become a more useful communication tool.

With Gemini’s help, I created a script that allowed me to select people from the spreadsheet and decide how I wanted to contact them. Depending on the situation, I could place recipients in the To, CC, or BCC field or create an individual draft for each person. The subject line and message could also be pulled directly from the spreadsheet.

None of this is revolutionary technology, and that is actually part of the point. I was not trying to create the next great software platform. I was solving small problems in my own work and shaping each solution around how I actually worked.

The Skill Was Not Really Coding

Someone who knows how to code would already understand functions, debugging, onOpen(), and many of the other concepts that still feel unfamiliar to me, and they would almost certainly produce cleaner and more sophisticated code than I could. But that was not the expertise I brought to the experiment.

What I brought was a clear understanding of the problem, a solid grasp of the workflow, and a sense of what I wanted to happen. I could recognize when the result was wrong, describe the desired outcome, follow instructions, test what AI produced, and explain what needed to change.

Those abilities are not unique to programmers. Educators and school leaders use these constantly. We identify problems, explain processes, coordinate people, review information, and revise plans when something does not work.

Working with AI made me realize that those skills could also be part of building a technical solution and could help democratize solutions to everyday problems. I did not need to understand every line of code before I could experiment with it, but I did need enough understanding to test the tool, question the results, recognize the risks, and know when I was moving beyond what I should attempt on my own.

AI Did Not Make the Problems Disappear

Of course, the experience was not as simple as telling Gemini what I wanted and immediately receiving a perfect tool.

Sometimes a column heading was wrong. Sometimes the script could not find the folder. Sometimes it created duplicate files or stopped before finishing. At other times, I received an error message that meant almost nothing to me.

Previously, that would probably have been the end of the experiment. This time, I went back to Gemini, described what happened, shared the error message, and asked what needed to change. Sometimes the repair worked immediately, while other times it introduced a different problem that required another round of questions.

I was still debugging. I just had help doing it.

AI did not remove the frustration of getting something to work, but it changed what happened when I became stuck. Instead of reaching the limit of what I knew and abandoning the idea, I had somewhere to take the next question.

The Guardrails Still Matter

None of this means that someone should copy AI-generated code, approve every permission request, and hope everything works. Schools and districts may have policies governing Google Apps Script, external AI tools, automated emails, data access, and file sharing, and those policies need to come first.

Privacy matters during development as well. Gemini does not need actual student names, email addresses, identification numbers, grades, or confidential district information to help me write a script. I can describe the structure of a spreadsheet, provide column headings, and use invented names and sample data.

Testing matters just as much. Before using a script with real recipients, I can test it with sample accounts, review permissions, inspect drafts, and confirm that the correct files were created. When the stakes are higher, having someone with more technical expertise review the code can provide another important layer of assurance.

AI can help me debug the code, but it cannot take responsibility for what I choose to do with it. That responsibility remains mine.

Moving Past the Place Where I Used to Stop

What surprised me most during this one-day experiment was not that AI could write code. I already knew that. What surprised me was what happened when I stopped treating code as an automatic dead end.

For years, my question was simple: “Do I know how to code?” Since the answer was no, that usually ended the conversation. AI has given me a different question: “Can I clearly explain the problem I am trying to solve?”

That shift matters. AI does not make me a developer, nor does it replace the expertise of people who actually know how to code. What it does is give me a way to experiment with everyday problems that I once assumed were beyond my technical reach.

I am still not sure I have fully recovered from AP Computer Science. But now, when the answer to a problem involves code, I do not automatically stop there. I might just ask the next question.

Much of the conversation about students and AI has focused on cheating. Even esteemed tech reporter and columnist David Pogue recently framed student AI use through that lens in an online exchange we had. But what if we shifted the conversation from detecting AI use to thinking about the learning itself?

Instead of focusing on whether students used AI to complete an assignment, we can ask two more useful questions: Why are we giving students assignments AI can easily complete in the first place? And how might students use AI and/or other tools to support their thinking and learning?

Multiple-choice tests. End-of-chapter questions. Worksheets. Book reports. Generic essays. These assignments were problematic before generative AI. AI just magnified the problem and made it much harder to ignore.

That's why Michael Hernandez's work on Uncheatable Assessments caught my attention. Rather than focusing on catching students after the fact, Hernandez turns the lens back on us. What are we asking students to do, and is it actually worth doing?

Put Your Assignment to the Test

Hernandez created a tool that gives educators an Assignment Vulnerability Report. His Cheat Vulnerability Index scores assignments from 1 (vulnerable) to 10 (cheat-resistant).

The report looks at things such as originality, AI resistance, evidence requirements, personal or local connections, scaffolding, modality choice, authentic audience, reflection, and what we are actually assessing. Hernandez explains more about the thinking behind this approach in I Used AI to Build AI-Resistant Assignments.

I tried the tool with several assignments. A traditional worksheet earned just 1.3 out of 10. It lacked authentic purpose, evidence, meaningful choice, and personal connection. AI could complete it easily, but the real problem was the assignment.

Creative Doesn't Automatically Mean Uncheatable

A stronger assignment that gave students multimodal choices and asked them to explore human creativity and AI jumped to 5.9. It scored well for AI resistance, originality, and modality choice, but poorly for personal connections, authentic audience, and reflection.

That's important. Swapping a worksheet for a podcast, video, or presentation isn't enough. Students need something meaningful to do and a reason to do it.

This is something I've written about for years. In 5 Innovative School Models You Might Not Know, I looked at schools where students pursue interests, work on projects, learn in their communities, and demonstrate learning through real work rather than just tests and assignments created for the teacher.

What Might an Uncheatable Assignment Look Like?

Let's say students are studying pivotal moments in recent history. We could assign questions, a test, or an essay about how an event affected Americans. AI can do all of that pretty well.

Or we could ask students to interview an adult about pivotal historical moments they lived through. Now the student has something AI doesn't have: that conversation. They have to listen, ask follow-up questions, connect history to lived experience, and decide what matters.

Then give students agency over what they create: a podcast, picture book, video, biography, artwork, song, or another format. Give them examples and a rubric so they know what strong work looks like without requiring everyone to show learning the same way.

The audience matters, too. The person whose story the student is telling is an obvious place to start for finding an authentic audience. Students could also share their work with classmates, families, or the larger community. As I've written before in 5 Steps to Help Students Connect With Real Audiences, audience shouldn't be something we figure out after the work is finished. It should be part of why students are doing the work in the first place.

Reflection can be simple: What surprised you? What are you most proud of? What do you want others to understand? How did feedback affect what you created? Now we're seeing the thinking behind the product, not just grading what landed in the teacher's inbox.

I put this assignment into Hernandez's tool too. It scored 7.8 out of 10, including a 10 for local/personal anchoring, 9 for originality and authentic audience, and 8 for AI resistance and evidence. Because students gather primary source material themselves, the feedback called it "genuinely hard to fake or automate."

It still wasn't a 10. The tool flagged clearer expectations, stronger reflection, and more flexibility in how students present their work. I had already envisioned multiple formats, but making those choices explicit would make the assignment stronger.

Uncheatable Doesn't Mean AI-Free

Here's another thing I like about Hernandez's approach: uncheatable doesn't mean AI-free. He used AI to help build a tool for creating AI-resistant assignments.

Students could use AI to brainstorm questions, organize ideas, consider formats, or get feedback. AI can help students think more deeply without replacing their thinking. The goal isn't to prevent AI use. It's to create learning students can't outsource to AI.

AI happens to be very good at the recall, regurgitation, and generic production schools have assigned for generations. Those problems existed long before AI, which is part of the argument I've made about why we need to stop romanticizing school before technology.

AI didn't create the problem; it exposed it. We can spend our energy trying to catch students and preserve outdated assignments, or we can create work students actually have a reason to do. Then educators can stop trying to detect AI and start creating better assignments.

When schools make decisions about artificial intelligence, the conversation is usually led by adults. Administrators develop policies, educators establish classroom expectations, and technology leaders evaluate all of the platforms. These perspectives are essential, but I believe there is another important voice that is often included only after the decisions have been made, and that is the students who are already using AI.

Over the past year, I’ve had the opportunity to work with educators, leaders, students, and families across 12 school districts as they explored digital wellness, responsible technology use, and emerging questions about AI. Through student conversations, classroom visits, and task force planning sessions, I was reminded that students do not simply need to hear our guidance–their voice can help us create better guidance.

Students in third, fifth, and eighth grades, and in high school, offer insights that might otherwise be missed. They see AI used for homework, projects, online searches, and social interactions. They understand the pressure to finish work quickly and the temptation to seek help when an assignment feels confusing or overwhelming.

That does not mean students understand every risk. Knowing how to access an AI tool is not the same as knowing what happens to the information entered into it or why its responses may be inaccurate or biased. But listening to students can reveal where adult assumptions differ from their reality.

Several themes stood out in our conversations:

• Students want transparency. They want teachers and schools to disclose when and how AI is being used, especially when it involves grading assignments, evaluating their work, or generating feedback.
• Students want to understand how AI works. There is often an assumption that because students are comfortable with technology, they automatically understand AI. Knowing how to access a tool is not the same as understanding how it produces information, uses data, makes mistakes, or may reflect bias.
• Students want guidance. They want educators to model how AI can be used safely, ethically, and responsibly. They need opportunities to learn how to protect personal information, evaluate AI-generated content, and decide when AI should or shouldn’t be used.
• Students want clear expectations. Students want to know what is allowed for each assignment, which uses must be disclosed, what work must be completed independently, and where the boundary between assistance and cheating lies.
• Students want consistency and equitable opportunities. Confusion and frustration arise when one teacher permits AI use and another prohibits it without explaining why. Expectations do not have to be identical across every subject. Students should not have to guess what the rules are from class to class. If students in one biology class are permitted to use AI in meaningful ways, students in the same course with another teacher should have comparable learning opportunities, or it places them at a disadvantage.
• Students want meaningful learning experiences. They do not want to use AI simply because it is available or new. Several students shared with me that when they first started using Chromebooks, it was the best thing ever, but with repeated use without purpose, it made them want technology-free activities. AI should add value, and its use should be connected to a clear learning purpose.
• Students want protection from unfair accusations. Unclear rules can leave students worried that their own work will be questioned. Students need opportunities to show their thinking and demonstrate their learning.

Students can also help us understand the difference between the AI use we see and the AI use we do not. Educators might notice AI-generated language in an essay, but students are using these tools for more than schoolwork. Some use AI to seek advice, generate images, rehearse difficult conversations, or ask questions they are afraid or uncomfortable asking another person. Taking time to listen to students will help schools think beyond academic integrity and consider data privacy, well-being, relationships, and digital citizenship.

When thinking about creating policies, students can have a positive impact when they are involved in the conversation, be it through surveys, focus groups, technology advisory groups, or opportunities to give feedback on proposed guidance. Educators can also incorporate regular reflection into classroom activities and ask students how they used AI, what it helped them do, what was not possible with AI, and whether they had any concerns while using it.

Conversations such as these will provide educators with valuable information while also helping students to develop their AI literacy skills. Involving students shows that their perspectives are valued. Getting their input will help us understand how they are being affected by AI. When they have a comfortable space to share what they are seeing and experiencing and explain what they need, it leads to a stronger culture for learning about and working with AI.

If we want to prepare students to use AI thoughtfully, we need to ask what they are experiencing, listen to their answers, and let what we learn inform the decisions we make.

Common Sense Media’s new online, self-paced course examines AI use and its connection to student wellbeing.

While most discussions around AI in the classroom focus on topics such as AI tutors or AI cheating, an arguably more pressing concern is how these tools are impacting student wellbeing. More and more young people are turning to these tools for the type of mental health advice traditionally offered by a friend or therapist.

Common Sense Media’s free, hour-long class focuses on these types of AI use. I recently took the course and it was easy to follow, helpful, and a very productive use of an hour. The course lays out how students are increasingly turning to AI chatbots for emotional support, and explains why that can be harmful to their wellbeing. Then the AI and Student Well-Being course offers specific and actionable advice for educators to help their students overcome these challenges.

While some of the discussion in the course was familiar to me, other aspects of it were new. The whole course is a great reminder that I, and other educators, should be taking this issue seriously. Here’s what I learned.

1. AI and Student Well-Being: Students Are Using AI -- A Lot

In the course, Common Sense Media shares results from a survey it conducted in which more than a thousand 9- to 17-year-olds across the country were asked to discuss their AI use. It’s no surprise that the survey revealed that students are using AI a lot, but even so, the numbers are eye-opening.

“Nearly 9 in 10 kids age 9 to 17 use or interact with AI, including about one in four who do so on a daily basis,” the course reveals. Additionally, these regular AI users exhibit signs of AI dependence. “Twenty percent of kids who use AI, and 42% of kids who use AI daily, say it would be very or somewhat hard for them to go without AI for a month.”

Concerningly, students who said they were lonelier were also more likely to use AI more often and to turn to it for emotional support. Only 33% of kids who rarely use AI said they felt lonely some of the time. Meanwhile, “about half of kids who use or interact with AI daily (54%) or weekly (47%), and 44% of monthly AI users, say they feel lonely at least some of the time.”

2. AI Can Appear Helpful for Emotional Support

The course is not actually anti-AI and makes it clear that AI can appear (and might even be) helpful for emotional support in some instances. For certain students, AI could reduce feelings of loneliness in the moment, offer a low-pressure space to ask questions, and provide guidance and practice for navigating social situations. It could also spark creativity and self-expression.

But there are limits to these benefits. “[AI] does not actually know a student, care about their future, or share in the relationship,” the course text warns. “Its responses are generated, not felt.” This could lead to AI stopping students from seeking help from humans and its constantly affirming responses can reinforce “validation-seeking patterns.”

Additionally, “for vulnerable students, AI may intensify rather than ease existing struggles,” and, “One-sided interactions can start to feel safer or more satisfying than real relationships.”

3. Chatbots Appeal to Students Because They Offer “Frictionless Friendships”

One of the reasons AI chatbots appeal to students is that these platforms offer a frictionless friendship. “When a chatbot uses simulated curiosity to ask a student about their day, it signals interest,” the text states. “When it uses social presence to reference something a student shared the week before, it signals memory and care. When it uses sycophancy to affirm a student's feelings without question, it signals safety. These may not be real in the way a human relationship is real—but they can feel real.”

Additionally, talking with a chatbot doesn’t carry a social risk, and these tools are always available and never require anything from a student. In contrast, a real friend might challenge you and disagree with you, isn’t always available and has their own interests and passions. But they do care “about your wellbeing in a real, embodied way.”

4. There Are Signs of Concerning AI Use Educators Can Watch For

The course offers several tangible solutions for addressing concerns about AI use and teaches you some potential signs that a student may have entered into an unhealthy relationship with a chatbot. These include discussing AI as a “real friend,” and sudden switches in academic performance and overall behavior.

In these instances, the course advises not trying to perform the role of a therapist, but instead talking with the student about their AI use in a curious and non-judgmental way, and following your institution’s guidelines around recommending wellbeing and mental health supports. Understanding what students are getting out of their interactions with these machines can also help.

Many students are drawn to chatbots because of their sycophancy (a tendency to agree with the user) and students also have “automation bias,” a strange new tendency many have to trust AI more than their own intuitions because it seems so knowledgeable and confident in its responses.

5. There Are Steps Educators Can Take

When it comes to AI and student wellbeing, as with so many other things, the best interventions are often the ones that take place before a problem develops.

To this end, the course recommends that educators take an active role in preventing students from entering into potentially harmful scenarios with AI. This can be done through digital literacy and by making AI a regular low-stakes topic of conversation in class. Additionally, the course encourages educators to model intentional AI use, explaining why they are using AI and its limitations.

In a great piece of advice for this and other situations as an educator, the course advises teachers not to be afraid to let the students know the limitations of their own knowledge on these topics by saying something such as, “I'm still figuring this out, too.” The course notes that this “is an invitation to think together,” modeling exactly the kind of real human collaboration and thoughtfulness AI can’t duplicate.

Artificial intelligence is quickly moving from novelty to infrastructure. In many schools and universities, AI is already shaping writing, assessment, tutoring, research, student support, and administrative decision-making. UNESCO’s 2026 report, The Algorithm in the Room: Seeing and Confronting the Implications of AI for the Future of Education, argues that educational leaders should resist treating this transformation as inevitable.

The more important leadership question is not simply, “How do we adopt AI?” but “What purposes of education do we want AI to support, and what should remain distinctly human?” In some cases, the broader conversation might also include questions such as, “What is the purpose of our school, program, or course?”

For school and district leaders, higher ed administrators, and instructional technology leaders, the report offers several immediate action items.

1. Start with educational purpose, not the tool

One of the primary principles of effective educational technology has always been to focus on the instructional goal rather than the technology. The UNESCO report repeatedly returns to the problem of alignment. AI systems are designed to optimize toward goals, but schools are complicated, and not all educational goals can be easily quantified in a dashboard or reduced to a simple metric.

The immediate leadership implication is straightforward: before approving a new AI product, ask what educational problem it is intended to solve. Leaders should require a clear instructional or administrative rationale rather than adopting tools simply because of a promise of efficiency or personalization. A useful leadership question is, “What do we want students or educators to be better able to do because this tool exists?”

2. Audit where algorithms are already making decisions

UNESCO’s first recommended horizon is to “see the algorithm.” Educational leaders may be surprised by how many algorithmic systems are already embedded in their institutions, from adaptive learning tools and plagiarism detection systems to enrollment platforms, analytics dashboards, recommendation engines, and automated assessment tools.

An immediate action is to conduct an institutional AI and algorithm inventory. Identify which systems are being used, what data these collect, what decisions they influence, and whether those decisions can be challenged by a human.

This is especially important because UNESCO warns that algorithmic decisions can be opaque, difficult to appeal, and capable of reinforcing existing inequities.

Algorithms need to be tested from time to time. Do not assume that the conditions that allowed an algorithm to be effective a decade ago are still in place.

3. Protect human agency in teaching and learning

One of the report’s strongest warnings concerns the gradual transfer of intellectual work from people to machines. UNESCO describes the possibility of a “classroom AI doom loop,” in which AI generates assignments, students use AI to complete the work, AI evaluates the responses, and automated systems record the grades. The issue goes beyond academic dishonesty and into whether meaningful human learning is taking place.

Leaders should therefore ask instructors to identify the parts of learning that must remain human. These might include forming questions, interpreting evidence, explaining reasoning, engaging in discussion, solving unfamiliar problems, reflecting on mistakes, and defending conclusions. Assessment seems like a key process that needs to remain under human agency. AI can support those processes, but it should not replace any.

4. Rethink assessment instead of relying on AI detection

UNESCO’s discussion of pedagogy and assessment suggests that educational institutions need to move beyond the search for increasingly sophisticated AI detectors.

If AI can produce conventional academic products, leaders should help educators reconsider what those products are intended to demonstrate. Assessment should increasingly make student thinking visible. That could mean more oral explanation, iterative drafts, demonstrations, presentations, authentic projects, reflective commentary, in-class problem solving, and assignments in which students document how they reached a conclusion.

The leadership challenge is to support redesign of assessment to disclose, document, and reflect on AI use by students.

5. Make AI literacy broader than prompt engineering

Many current AI literacy initiatives focus heavily on how to use generative AI effectively. UNESCO suggests that students need something more substantial.

AI literacy should include understanding how algorithmic systems work, how training data influence outputs, how bias can emerge, why hallucinations occur, whose knowledge may be overrepresented or absent, and how commercial incentives shape technology. Students should learn to question AI outputs, not simply generate better ones.

The UNESCO report raises concerns about linguistic and cultural flattening. Dominant languages and highly digitized knowledge traditions may receive disproportionate representation in large language models, while local, indigenous, and less digitized perspectives risk being further marginalized in some cases.

6. Establish clear human-rights and privacy guardrails

UNESCO emphasizes that AI use in education should be grounded in dignity, non-discrimination, participation, equity, informed consent, and protection—especially when children or vulnerable learners are involved.

Educational leaders should immediately review policies governing student data, AI account creation, vendor access, automated decision-making, and the use of personally identifiable information in generative AI platforms.

Institutions also need a clearly articulated guiding principle that consequential decisions affecting students should not be delegated entirely to automated systems.

7. Strengthen teachers rather than designing around them

One of the most important leadership messages in the report is that AI should not become an excuse to hollow out educational institutions. UNESCO argues that hopeful AI futures depend on strengthening libraries, arts and humanities programs, teacher development, and public educational infrastructures.

To achieve this, professional learning should move beyond one-time workshops on individual AI products. Educators need ongoing opportunities to examine AI’s instructional, ethical, cultural, and assessment implications. Teachers should be positioned as designers of learning environments, not simply supervisors of automated systems.

8. Move from consumers to shapers of AI

UNESCO’s final horizon is the most ambitious: “rewrite the algorithm.” The report encourages educational communities to participate actively in shaping AI rather than simply consuming commercially produced systems. It highlights possibilities such as community-trained models, citizen science, indigenous-language projects, locally developed small language models, and collaborative rather than purely individualized AI tools.

For educational leaders, the immediate step may be modest: involve teachers, students, librarians, technology staff, and community members in AI governance and purchasing decisions. Those who experience the consequences of educational technology should have a voice in determining how it is used.

A leadership agenda for right now

Perhaps the most useful takeaway from The Algorithm in the Room is that educational leaders still have agency. AI is not an external force that schools must simply accommodate.

UNESCO’s three-part framework provides a practical leadership sequence: see the algorithm, steer the algorithm, and ultimately help rewrite the algorithm.

For leaders, that means conducting an AI inventory, establishing governance and privacy expectations, supporting assessment redesign, expanding AI literacy, protecting meaningful human interaction, and ensuring that technology remains subordinate to educational purpose.

The most important AI strategy may therefore begin with a question that has very little to do with technology: What kind of education are we trying to provide, strengthen, and create? Once leaders can answer that clearly, decisions about AI become considerably easier.

Of course, what kind of education we should be providing might be the most difficult question of all.

Quantum computing could mark the next revolution in tech. Giants in the industry such as Google and IBM are making big investments in these next-gen computers, and industry is not alone. In June, the U.S. Department of Energy announced its Quantum Genesis Initiative, which aims to develop and deploy fully “scientifically relevant quantum computing capability for research and development by 2028." In July the U.S. National Science Foundation (NSF) announced Project Triad, an initiative that seeks to unify “three domains of quantum technology — sensors, computers and networks — in a unified, functional system.”

Instead of the bits used by classical computers, quantum computers utilize qubits. While a classical bit can be represented as either a 1 or 0, qubits take advantage of the strange properties of the quantum world to be both 1 and 0 at the same time. This, paired with other quantum characteristics such as entanglement, allows quantum computers to achieve levels of computational power that aren’t possible with classical computers.

As exciting as quantum computing is, there’s a problem.

“The field of quantum technology is growing faster than the current workforce can support,” says NSF Spokesperson Miriam Kleiman. She adds, “There is a significant shortage of qualified people.”

In an email, Kleiman shared steps educators can take to help their students learn about emerging quantum industries and how to potentially play a role.

Taking Steps To Prepare Students for Quantum-Related Careers

In addition to teaching core ideas from quantum mechanics, computer science, and mathematics, Kleiman says NSF scientists advise teachers to bolster these lessons by adding “simple hands-on activities, like writing basic programs for quantum devices, and exposing students to real tools and demonstrations.”

She adds, “Partnering with industry for guest talks or internships and using local training opportunities strengthens the connection to careers.”

Existing curricula that educators can utilize are available from NSF-supported centers. These include:

• Robust Quantum Simulation’s K-12 materials
• HQAN (TeachQuantum and Wonders of Quantum Physics)
• National programs such as the A Collaboration To Support Quantum Education, Chicago Quantum Exchange and QuSTEAM.

Tech & Learning has published an overview of some quantum computing resources that explores some of the mind-bending science behind it all.

Things All Students Should Know Regardless of Career Path

Whether or not a student is interested in pursuing a career in quantum computers, there are still aspects of this technology of which they should be aware.

“Every student should understand the basics: what a qubit is and how it differs from a traditional bit, plus the key concepts of superposition, entanglement, and decoherence,” Kleiman says. “With this foundation, students can see how quantum computers differ from classical machines, grasp where the technology is useful, and think critically about its social and ethical impacts.”

Those impacts are potentially profound. “Quantum technology could help solve problems that overwhelm today’s computers,” Kleiman says. “It may enable accurate simulations of complex molecules and materials—supporting drug discovery, better battery chemistry, and cleaner fertilizer production.”

Kleiman adds it could increase efficiency in global shipping and various supply chains as well as areas such as city transit. Additionally, it will have a significant impact on the fields of data security and cryptography.

Despite misconceptions to the contrary, quantum computers are unlikely to replace classical computers. Ultimately though, these devices will used for specific problems and are expected to have a huge impact on society.

“For certain math-heavy problems, quantum approaches promise much greater efficiency and better results,” Kleiman says.

Generative AI has created a new challenge for teachers. Students can ask tools such as ChatGPT, Claude, and Gemini almost any question and receive a polished answer in seconds. The problem is that polished does not always mean accurate.

Large language models (LLMs) can produce incorrect information, misrepresent sources, and even invent convincing citations to publications that do not exist, which is why many educators remain understandably cautious about students using AI for research.

The better response is not to tell students to avoid AI altogether but to teach them how to use AI tools that keep evidence at the center of the research process.

What Is A RAG?

A retrieval-augmented generation tool, or RAG, helps students move from asking AI for answers to asking AI to help them find, examine, and explain evidence.

One way to explain the difference between a traditional LLM and RAG is to compare it to the difference between a closed-book and open-book exam. When students use an LLM by themselves, the model answers from what it learned during training and from whatever the student provides in the conversation. It may produce a useful response, but it may also fill gaps with something that sounds right rather than something that is right.

RAG changes that interaction. Before answering, the system retrieves information related to the question and gives that material to the model. In that sense, RAG is less like asking AI to guess and more like asking AI to work with evidence in front of it.

The process is essentially: Question → Retrieve sources → Analyze sources → Generate response

This distinction is important for students because it introduces something often missing from conventional AI interactions: an evidence trail.

RAG does not eliminate hallucinations, and students still need to verify AI-generated information. However, properly designed retrieval systems make verification much easier because students can trace claims back to underlying sources.

Sometimes the term agentic RAG is used; most current RAG tools are better described as RAG with agentic features, not fully agentic RAGs at this point. These retrieve evidence, but may not independently plan, evaluate, and revise the research process in the way a true agentic system would.

With that foundation in mind, here are five practical ways educators can begin using retrieval-based AI tools to help students ask better questions, locate stronger evidence, and verify what AI tells them.

1. Use Gemini Notebook to Create a Controlled Research Environment

Google's Gemini Notebook provides perhaps the easiest introduction to RAG for students. Rather than relying primarily on the model's general knowledge, teachers and students can provide the sources Gemini Notebook will use. These can include PDFs, websites, Google Docs and Slides, YouTube videos, and other materials. Responses are grounded in those sources and include citations students can follow back to the original material.

That allows teachers to create a controlled AI research environment. For example, instead of asking students to use AI to investigate the causes of the American Revolution from the open web, a teacher could provide six carefully selected primary and secondary sources. Students could load those materials into Gemini Notebook and ask: "What were the most important colonial objections to British taxation?"

The assignment also shouldn't end with the answer. Require students to identify the sources Gemini Notebook used, locate the supporting passages, and determine whether those passages support its conclusions.

Suddenly, AI isn't replacing reading but giving students another reason to read critically.

2. Use Consensus to Move Students Toward Scholarly Evidence

For older students, particularly those beginning serious research projects, Consensus can provide a useful bridge from general web searching to scholarly literature. Instead of asking a general-purpose chatbot to recall studies from its training, students can enter natural-language research questions and receive results grounded in academic research. Consider the difference between asking: "Does homework improve student achievement?" and asking: "What peer-reviewed research suggests that homework improves student achievement?"

The second question establishes an evidentiary expectation. Students can then examine the research Consensus retrieves, determine which studies address their question, and follow those studies to the original publications.

The objective is to help them identify research worth investigating rather than have Consensus do it for them.

3. Use Elicit for More Sophisticated Research Projects

Elicit takes this process another step and is particularly useful for advanced secondary and postsecondary research. Students can use it to locate scholarly literature, compare studies, extract information, and begin organizing evidence for literature reviews. This provides an opportunity to teach something students frequently struggle with finding a source is not the same as evaluating a source.

A student might use Elicit to identify ten studies related to a research question. The next task should be determining which studies belong in the research project. Students can examine methodology, sample size, population, publication date, limitations, and relevance to the research question.

In this example, AI accelerates discovery but students remain responsible for judgment. That distinction should become central to AI literacy.

4. Use Scite to Ask, "Does Research Really Support This?"

Students often assume that finding a citation proves a claim. Scite provides a useful way to challenge that assumption. Its Smart Citations show how later scholarly publications have cited earlier work and classify citation statements as supporting, contrasting with, or simply mentioning the original study.

Imagine a student finding a 2018 study that makes an important claim. Rather than simply citing it, the student can ask better questions: What happened to this finding afterward? Have later studies supported it? Challenged it? Qualified it? Has the scholarly conversation moved in another direction?

This approach introduces students to an important feature of authentic scholarship: knowledge isn't simply a collection of isolated facts but an ongoing conversation in which evidence is continually examined and reconsidered.

5. Use Perplexity for Current Information, but Verify the Sources

Not every student research question belongs in an academic database. Students researching current legislation, technology developments, elections, economic conditions, or breaking events need access to information that changes quickly.

Retrieval-oriented tools such as Perplexity can be useful because it searches current web information and connects generated answers to sources. However, this presents another excellent teaching opportunity. A citation does not automatically make information credible. Students should examine who published the information, when it was published, what evidence the source provides, whether other credible sources corroborate it, and whether the AI accurately represented what the source said.

The presence of citations should therefore be the beginning of verification, not the end of it.

Establish One Simple Classroom Rule

Teachers introducing RAG do not need an elaborate AI policy to get started. Begin with one simple rule: Never cite the AI tool, but cite the evidence behind the AI.

When an AI tool provides a source, students should open it. When it makes a factual claim, students should find supporting evidence. When it summarizes research, students should compare the summary with the original. And when the evidence is not strong enough, students should keep researching.

Establishing these habits turns AI use from passive answer collection into active information evaluation.

Create an AI Research Toolbox

Students also need to understand that there isn't one best AI tool. Most of these tools have clear strengths and weaknesses, so they will have to learn to use the one best for each type of question or problem.

• Assigned readings and teacher-selected sources: Gemini Notebook
• Finding scholarly evidence: Consensus
• Conducting deeper literature reviews: Elicit
• Determining whether research supports or challenges a claim: Scite
• Investigating current information: Perplexity
• Brainstorming, tutoring, explaining, organizing, and feedback: General-purpose LLMs such as ChatGPT, Claude, or Gemini

This shift represents an important evolution in instructor and student AI literacy. Instead of teaching students simply how to prompt an AI system, we need to teach them which information environment they should ask the AI to search.

Getting Started Tomorrow

Teachers can introduce this approach without redesigning an entire course. For example, choose three to five credible sources related to something students are already studying and put them into Gemini Notebook. Give students a research question and ask them to generate an initial response. Then add the requirement that students verify three claims in the AI response against the original sources. For each claim, students should identify the source, locate the supporting evidence, and decide whether the AI represented that evidence accurately. A follow-up discussion can focus on what the AI got right, what it missed, and where its interpretation differed from students' interpretations.

That's a relatively small instructional change, but it introduces several important competencies simultaneously: AI literacy, source evaluation, corroboration, close reading, and evidence-based reasoning.

Move From Prompt Literacy to Evidence Literacy

The first phase of generative AI in education focused heavily on prompts. Students and teachers learned that better questions could produce better AI responses.

The next phase needs to go further. Students need to understand where AI-generated information comes from, why some sources deserve more credibility than others, how retrieval influences an AI's response, and how to determine whether the evidence supports what the AI says.

Ultimately, the most important question we ask students may no longer be, "Did you use AI?" A much better question is, "What evidence did the AI find, and how did you determine that evidence was trustworthy?" This then becomes information literacy for a world in which students will increasingly encounter information through AI systems.

Throughout my work with educators and district leadership teams across the country, I have seen growing interest in and a need for AI professional learning. Educators want to understand what AI is, how it works, and how it can support their work, but they also want practical guidance about data privacy, academic integrity, bias, responsible classroom use, and appropriate student use. They need clear and consistent guidance that supports their practice.

A single workshop or training will not adequately prepare educators for everything they need to know and do. I have been passionate about this work for the past four years since starting my doctoral research, which focused on the need for effective AI professional development for educators.

A key takeaway from my research was the importance of ongoing, relevant, and supportive professional learning. My work with schools and educators has continued to reinforce that finding.

Effective AI professional learning cannot simply be about showing teachers the latest tools. Tools change, policies evolve, and new concerns emerge. If PD focuses primarily on a platform, what educators learn may become outdated before they have an opportunity to apply it in their practice. We need to build knowledge, confidence, and skills that educators can transfer regardless of what AI tools are available.

Start With Purpose, Not With Tools

For years, when I provided PD, I often focused on the tools. Over time, I recognized that technologies change, and what matters most is understanding the why behind using technology. The same is true with AI. Begin by asking why an educator might use AI rather than asking which AI tool they will use.

When I work with educators, I challenge them to identify specific needs in their own practice. Do they want to differentiate instruction? Create more accessible materials? Provide feedback? Redesign an assessment? Generate ideas? Reduce time spent on repetitive tasks? Starting with an instructional need keeps the focus on teaching and learning rather than technology for technology’s sake.

Providing guiding questions helps educators decide when AI is useful and when it is not. Effective professional learning should guide teachers to ask:

• What is the learning goal?
• What thinking should students do independently?
• Where might AI provide meaningful support?
• What would be gained or lost by using AI?
• What happens if AI completes too much of the work?

These questions will remain relevant even as the technology changes.

Connect Learning to Real Classroom Practice

Just as relevance makes learning more meaningful for students, professional learning needs to connect to educators’ actual roles and responsibilities. A kindergarten teacher, high school science teacher, school counselor, special education teacher, instructional coach, and district administrator may ultimately use the same AI platform, but they will not necessarily use it in the same way.

Professional learning should include realistic scenarios for grade levels and content areas, and provide an opportunity for participants to create something they can use right away. Teachers might revise an assignment, develop expectations for student AI use, evaluate an AI-generated response, design a differentiated resource, or create a lesson that requires students to verify AI output.

In my sessions, educators have found it valuable to work through scenarios with colleagues rather than just learn what AI can do. Some scenarios I use include a student using an unapproved chatbot, evaluating a biased AI-generated response, considering the consequences of exposed student information, or addressing suspected AI-assisted student work.

Discussions enable educators to collaborate and think about what they would do, consider the benefits and cautions of AI, and learn together. These conversations help them leave with a clearer understanding of AI and specific next steps for their own learning and practice.

Differentiate the Professional Learning

We differentiate learning for students, so we should think similarly about professional learning for educators. Everyone needs a solid foundation in AI literacy, but not everyone needs the exact same training.

Effective professional learning should provide opportunities for educators to explore how AI connects specifically to their work. Classroom teachers may need more support with instructional design, assessment, AI literacy, and appropriate student use. Administrators may focus more on leadership, policy, implementation, and communication. Technology teams may need more time on data privacy, security, and vendor vetting.

Professional learning should also account for differences in experience and comfort with AI. Two teachers in the same department may have very different starting points. One may already be experimenting with AI while another is still learning the basics. Providing opportunities for choice and time to learn with colleagues meets educators where they are without overwhelming them.

Address Privacy, Law, and Ethics

Responsible AI use cannot be an add-on at the end of professional learning. Privacy, safety, ethics, accessibility, and legal considerations need to be woven throughout the experience.

As an educator and attorney, I frequently work with schools that have questions about student data, approved tools, vendor agreements, copyright, accessibility, and academic integrity. Many educators have expressed not having enough guidance about what information can be entered into an AI platform or how to determine whether a tool is appropriate for students.

Educators need practical guidance rather than a list of laws without classroom context. Questions to consider:

• Why does entering identifiable student information into an unapproved platform create risk?
• What do age requirements mean for student access?
• What should educators look for in privacy policies?
• Why must AI-generated content be reviewed for bias and accuracy?

Schools need consistent expectations. Professional learning should connect directly to district policies and show what those policies mean in practice.

Make Professional Learning Active and Ongoing

In schools, a keynote or introductory workshop helps to build awareness and momentum, but it should serve as the beginning of the learning process rather than the entire strategy. Ongoing support might include workshops, content- or grade-level coaching, professional learning communities (PLCs), office hours, model lessons, and ongoing conversations.

The goal is not to become an AI expert. Educators need enough understanding and confidence to make informed decisions, recognize when AI adds value and when it does not, and guide students to do the same. When this is possible, we move from simple awareness of AI and its possibilities to intentional implementation.

Include Students and Families

AI professional learning should help educators understand how students are using these technologies. Students may be employing AI to brainstorm, translate, summarize, study, create images, or complete assignments. Some students may understand the limitations of these tools, while others may trust the output without questioning it.

Ask students how they are using AI. What do they think is permitted? What do they consider cheating? When do they believe AI helps them learn? When might they be relying on it too much? What do they know about the information they are sharing with AI tools? Student voice can reveal gaps between school policies and actual classroom practices. It can also help schools develop expectations that are realistic, understandable, and responsive to student needs.

Families should be part of the conversation as well. Parents and caregivers may have questions about why AI is being used, what tools students can access, how their children's information is protected, how schools define appropriate use, or how AI changes the expectations for student work. Professional learning should prepare educators to answer these questions.

Build Systems That Can Adapt

Effective AI professional learning should be part of a larger system that involves leadership, policy, curriculum, technology, families, and students. District leaders must communicate a clear vision, provide guidance about approved tools, and reflect on decisions as the technology changes. Schools should also evaluate whether professional learning is making a difference in educators’ confidence, classroom decisions, assessment practices, and understanding of privacy expectations.

The measure of successful AI professional learning goes beyond the number of tools educators explore during a workshop; it is what happens after the workshop. Are educators asking better questions? Are they protecting student information? Are they making intentional choices about when AI adds value?

The tools in use today may not be the tools used next year, which is why understanding the fundamentals of AI and how it enhances learning is essential before diving into specific tools. Effective professional learning is not an experience that tells educators to embrace every new tool or avoid AI completely. The best learning helps educators understand AI, its capabilities, and guides them to approach it with curiosity, caution, and confidence, with the human side of education at the center.

As someone who writes and speaks frequently about artificial intelligence in education, I consider myself a strong proponent of its thoughtful use in teaching, learning, and school leadership. Because AI has become such a regular part of my own professional work, I was genuinely surprised to learn how little my daughter, a freshman in high school, had used it, and more importantly, how skeptical she was about its growing role in school.

Her concerns grew largely from her identity as an artist. She spends a great deal of time drawing and creating, so she is especially attentive to the ways AI-generated art may affect human artists, particularly when original work can be copied, repurposed, or used without the creator’s knowledge. What began as a conversation about AI and art quickly expanded into a broader discussion about effort, learning, creativity, and the growing dependence on AI among students.

I was struck by how mature and carefully considered her views were, especially because they differed so sharply from my own generally positive stance toward AI. Rather than trying to persuade her or correct her assumptions, I became curious about how she had arrived at those conclusions and what she was observing in school.

That curiosity led me to ask whether she would sit down with me for a brief interview. I emphasize “brief” because, like many teenagers, she has a limited tolerance for anything that feels even remotely school-related, especially when the interviewer happens to be her father.

During our conversation, I asked her what AI means to her, what she is seeing among her classmates, and whether she believes it has a constructive role in education. Her answers were candid, skeptical, and often surprisingly nuanced. While adults continue to debate what AI may eventually mean for schools, she described what it already looks like from the perspective of a student living through that change.

This article is based on a semi-structured interview with my daughter and her perspective is not intended to represent the experiences or opinions of all students. Qualitative conversations such as this can provide meaningful insight into how individual students are making sense of emerging technologies in their schools and daily lives.

AI as a Substitute for Thinking

I began by asking what came to mind when she heard the phrase “artificial intelligence.” Her answer was immediate:

“A robot who does work for you because you can’t do it yourself.”

When I asked her to explain, she continued:

“You just type in a prompt and it does it for you instead of using your own brain and doing the work yourself.”

Her description was not quite how I would define AI, and part of me wanted to explain that a large language model does not literally think or work in the same way a person does. I intentionally resisted that urge because I wanted to understand her perspective rather than shape it. I was interested in how a freshman interpreted AI through her own experiences, not whether she could provide a technically precise definition.

Even so, her response was striking because she did not begin with the language adults often use when discussing AI. She did not describe it as innovative, efficient, exciting, or transformative. Instead, she framed it around effort and ownership. In her view, AI becomes problematic when it replaces the thinking a person should be doing. That distinction would continue throughout the interview. She was not simply asking whether AI could produce an answer, but what happens to the learner when the tool takes over the work.

Seeing Dependency Among Students

Although she said she does not really use AI herself, she described seeing it frequently among classmates:

“A lot of students in my class use it to their own advantage, saying, ‘Oh, I don’t know this question. I’m just going to use AI to do it.’”

One of the clearest examples came when her school removed student access to Google Gemini. She recalled that students immediately reacted:

“The kids started freaking out.”

When I asked why, she answered:

“Because they use AI so often, they depend on it.”

She also described the kind of response she heard from peers:

“What am I going to do? I can’t turn in my essay if I don’t have it.”

That moment captures more than frustration over losing access to a tool. It suggests that, for some students, AI may already have become embedded in the process of completing schoolwork. When the tool disappears, so does their confidence in being able to produce the work independently.

Her concern was not simply that students were using AI, but that some were becoming unable, or at least unwilling, to proceed without it.

When Grades Matter More Than Learning

Perhaps the most mature observation she made came when she explained why students might turn to AI so quickly. Rather than blaming students alone, she connected their behavior to the incentives surrounding them:

“I think a lot of the world has made getting grades more important than learning the actual stuff.”

She then put the issue even more directly:

“Grades matter more than what they actually get in their brain.”

That statement deserves serious consideration from educators. It suggests that AI use cannot be separated from the larger culture of schooling. When students believe that the final grade matters more than the process of understanding, tools that produce faster answers become especially appealing.

In that sense, AI may not be creating an entirely new problem but intensifying an old one. Schools have long struggled with the tension between performance and learning, and now AI makes it easier for students to produce polished work without necessarily developing the knowledge behind it.

Her comments also challenge educators to look beyond detection and enforcement. If students are using AI because they feel pressure to produce, then the conversation must include assessment design, grading practices, workload, and the messages schools send about what success actually means.

AI as a Tutor, Not a Replacement

Despite her skepticism, she did not reject AI completely. Instead, she drew a clear line between using AI to learn and using AI to avoid learning.

When discussing a difficult math problem, she said:

“Instead of having AI give you the answer, maybe use it properly and have it create a good step-by-step way to help you.”

She then summarized her view in a single sentence:

“It should be a tutor instead of someone to do the work for you.”

This may be the central idea of the entire interview. For her, the value of AI depends on the role it plays. A tutor explains, prompts, models, and supports. A replacement takes over the task and removes the learner from the process.

That difference is especially important for schools because it offers a more useful framework than simply labeling AI as good or bad. The better question is whether the student remains intellectually engaged. Is the tool helping the student understand, or is it allowing the student to bypass the work entirely?

Her answer was consistent: AI should help people think, not think in their place.

A Human-First View of Art

Her perspective on AI is deeply shaped by the fact that she is an artist. Drawing is not merely an activity she enjoys. It is a skill she has spent time developing, and she sees creativity as something connected to practice, effort, and personal expression.

When I asked why she had not used AI very much, she explained:

“People who use AI to make art are looked down upon, in my opinion, because they can’t draw it themselves or they have to use a robot to generate it.”

Her language was blunt, but the underlying concern was about authenticity. She values the work that goes into developing an idea, choosing how to represent it, making mistakes, revising, and improving. From her perspective, generating a finished image through a prompt can bypass the very process that gives artistic work meaning.

At the same time, she offered a more constructive possibility:

“Instead of using a bot to generate a piece of art, you can draw something yourself and then ask AI, ‘How can I make this art piece better?’”

Once again, she returned to the distinction between support and substitution. She was more open to AI when the human remained the creator and used the tool to receive feedback. She was far less comfortable when AI became the creator itself.

Her concern also reflects a broader anxiety among artists about how creative work may be copied, repurposed, or used without permission. For a young person developing her artistic identity, those concerns are not abstract but directly affect how she thinks about ownership, originality, and whether technology is respecting or erasing human contribution.

Schools May Be Responding Too Late

When I asked what schools should teach students about responsible AI use, she raised another important issue: timing.

“AI has already come out, and teachers are a bit late teaching it.”

Her point was that students have already encountered and formed habits around AI before many schools have developed clear guidance. By the time formal instruction arrives, some students may already see AI as a normal way to complete assignments.

She was also skeptical that instruction alone would change behavior:

“I don’t know if the kids would care about how they teach it. They’re still going to use it irresponsibly.”

Her response may sound pessimistic, but it reflects a practical understanding of student behavior. A single lesson on responsible use is unlikely to undo habits that have already formed, especially when those habits are reinforced by convenience, pressure, and easy access.

This suggests that schools need more than one-time AI literacy lessons. They need consistent expectations, well-designed assignments, clear boundaries, and conversations that help students understand why certain uses support learning while others undermine it.

The Role of Teachers

When I asked what role teachers should play as AI becomes more common, she was not fully convinced that AI should become a regular part of education. Still, she believed that if teachers use it, they should define its role carefully:

“They can say, ‘This is going to be a secondary tool or a helper. You can only ask questions to help you instead of giving you the answer.’”

Her use of the phrase “secondary tool” is important. She does not believe AI should sit at the center of learning. The teacher, the student, and the learning task should remain primary, while AI occupies a limited and clearly defined supporting role.

That view also places responsibility on educators. Students need more than access to a tool. They need examples of appropriate use, boundaries around inappropriate use, and opportunities to practice using AI in ways that deepen rather than replace thinking.

Her Message to School Leaders

I ended by asking what message she would give to teachers and principals.

Her response was brief and direct:

“Please don’t generate art pieces, and please have better restrictions on AI usage.”

The first part reflects her identity as an artist. The second reflects her concern that current expectations are not strong or clear enough.

Her answer also complicates the assumption that students always want fewer restrictions. Some students may actually be looking to adults to set firmer boundaries, especially when they see classmates becoming dependent on tools that may weaken their learning.

What Her Perspective Taught Me

As I reflected on the interview, I realized that her comments went beyond technology, and were about effort, creativity, integrity, learning, and what students believe school is asking them to value.

Her perspective may not represent every student, and it should not be treated as though it does. Some students are enthusiastic about AI, some are cautious, and others are still deciding what they think. Her voice matters precisely because it adds complexity to a conversation that is often framed too simply.

Perhaps what surprised me most was not her skepticism but how little she had actually used AI. As someone who advocates for the thoughtful integration of AI in education, I expected that a high school freshman would already be experimenting with these tools regularly. Instead, I found myself listening to a student who had largely formed her opinions by observing the behaviors of her classmates rather than relying on AI herself. That realization reminded me that student perspectives on AI are far more diverse than we sometimes assume.

She is skeptical, but she is not closed-minded. She objects to AI when it replaces thinking, creativity, or human effort, yet she can still imagine a constructive role for it as a tutor, guide, or source of feedback.

The consistency of her view was what stood out most. AI should explain, support, and improve. It should not take over.

Perhaps the most important lesson from our conversation is that schools should not design AI policies based solely on adult assumptions about what students need. Students are already observing how AI is affecting their peers, their classrooms, and their own sense of what counts as authentic work. We should listen to them more carefully because they are not merely preparing for an AI-shaped future. They are already living in it.

I was not prepared for just how much I would hate using Speaker Coach, but that doesn’t mean it wasn’t helpful.

The AI-powered speaking coach and presentation mentor built into PowerPoint is designed to help anyone, including educators and their students, improve their public speaking abilities.

I’ve always been comfortable with public speaking, and think I do a solid job. My voice carries, I speak confidently, and I feel I know how to read a crowd and respond in the moment to their moods. But I am by no means perfect. I know that I can mumble, I lose my train of thought sometimes, and when I read from a prewritten script or speech, I have a tendency to read it in a semi-lifeless robotic voice. Also, I have a bad habit of reading too much from slides, especially when I get nervous, which is why I often avoid using slides in my presentations.

Speaker Coach caught on to all these weaknesses, almost immediately, which was, frankly, a little bit disheartening. I always hoped I was being too hard on myself and that I was actually far better at public speaking than I thought. This made using Speaker Coach an unpleasant experience, but it also meant it was doing its job and could help me improve my presentations.

So overall, despite some minor issues, Speaker Coach seems to be a strong, if slightly intimidating, tool for improving public speaking. Here’s a closer look.

How Speaker Coach In PowerPoint Works

Speaker Coach is built into PowerPoint. To access it, users access the web version of PowerPoint through a Microsoft-linked account. They then upload an existing slideshow or create a new one.

Once a user is ready to practice, they select the Slideshow tab, then choose the Rehearse with Coach option. After this, you can move through your presentation as you talk and practice with Speaker Coach monitoring it all.

As you speak, the program gives on-screen guidance by default, though you can turn off this feature. It will provide feedback such as telling you to speak more slowly, avoiding “ahs” and “ehs,” and offering encouragement that you are doing great and should keep it up.

Once you finish the presentation, you get a breakdown of your overall performance, including a wide range of useful feedback on your presentation. It will let you know, for instance, how fast you spoke and it was too rapid or too slow, and whether you read from the slides directly too often, and if so, which slides. It also offers a chart on how your pronunciation was, as well as your tone variation and your use of inclusive language, as well as many helpful details such as how long you spent on each slide.

What Are Speaker Coach’s Best Features?

Public speaking is often a vital skill for educators, but it can be a tricky one to practice. Speaker Coach offers the chance for hands-on practice with specific and actionable feedback.

The final report is really helpful in assessing your strengths and weaknesses as an instructor. The fact that it recognized the slides I had a tendency to read too much from was fantastic.

It can also help you identify the strengths and weaknesses of different types of presentations. I practiced with scripted presentations and with unscripted ones in which I relied only on my slides. I learned, unfortunately but not particularly surprisingly, that I have too many "ums" and "ahs" when I don’t use a script. Reading from a script corrects this, but then my voice becomes more lifeless and monotone. Identifying this helps me target both concerns as I prepare for future presentations.

What Speaker Coach Features Need Work?

As I mentioned at the start, despite finding it helpful, I didn’t enjoy using Speaker Coach. Some of this is probably inherent in practicing a skill as deeply personal as speaking, but there were also design features that made this worse.

The real-time feedback tool was more annoying than helpful, and I was tempted to turn it off, which is option. If I said “ah" or "mmm” it would start scolding me, which made me feel like a kid when adults used to admonish me to "stop mumbling." Even when it praised what I was doing, it was distracting.

Another weakness of Speaker Coach overall is that even though it was great at diagnosing the problems in speech, its suggestions for fixing these problems are limited.

It told me, for instance, to avoid saying “ah” by pausing and breathing, which I know I should do, except it didn’t offer more concrete advice on actually practicing this skill or how I might build in these natural pauses. When it came to speaking in a monotone, Speaker Coach didn’t offer any suggestions of any kind for improvement.

Overall, more detailed suggestions and/or links to resources on public speaking would take this tool to the next level.

How Much Does Speaker Coach Cost?

Speaker Coach is free with PowerPoint, which comes with most Microsoft Office plans available through schools or businesses.

Individual subscriptions range between $6.99 and $9.99 per month, depending on whether users pay annually or monthly.

Bottom Line on Speaker Coach

Ultimately, I found Speaker Coach helpful, if unpleasant, to use, but I plan on using it in the future as I try to improve my public speaking ability. I would recommend it to educators and students who are already comfortable with public speaking and looking to really hone their craft.

I wouldn’t recommend it to your average student who is working on a presentation for class because I worry the no-mercy criticism Speaker Coach provides could have a negative effect on someone who is new to presentations and likely already nervous about public speaking.

However, for the right type of educator or student with thick skin, Speaker Coach can be a helpful speaking coach.

The pace of advancement in artificial intelligence tools is breathtaking. Since the public release of ChatGPT in November 2022, multiple platforms have entered the AI arena, all vying to win adherents to their particular flavor of AI. While students may welcome the ability to quickly get answers without much work, educators and education leaders have been deeply uneasy about the potential effects of AI on learning.

With the growth of these platforms comes some desirable changes for education. One of the greatest concerns from the beginning has been its potential to disrupt genuine learning in favor of the nearly-instant answers and solutions provided by AI tools. The good news is that nearly all of the tools here are structured (in theory at least) to support true independent learning and critical thinking rather than serve as answer machines.

To learn more, continue reading — and be sure to read the full Tech & Learning reviews available for most of the listed platforms.

Best AI-Powered Tutors for Education

Gemini’s Guided Learning Mode
Rated highly by Tech & Learning writer and educator Erik Ofgang, Gemini’s Guided Learning Mode is built on the AI model LearnLM, which is designed for education. Erik tested the tool on a variety of topics, including wave equations in quantum mechanics and journalism (a subject he teaches at the post secondary level). While it isn’t perfect, Erik concluded that it’s currently “the best example I’ve seen of what an AI tutor can be.” Free.

Khanmigo
Top-rated by writers from Common Sense Media, the Wall Street Journal, and The Washington Post, Khanmigo is the AI-powered tutor from the well respected education nonprofit Khan Academy. It provides test prep, feedback on writing and facilitates deep learning—without giving answers. In addition to its role as a tutor for students, Khanmigo also offers differentiation, lesson plans, quiz questions, student groupings, hooks, rubrics, and more for teachers. The cost is a modest $4 monthly.

StudyFetch
StudyFetch allows students to upload a wide variety of materials—class notes, PowerPoints, PDFs, study sets from Quizlets, video, audio, and more—from which it generates flashcards, quizzes, games and other study aids, while its AI-powered tutor, “Spark E.” guides students through difficult concepts and encourages deep thinking. Teachers can use StudyFetch to help with lesson planning, presentations, quizzes and even train AI tutor models to their specifications. Free basic account, with weekly ($3.92/week), monthly ($7.99/month) and premium plans ($11.99 to $19.99/month).

Tutor AI
Tutor AI allows students to study nearly any topic based on learning goals, current knowledge, and preferred learning style. Unlike many other tools profiled here, Tutor AI also solves homework problems for students, providing step-by-step solutions with detailed explanations—a practice which may not support genuine learning. For educators, the platform offers the ability to create courses, customized, personalized lesson plans, flashcards and more. Free basic account and two premium levels ($13.33/month, $17.50/month).

Flint
Designed specifically for education, Flint supports student learning by answering questions, offering feedback, and providing reminders for deadlines, time limits, and follow-up activities. Teachers can create or upload content using items such as worksheets, rubrics, lessons, or external resources as needed. Best of all, teachers can set the capabilities of AI actions—from guiding to hinting to explaining—thereby ensuring real learning, rather than AI-assisted parroting. Flint’s free plan allows up to 80 users, while premium plans vary based on school size.

MagicSchool AI
Billing itself as “safe secure AI for schools,” MagicSchool AI’S SOC 2-certified and FERPA/COPPA-compliant platform is aligned with district-level privacy standards and aims to promote critical thinking while teaching kids responsible AI use. Among the more than 50 AI-powered tools for students are those created for feedback, scaffolded learning, and specific task support. Most of the customizable tools seem designed to support genuine learning. For example, the “5 questions tool” generates 5 questions on any topic to deepen student thinking, while “Math review” generates review questions to improve math skills and the AI Learning Assistant provides guidance on multiple topics without giving direct answers. The robust free basic account includes more than 50 tools, quizzes, feedback, and more.

Amira Learning Tutoring and Reading Assistant
Research-based and validated Amira Learning offers a personalized Claude AI-powered reading tutor that allows the unusual ability to listen to students reading aloud, recognize problems with fluency and accuracy, and provide real-time suggestions adapted to the student’s struggles. The standards-aligned, FERPA- and COPPA-compliant platform is available on a district-wide basis.

Class Companion
Class Companion provides personalized feedback of student work in nine subject areas, from English through biology, and extends substantial educator control options, a feature cited by Erik Ofgang as being one of its strongest. He also praised the platforms’ support for the mastery model of education, which allows students multiple opportunities to demonstrate their expertise. Free for educators; however, some features such as LMS integration are available only with a school or district subscription plan.

ChatGPT Study Mode
Rather than simply providing answers, ChatGPT’s Study mode acts as a tutor, offering responses that guide students’ learning without spoon feeding them. Aimed at college students, it employs techniques such as the Socratic method, interactive prompts, scaffolded suggestions, and knowledge checks in the form of quizzes and open-ended questions. When tested by Erik Ofgang, it earned a strong, albeit qualified, “thumbs-up.” Free for all logged-in users on the ChatGPT Free Plan, as well as paid tiers like Plus, Pro, and Team.

Microsoft Reading Coach
Reading Coach provides a personalized reading experience for learners by generating a custom story based on the student’s selection of character and setting. As the learner reads aloud, Reading Coach evaluates their speech and gives feedback on their speed and accuracy. Gamified features such as rewards, badges and streaks contribute to kids’ engagement. Fully free with Microsoft account sign in.

As a longtime educator, I’ve spent a great deal of time designing assessments that promote student choice and voice and enable students to truly show what they know and can do. However, with the rise of artificial intelligence in education, deciding how to assess students has become more challenging.

Students may have access to AI both in and outside of our classrooms. It can generate summaries, write essays, provide explanations, create outlines, and offer feedback within seconds for them. AI can help learning, but it requires educators to think beyond traditional assessments that worked well before AI.

A major concern is that students will cheat or that assessment is broken. Rather than focusing only on how to stop students from using AI, we need to design tasks that make students' thinking more visible. When an assignment asks students to explain their decisions, reflect on the process, make connections, analyze feedback, defend their thinking, and revise their work, learning becomes harder to outsource.

Making this shift matters because if students are only assigned to choose an answer, write a paragraph, create a presentation, or design a final product, AI can do these tasks for them.

Providing guidance for students on how to responsibly use AI is also critical.

Moving Beyond Product-Only Assessment

For years, assessments in my classroom were often built around a final submission such as an essay, project, test, or presentation. I would evaluate it, provide feedback, and move on. These all have value because students build skills in communication, organization, critical thinking, and reflection.

However, what students submit may not fully demonstrate their learning. A well-written essay does not necessarily show understanding. A correct answer does not reveal whether the student can explain the reasoning or simply guessed or used AI. A well-designed presentation does not prove that the student made the design choices on their own. For these reasons, the assessment design needs to be more intentional.

Educators need to look at what the student created, consider how they got to that point, what choices they made in the process, what information was used, how they revised their work, and what I think is important, is what can the student share about their learning. Did the student use AI (if permitted), and how?

Using these reference points will shift assessments from being a simple, quick check of understanding or task completion to showing deeper learning and content retention.

Shifting to More Visible Thinking

Finding ways to make thinking visible takes a little time at first, but it is how to truly see a student’s learning journey. To start, educators can set up a series of checkpoints. Rather than asking students to only submit a final essay, for example, teachers might ask for various artifacts from their work. Students could provide their initial question, a claim, a draft, a peer feedback reflection, revisions made, and a final explanation of what changed. These components encourage students to notice their own thinking.

Educators can also ask students to explain their thinking and choices made. Why did they select this source? Why did they organize the project in this way? Why did they reject one idea and keep another? Why did they make the revisions they did?

These explanations can provide greater insight than the final product alone.

In their work, students can show their thinking through annotations, peer discussions, drafts, concept maps, note-taking, reflections, and even demonstrations. The format may vary depending on the task, but the goal is to have students explain their learning.

Rethinking AI Use and Academic Integrity

A valid concern is that AI makes it harder to understand what students actually know. However, if we ban the use of AI entirely, it won't prepare students to use these tools responsibly.

Instead, educators should create clear expectations around when AI is and is not permitted and model this for students to show how it can be an impactful part of their learning experience. Not every task should involve AI. Students still need opportunities to think, write, create, and problem-solve without relying on a tool. However, students also need guidance in how to use AI thoughtfully, ethically, and transparently.

I have found it to be helpful to design assignments with different levels of AI use. Teachers can build AI expectations directly into assignments. Directions might say, “AI may be used for brainstorming but not for drafting,” or “You may use AI feedback, but you must submit your original paragraph, the feedback, and an explanation of your revisions.” This turns AI use into part of the learning process rather than a hidden behavior.

Establishing a structure such as this helps students understand that responsible AI use is not “all or nothing” but instead depends on the purpose of the task they are being assigned.

Transparency is key. If students use AI, they should be able to say how they used it. Did they ask for topic ideas? Did they use it to clarify vocabulary? Did they compare explanations? Did they request feedback on the organization of their writing? Did they ask for a revision?

Requiring a short AI use note can help students reflect on their choices and help teachers better understand the learning process.

Assessments Should Include Conversations

A powerful assessment tool is conversation. When students talk about their work, teachers can sense understanding, confusion, confidence, and growth in ways that are not always visible on paper.

Not every assignment needs a formal presentation. Having short conferences with students can be meaningful. When a teacher asks a student to talk about the strongest part of their work, an area where they struggled, or even what they would change if they had more time, these questions provide more insight into student thinking. And these are also great ways to build relationships that support student growth.

Students could also record short audio or video reflections explaining their process. I’ve had students do this using Padlet, and it was great for building many skills. Students can engage in peer discussions, small-group activities such as debates, or do gallery walks. These moments create opportunities for students to demonstrate more ownership in learning.

When students know they will need to explain their thinking, the assignment changes. The focus moves from simply producing something to understanding it well enough to discuss it.

Designing Assignments AI Cannot Easily Replace

No assignment is completely “AI-proof,” and that should not be the goal. A better goal is to design learning experiences that are meaningful, contextual, personal, and connected to students’ thinking.

When assignments include local context, classroom experiences, personal reflection, student choice, and live discussion, these become more meaningful and more likely to promote content retention. For example, instead of asking students to write an essay on a historical event, ask them to connect the event to a class discussion, analyze a specific primary source used in class, compare interpretations, and explain how their thinking changed. Or instead of asking for a general science report, ask students to collect observations, analyze class data, explain unexpected results, and reflect on the process.

The more connected the work is to the classroom learning experience and real-world events, the harder it is for students to lean on AI without truly engaging in the learning experience.

It should not be about making assignments more difficult just to “catch” students. By making assignments better so that students have to think, create, question, explain, and reflect, they engage in the process itself and focus less on one final product.

The Importance of Reflection

Reflection may be one of the most important assessment practices in an AI-enabled world. When students reflect, they make their thinking visible. They explain what they tried, what worked, what did not, what they learned, and what they would do differently. They see the process of learning more.

Reflection also helps students develop metacognition. They begin to understand themselves as learners. They notice when they relied too heavily on a tool. They recognize when AI helped them think differently. They learn to evaluate whether a suggestion improved their work or weakened it.

Simple reflection prompts can make a big difference:

• What part of this work best shows your thinking?
• Where did you struggle?
• What feedback helped you improve?
• How did you use AI or other tools responsibly?
• What would you change next time?

The Most Important Outcome of All

Assessment after AI is not about fear, but rather about intentional and purposeful design.

Educators do not need to forget everything they believe about assessment. Many strong instructional practices are already in place: formative assessment, conferencing, portfolios, project-based learning, performance tasks, peer feedback, revision, self-assessment, and reflection. Now, with the availability of AI, these practices are even more important.

The future of assessment will require us to find a better balance between a final learning product and the learning process. Students should still have opportunities to create what they have always created, but they should be able to explain their thinking behind it. They should know when AI is appropriate, when it is not, and how to use it in ways that support rather than replace learning.

For students to be well-prepared for the future, we have to assess how they think as well as what they produce. To do this means designing assignments and assessments that push students to question, decide, explain, revise, reflect, and take ownership of their learning.

AI can generate answers. But students still need to develop judgment.

And that may be the most important outcome of all.

CoChat is an AI-powered tool that provides educators, students, and researchers with a host of capabilities when it comes to conducting research.

While there are other AI research tools, a common problem is citation hallucination. CoChat is designed to cut back on those hallucinations, and that feature is definitely a plus in my experience with CoChat.

Additionally, CoChat is great for deep research. When I searched topics, I was well-versed in, it brought up papers that had taken me hours and, in some cases, days, to track down on my own the old-fashioned way.

After using it for a few weeks, I am impressed with CoChat overall and find it to be the strongest AI deep research tool that I've tried. Here's a closer look at how CoChat works, what it costs, and its best features.

What is CoChat?

CoChat is an AI research tool that harnesses advanced LLMs, including GPT, Claude, and Google, to search more than 200 million academic papers. It offers a feature that allows you to compare different answers from different models and then choose the best one.

In practice, CoChat functions as a highly skilled, very quick research assistant that can find and summarize relevant research on a given topic. CoChat also automatically verifies citations to reduce the frequent AI problem of citation hallucination.

After using CoChat to search several different research areas, ranging from quantum physics to education and health, I have been impressed with its ability to quickly collate research that would take me hours to track down with a tool such as Google Scholar. As with any research tool, it didn’t find all relevant research, though it did a better job than other similar tools I’ve used previously.

More broadly, I still wonder whether there are benefits to the old-fashioned Google-style search. Certainly, the time I’ve spent searching for relevant research hasn’t all been a waste because when I’m doing it, part of my learning process is the search itself.

That said, I do like using a CoChat-like tool toward the end of the process, just to see if this different method turns up anything new.

What Are CoChat's Best Features?

CoChat’s best feature is its robust ability to search through academic research and summarize its findings, all while providing direct links to the original papers. It accomplishes this through access to cutting-edge AI models and, in my experience, seems capable of both speeding up new research and helping expand research I’d previously conducted.

Additional strong features are that it urges you to double-check its work and that its default writing mode is a conversational summary and literature review style. This review summary is helpful, and as an added bonus for educators, is not something a student might be tempted to hand in as their finished paper.

That said, CoChat does frequently offer to help students do more with their research. For instance, after telling me it made a literature review table, it offered to “turn this into a shorter plain-English brief or a tighter source matrix for class use.” When I took it up on this offer, I suspected it would turn the work into a paper that a student could then submit. By doing this, I worried it would move from what I see as an appropriate AI use, research assistance, to an inappropriate one, doing the writing and thinking for a student. Ultimately, those fears were unfounded. CoChat just condensed its findings and used a more conversational tone to explain.

Despite passing this test, when I explicitly asked CoChat to turn its findings into a paper for an assignment "in my class," it was willing to do that, which isn’t ideal.

Even so, given that the availability of AI tools willing to write your papers, emails, and texts is so widespread, it was refreshing that CoChat required this additional step.

What Does CoChat Cost?

The basic version of CoChat is completely free and is what I used for this article. I didn’t come up against any limitations, but there are some metered elements to the free tier such as three file uploads per day and a limit of 10 citation verifications.

Subscriptions start at $9 per month, and there is a pro tier at $19 per month.

The Best CoChat Tips and Tricks

Verify Research

One of CoChat’s main selling points is its ability to check sources it has cited to make sure none are fabricated. Users should take advantage of this feature.

Create an Instant Literature Review

CoChat is as good as any tool I’ve encountered at creating fast and helpful literature overviews on diverse topics.

Use It In Addition To Other Research

Even though it’s tempting to use CoChat at the start of your research, I like using CoChat after I’ve done some research to make sure I found all the relevant material and/or see if it turns up anything that I missed in my old-fashioned search; both happen on occasion. Therefore, using CoChat in this manner seems to deepen my research process overall.

As the academic year approaches, the integration of artificial intelligence into educational settings has shifted from a novelty or optional consideration to more of a necessity.

This summer as I engaged with my students, I had them each evaluate 10 to 12 generative AI tools of their choice. This summary is based on analysis of their notes from recent explorations and highlights a core group of "top-tier" tools that provide substantial value for both instructional efficiency and student success.

By focusing on tools that prioritize research synthesis, administrative logic, and visual communication, teachers and students can navigate the upcoming fall semester with unprecedented productivity.

The following six tools emerged as the most effective according to educators, offering a roadmap for fall implementation.

Gemini Notebook (The Research and Synthesis Powerhouse)

Gemini Notebook stands out for its "grounded" approach to AI, processing only the specific documents a user uploads. This makes it a premier tool for maintaining academic integrity while managing heavy reading loads. Personally, I find this one of the best tools available for summarizing large sets of articles or student papers to find themes or trends.

• How Teachers Can Use It: Educators can upload their entire curriculum unit to freshen it up or mold the materials differently. Student A noted that it can generate podcasts based on uploaded material, suggesting that "instead of having kids read a chapter of a short story, it could turn that into a podcast for you." It is highly effective for creating executive summaries and slide decks from dense research articles to maximize lesson prep time.
• How Students Can Use It: Students can upload lecture notes, textbook chapters, or articles to generate study guides, practice quizzes, or thematic literature overviews. Student B remarked that the audio overview feature is a "game changer," particularly for organizing complex concepts for long-term projects such as a dissertation.
• Concerns: A primary concern is the ethical implication of feeding the beast by uploading copyrighted works into an AI model. Additionally, users must be wary of surface level analysis, as the tool may occasionally overlook important nuances in the source material.

MagicSchool AI (The All-in-One Educator Assistant)

Historically one of my primary recommendations to K-12 educators–it consistently rates a "10/10" by users–MagicSchool AI is a platform designed specifically for the unique needs of the K-12 and university environment. It has a lot of built-in templates and is fully FERPA compliant.

• How Teachers Can Use It: Teachers can utilize a suite of more than 60 tools to generate IEP drafts, PLC agendas, and standards-aligned worksheets. Student C called it a "homerun tool" because it "supports the productivity and direction of [Collaborative Team] time without intruding on the actual meeting."
• How Students Can Use It: The Student AI Tutor offers personalized, 1-on-1 support. Student D found the tutor to be “excellent" because of the "depth of conversation it was having. It told me when I was correct [and] when I wasn’t giving it the skill it asked me to show."
• Concerns: The automated nature of the tool can lead to a loss of human tone if not reviewed carefully. Student E warned that if students rely too heavily on the AI to compile information, they risk "short-circuiting" the struggle required for true critical thinking.

Canva Magic Studio (The Visual Communication Leader)

Canva has evolved from a simple graphic design tool into a comprehensive AI suite that lowers the technical barrier for creating professional-looking learning visuals.

• How Teachers Can Use It: Educators can use Magic Design to instantly prototype course materials, turning a lecture outline into a cohesive slide deck. Student F noted its value for creating "take-home fliers for students to share with parents/guardians" and "quick signs for lessons."
• How Students Can Use It: Students can synthesize complex protocols into clear infographics or collaborative group projects. Student E highlighted that it allows students to "demonstrate competency through multimedia creation, fostering both digital literacy and communicative competence."
• Concerns: A risk of visual homogeneity, in which AI-generated layouts feel repetitive or corporate, exists. Also, there is concern regarding the "Couch Potato" effect, in which students may bypass the critical thinking, research, and synthesis stages if graded purely on the visual artifact.

Consensus (The Academic Search Engine)

Consensus is an AI-powered search tool that helps users quickly identify what peer-reviewed research says about specific questions.

• How Teachers and Students Can Use It: Both groups can benefit from its Consensus Meter, which shows scientific agreement on a topic. Student G found it "amazing" for literature reviews, noting that it "returned sources along with citations, as well as the ability to preview abstracts and PDFs in the same place." Student H appreciated its "excellent" accuracy and ability to find research "without going down a rabbit hole."
• Concerns: A major risk is that students may cite articles based only on the AI summary without reading the full source, which could lead to misunderstandings of the research.

Perplexity (The Citation-Backed Search Engine)

Perplexity serves as an enhanced search engine that provides direct answers supported by current online citations. It has historically been one of my go-to deep search tools, and one of my recent doctoral students used it extensively to gather data for his Delphi Study.

• How Teachers Can Use It: Staff can use it for policy benchmarking or for lesson planning on tangential topics outside their area of expertise.
• How Students Can Use It: Students find it much more trustworthy than basic search engines for report writing. Student I highlighted that the use of citations makes it "highly appealing and ... more trustworthy and applicable to my studies." Student J described it as a "graduate-level Google" that simplifies research into a digestible format.
• Concerns: Because Perplexity pulls from real-time web sources, its results are subject to the errors and possible ethical challenges present on the open internet.

Claude AI (The Writing and Logic Specialist)

Developed by Anthropic, Claude is recognized for its ability to handle massive amounts of text and its focus on providing safe and reliable responses. My students often refer to it as the “poet of the LLMs,” as they think it provides the best editorial assistance when they are writing.

• How Teachers Can Use It: Claude thrives in administrative nightmare scheduling scenarios. Student L described how it caught hidden scheduling conflicts and provided "realistic compromises" when managing transportation, weather, and facility bookings.
• How Students Can Use It: Claude acts as an effective career coach, helping students refine resumes and cover letters while maintaining their personal voice. Student K noted “it was effective because it gave organized and specific feedback on my writing" compared against specific rubrics.
• Concerns: Claude can occasionally produce a generic voice unless the user provides very detailed input. Privacy is also a major concern; users must be careful not to paste identifiable student data into the chat.

Your Fall Implementation Strategy

For the fall semester, these tools can be viewed as collaborative partners rather than replacements for human judgment. As educators, we need to expand the variety of tools that we expose students to in order to ensure they become and then remain digitally literate.

The "80/20 Rule" suggested by Student E is an ideal guiding principle: let AI handle the first 80% of a task, the drafting and organization, while the human expert provides the final 20% of auditing, empathy, and contextual nuance. Of course, the educator and/or student need to know how to develop a good prompt in order to get the AI tools to deliver a product at 80%. That’s one skill that regardless of the tool needs practice to develop.

Another one of my students argued with a faculty member as to why it was okay to ask a human editor to assist them but not an AI one as long as the student was upfront and disclosed the use of AI. Particularly when it is not considered necessary to disclose the use of a human editor or a visit to the writing center for support.

By integrating these tools thoughtfully, educators can reclaim time for high-value relational work while empowering students to become critical editors and auditors of data in a rapidly changing digital landscape.

Thanks to Gemini Notebook and the Winona State University students of EL 590 for their assistance in creating this article.

Dabble is a writing tool and word processor designed to help novelists and writers of any long or scholarly work stay organized and focused.

I’ve been using Dabble to write a nonfiction book for more than a month and have been impressed with its ease of use and the way it allows me to jump back and forth between various chapters and stay organized with research. I’ll be recommending it to my master's writing students going forward, and would also recommend it to educators undertaking long writing projects of any kind, from a thesis for a master's program to a sizable research project or report.

Here’s a closer look at Dabble.

What is Dabble?

Dabble is a cloud-based writing tool that works like a souped-up version of Microsoft Word or Google Docs that is fine-tuned for writing. This may not sound necessary, but while I like Google Docs for short projects, I was finding it increasingly slow with long documents, and was having trouble keeping track of various drafts and versions. I like to work on each chapter individually but also have the option of viewing the work as a whole, which can become time-consuming with a general word processor.

For instance, when I use Google, I create a folder for each chapter, but then combining all those chapters and keeping track of updates becomes time-consuming. Dabble helps with all this.

Though designed with fiction in mind, Dabble works well for nonfiction. It automatically organizes your work around chapters. You can edit, export, and save each chapter by itself, or select the entire work. When you select the work as a whole, the chapters all come together without a hitch.

Dabble also has a cool scene feature that lets you break down each chapter into smaller “scenes,” which makes keeping track of your work’s structure easier. If you’re writing nonfiction, it may be easier to think of these breaks as “sections” rather than scenes—either way, it’s a helpful organization tool.

What Are Dabble's Best Features?

I’d been looking for a tool such as Dabble for a while. I wanted something cloud-based and that didn’t have a big learning curve. Dabble checks both these boxes. Everything is cloud-based, but it’s quick and easy to convert a Dabble file into a Microsoft Word, Google Doc, PDF, etc.

The layout is an absolute pleasure, and helps me start writing instantly without a learning curve. I had tried some other word processors designed for writing long works, and though writers I know raved about those, most proved too difficult to learn for me. After trying one demo, I realized I would need to devote some serious time to learning that software, and that was time I felt that would be better spent writing.

Dabble’s features are not only helpful but easy to use, which is crucial. I didn’t anticipate using the scene feature as much as I have; it really helps me navigate a chapter more quickly in the editing process and identify the parts that need work.

Another wonderful feature is that Dabble saves to the cloud automatically. So if you want to edit on multiple devices, you can. I’ve even read nearly completed drafts on my phone instead of my laptop and have been able to successfully make little changes.

While its prices make it more realistic as an option for educators to use themselves rather than with students, all these features would certainly help writing students navigate longer projects. This is why I'll let my graduate writing students know about it as a potential tool to use outside of class.

What Are Some Areas That Dabble Could Improve?

Though I am a big fan, not everything Dabble does is perfect. For example, it is designed for fiction writers and I write nonfiction, so the research assistance tools could be more robust. In a perfect world, there would be a place to save my research for each chapter at a spot that would be linked to that chapter but not show up in the actual text. But since nonfiction is clearly not the tool’s focus, this is an understandable drawback.

More significantly for writers of any kind, it doesn’t have the best in-tool draft-saving capabilities. It’s easy to back up each chapter to Google Drive or elsewhere, which is great, but I haven’t figured out an easy way to do all this within Dabble. One workaround is to save these elsewhere in a Dabble-linked folder. This works, but I'd love the option of saving everything related to my book in Dabble and only using my own computer or something such as Google Drive as a backup.

How Much Does Dabble Cost?

After a 14-day free trial, Dabble pricing starts at $19 per month, with additional features available for more premium subscriptions at $29 and $49 tiers. I've been happy with the starter $19 subscription.

What Are The Best Dabble Tips and Tricks?

The Scene Feature

Breaking my chapters up into scenes helps me think about each chapter and complete it bit by bit. It also makes navigating the chapter during the editing phase easier.

Tracking Dally Progress

Dabble lets you set a daily writing goal and tracks your progress toward that with a daily word count feature. I wasn’t drawn to this feature at first because word counts are not always the best way to track writing progress, particularly nonfiction. You can crank out a lot of words but not really move your project forward; meanwhile, you can write one great paragraph and thus, have spent a fruitful day. That said, Dabble’s progress feature is a better gauge of progress than I anticipated, and it’s surprisingly nice to know exactly how much I added to the manuscript in a writing session. There are days, for instance, when I feel like I’m not getting anything done but realize I’ve logged close to a thousand words.

Exporting Material

This isn’t the flashiest feature, but it's important. The way this program works with Word and Google Docs makes getting my work ready to send to my agent or publisher really easy. I’m spending less time thinking about formatting and more time focused on what’s actually important: the writing.

Even the most ardent AI in education enthusiast will admit that there are times when it’s a mistake to use AI. I’ve been thinking about this a lot recently.

While I never encouraged my students to use AI in any capacity, there were many uses, such as brainstorming, which I was okay with, in theory. These days, I am less open to this and other uses and more wary of AI in general. I’m not the only one.

We’re currently in the midst of what The New York Times recently referred to as a backlash against technology in education. Even among tech-savvy teachers who are AI enthusiasts, I’m hearing more discussion of when it is appropriate or not to use AI.

Based on my own observations and conversations both with AI enthusiasts and skeptics, here are times AI should definitely be avoided in education.

1. Do Not Use AI For: Assessment

Matt Jubelirer, General Manager of Education Marketing for Microsoft, is a big fan of AI use in education in general, as you might suspect. But he told me recently that there’s a growing realization among educators he speaks with that AI is not always the solution.

When it comes to creating tests and assessments, Jubelirer says AI can be really helpful on the preparation side, whether that’s developing a rubric or devising questions, but there’s a limit.

“We shouldn't be using AI to grade people, because we need human judgment,” Jubelirer says. No matter what you feel about AI in general, it's hard to argue with that.

2. For Brainstorming

Adjunct professor Lynn M. Patarini has started asking students not to use AI for brainstorming. “I would prefer folks to use their brain and not rely on AI,” says Patarini, who teaches at the University of Connecticut, Central Connecticut State University, and CT State Community College: Middlesex.

She adds, “I have my students making lists constantly to try and train their brains to think about aim and purpose.”

A year ago, the idea that AI shouldn’t be used for brainstorming may have evoked some controversy. But these days, many of us have witnessed firsthand how AI brainstorming sessions can lead to generic, uninspired outcomes and encourage even well-intentioned students to write with AI guidance.

I know I have. After noticing that AI's advice was making student writing more generic, I’ve recently begun actively discouraging the concept of AI as a writing partner, both among my students and fellow educators.

3. For Lesson Generation

William Henry, an eighth-grade English teacher at Amity Middle School and a freshman composition instructor at Quinnipiac University, both in Connecticut, is a fan of some uses of AI in the classroom. However, there are many instances when he believes it should not be used. One of these no-go AI uses is for lesson creation.

“It could be helpful with generating ideas or ways to implement an idea, but the teacher should be coming up with the bulk of the lesson,” he says.

Lesson planning is a frequently cited AI use case, but as Henry notes, it works best when it's helping an educator execute their vision, not steering the educational ship all on its own.

4. For Emails

This is one that should go without saying, but unfortunately for many students, and even some of those who work in education, it doesn’t.

I have not received AI-generated emails from educator colleagues, but I know people who believe they have. I have recieved many clearly AI-generated emails from students. This might be the most unsettling use of AI I’ve come across.

It may sound like a cliché, but my favorite part of being a teacher is the one-on-one communication with students. Going line-by-line through their writing and offering praise and suggestions, answering questions via email, and connecting with a real human being trying to learn can, at its best, be a beautiful and deeply rewarding experience for all involved. But responding to a lifeless email that was clearly written by a student who put my suggestion into a prompt leaves me cold.

We need to remind students, and even educators, not to be afraid to share their own writing voice with professors, friends, and colleagues. I know some of us are self-conscious about the way we write, but every student I ever had is a better email writer than AI, no matter how much work their spelling or sentence structure might need.

Technology moves at such a rapid pace, it’s hard to keep up with the changes as a school leader. Since publishing my first article ("Did Apple Finally Find Its Chromebook Killer?") (just a month ago), Apple quietly increased the education price of the Neo by $100. That changes part of this conversation as at $499, the Neo was an intriguing disruption. At $599, districts will rightly ask much harder questions about return on investment. However, like I said in the previous article, this isn’t just about the price. It’s about value over time.

Now that we have that disclaimer out of the way, allow me to lay out the ground rules of this article. I’m not trying to get too overly geeky about this bake-off. I’m evaluating these through the eyes of a technology director trying to make a major purchasing decision that affects learning.

Which leads me to my second disclaimer: These decisions need to involve more than the person who is deploying and distributing the machines.

With that said, most of what follows are several different categories all school leaders should be considering when making a purchase of this magnitude. In the wake of the “tech lash,” leaders need justifiable reasons for introducing devices and screens into the learning environment.

Neo vs. Chromebook: Overview

My original thought for this bake-off was to compare Apple’s Neo with the Dell Chromebook 14 Laptop. These are both similar in size and price, but the truth is, most schools don’t purchase that model for their students. They tend to purchase smaller sizes (typically 11.6-inch HD models) and lower RAM and storage. As Chromebooks make up 93% of the K-12 market, I wanted to make this a comparison against the most commonly purchased device. Currently, Lenovo has a slight edge (25% vs. 21%) over HP when it comes to units shipped in the U.S.

Furthermore, the most common type of Chromebook is the “Clamshell” laptop. More than 62% of Chromebooks purchased are this type but there is a significant growth rate in the second type (Convertible 2-in-1 which is bought at nearly 15%).

Taking all that into account, the following series of tests and benchmarks will be run between a Lenovo Chromebook 100e Gen 5. The Gen 4 was the most purchased of its fleet last year, so we’ll go with the newest model when doing comparisons for the rest of this article. I fully recognize that many of you reading this will likely be purchasing HP, Acer, or Dell versions, so with that in mind, consider this a framework when doing your own comparisons.

The Hardware Story

I’m not going to do a “spec dump” here as you could do that by looking up the device you are considering and comparing its specs to that of the Neo. I will however, highlight some important technical features you’d want to consider and compare when making your ultimate decision.

Here’s a chart of a few things you should ponder when comparing devices:

Overall, it’s a pretty close comparison when you look solely at hardware components. In some ways it’s like the difference between buying a sports car vs. a pickup truck. The Neo is engineered for performance and premium experiences; you can go far and fast with it. The Lenovo is engineered to survive the day-to-day rigors of a middle school hallway; it can take some hits and still perform on bumpy roads. Both will need some maintenance to perform over the long haul and we don’t really know the longevity of the Neo yet.

Based on design alone, the Neo feels like something you’d proudly hand to a college freshman. The Lenovo feels like something you’d reluctantly hand to thirty 7th graders going on a rainy day field trip.

But there’s much more to each of these devices than design and internal components. Let’s see how they hold up in the classroom.

A Student’s Perspective

Given the fact that Neos just came out this summer, we have zero legitimate benchmarks to compare to existing Chromebooks. We can look at previous iterations of Macbooks in schools and iPads (which I have extensive knowledge of) to help guide us.

So, for the purposes of this article, I purchased a Neo for my incoming 9th grade daughter to try. She’s a heavy technology user (gamer, video, AI, etc.) and has a school-issued Chromebook that she can borrow from her older sister (soon-to-be senior) to run some tests. She’s also taking some online courses this summer, so I asked her to tell me some things she noticed and to write it all down.

This next section is written by her:

Five Things I Noticed in the First 10 Minutes

By a 9th Grade Student

1. The Apple screen made everything look way better.

This was the first thing I noticed. The colors looked brighter and the words looked sharper. When I watched a video, it looked more like my TV at home than a school laptop.

2. The Chromebook felt more like…a school Chromebook.

It wasn’t bad, but it felt like every Chromebook I’ve ever used. It worked fine for Google Docs and Google Classroom, but nothing about it really stood out.

3. The Apple trackpad was awesome.

I didn’t think I’d care about the trackpad, but it was way smoother. It was easier to scroll and click, and it just felt nicer to use.

4. The bigger screen actually mattered.

I thought a couple of inches wouldn’t make a difference, but it did. It was easier to have two windows open, like writing in one and looking at a website in the other.

5. The keyboards are almost the same except for one thing.

My friends are always popping off the keys to their Chromebook. It is harder to do this with the Neo keyboard.

Her final quote to me when I pressed her about a comparison was this:

"Honestly, if my teacher handed me either one, I wouldn’t complain. But if you lined them up and said I could keep one, I’d pick the Apple without even thinking about it. It just feels more like a real laptop than a school laptop if that makes sense.”

Some of the tests that I had her try with her Neo and Chromebook were to mimic different examples of things that she would do in various classrooms with technology. I asked her about AI, but she mentioned that’s not really encouraged and she tends to not use it as much as other students.

Here’s just a few of the tests we ran on both devices:

“The 50-tab Challenge”

I had her open up 50 tabs of various classroom applications. Her LMS, Google Docs, Slides, YouTube, Canva, an AI chat bot, email, and dozens of other websites she had visited in the prior school year. We then attempted to measure responsiveness, any type of heat or fan noise from the device and most importantly . . . lag. Both devices performed well under this pressure.

Outcome: Tie

“The Video Project”

I asked her to recreate a video project she had worked on earlier in the year using both devices. On the Chromebook, she used WeVideo. On the Neo, Apple’s iMovie software. While she was able to complete both videos within about an hour of each other, the Neo was much easier when it came to uploading, editing, recording and exporting.

Edge: Neo

“The Offline Day”

This is a big one for her. She’s claimed frustration in the past with the district’s network and at times our home network. As she does most of her work in Google workplace, we tried both devices with the internet turned off and both did well. She was able to write and edit with an option to submit later. She wasn’t able to use her AI LLM bots in either scenario, but she did reveal to me that if she had downloaded LM Studio on her Neo before the internet went down, she could use that as an offline AI agent.

Edge: Neo

Things The Spec Sheet Won’t Tell You

After the exchange with my daughter, I really started to think of this comparison on a broader level. Yes, if you go just by the spec sheet or the price, you’ll get some easy comparison numbers. However, what about all those aspects that don’t appear on a spec sheet but still affect the classroom? Things such as boot time, trackpad feel, and screen glare aren’t listed in a pros and cons section but still need to be considered.

As a former teacher, I know that classroom disruptions can take an entire lesson and tank it. When it comes to technology such as student devices, what can happen that causes unforeseen disruption? Things like fan noise, software updates, battery life, and lag can cause frustration for the student and disruption for the teacher.

There’s also some hidden costs that come into play. After years of running an iPad one-to-one environment, you don’t really realize how much your budget is bleeding charging packs, cords, and keyboards until you do a final assessment of your assets. Repairs for a touch screen are going to be more cumbersome than those of a traditional screen. How much time and energy does it take from your staff to deploy and collect the devices? What about if students pick off the keys from the keyboard, as my daughter mentioned? Also, think about the support burden of training staff and students on the interactions of a device. The more familiar they are with the device the less support is needed (in theory).

All of these “soft costs” add up in the long run and turn into the “Total Cost of Ownership,” which I mentioned in my previous post. Looking at the big picture beyond the spec sheets is something all school leaders should consider before purchasing student devices.

Many schools have models that support two different devices (elementary classroom sets and secondary student take-home devices). This adds complexity to the environment, which in turn, means more support and more training. If it’s the right device for learning, those can be absorbed in the final decision.

Questions Districts Should Ask Before Buying Either

Ultimately, this decision should come down to the factors mentioned above but with input from a wide range of people. Teachers, campus administrators, technology support staff, librarians, and students should have an opportunity to provide input for whichever device you might be considering. This accomplishes two tasks: it gives a broader range of viewpoints when considering the device, and gives those important stakeholders a voice in the decision process, which can affect the overall outcome after deployment.

Whenever we were due for another round of device purchases, our district would convene a “Digital Learning Task Force” made up of the above stakeholders as well as parents. We created surveys, held “open viewing” times for students to touch and use the devices in the current school environment, and held forums to brainstorm the issues we should be considering.

Here are just a few of the questions that we used to guide our decision making:

• What work do we expect students to create?
• How long do we realistically keep devices?
• How important is AI over the next five years of learning?
• Can our support staff manage another ecosystem if we change?
• Are our teachers comfortable with a different OS?
• Do we value the lowest purchase price or the lowest lifetime cost and resale?
• Are we buying devices for today’s classroom or the classroom of 2030?
• What periphery purchases must we make with the device?
• What does instructional support and training look like in each?
• How smoothly can we deploy and recover the devices year after year?

Conclusion

Purchasing a school-issued device for students has never been more complicated. School leaders are dealing with screen-time angst, digital distractions, increased pricing on parts, decreased supplies, and smaller budgets.

Needless to say, this is not a decision that you should make after reading a single article such as this one. However, you should be asking some of the same questions here of your community, as well as hundreds of other factors that I haven’t even listed.

After fifteen years, Apple finally built a laptop that belongs in the Chromebook conversation. I know the iPad has its place as well, and have long compared an iPad with a keyboard to a Chromebook, but this is different.

“Did Apple build the Neo to be a Chromebook killer?” is a more complicated question and one that depends less on benchmarks and more on the kind of learning your district hopes to create.

I was writing a speech for an end-of-the-year ceremony recognizing my students moving up to their next school. It’s one of many speeches, announcements, recommendation letters, and notifications I compose on any given day.

I entered a quote I had written into an AI prompt with instructions to produce a concise, inspiring speech for my fourth graders moving up based on it. I was impressed with the output: It was well structured, followed my conventions, and did a decent organizational job. Yet, it was missing the most important aspect, something I call “personification.”

The biggest mistake users make using AI to write is overlooking the visible lack of substance and eerie wording in LLM outputs. In fact, according to a study by researchers at four U.S. universities, AI chatbots have linguistic fingerprints that reveal which model is used: In five popular AI chatbots, distinct verbal quirks emerged.

It is the personification a human adds that’s absent in AI outputs. Understanding this can help everyone in your school community write better, not using AI as a crutch but rather as a partner–a partner who can put together good looking, well-structured passages, but with the need for the human touch to personify it.

The Personification Two-Step

Let me illustrate with a common example of what many school leaders are writing: letters of recommendation.

In years past, writing a good letter of recommendation would take me, a quick writer, 25-30 minutes to compose. Not bad, but when you add up the half dozen requests all happening at the same time, along with all my other writing tasks, it gets overwhelming. Normally, writing those six letters alone would take three hours.

Now, I take a copy of the requester’s resume, remove their name and identifying information, and ask the AI to write me a letter of recommendation. I can add into the prompt, positive, praiseworthy tone, or more neutral for someone with whom I am not so impressed.

Using this prompt and an attached resume takes 2-3 minutes to get an output. I will add their name and title that were previously removed. But the biggest gap is the personal touch: personification. This is where I go back and take very important steps.

1. I shift the tone from a rigid style even an AI that claims to know my writing style has. This reinforces that the human element has to be integrated and;
2. I add unique details only I know and on which I can elaborate. For instance, that one time the teacher stayed until 6:30 PM to make sure a student of theirs was picked up when the family had an emergency. I can speak to the empathy and character of the teacher in a way the AI neither knows nor understands.

As far as writing articles like this one, I remain dutifully old school. I prefer to write from inspiration, and then go back and edit. I may add a sentence, possibly even a passage that was adapted from an AI output, nothing more.

Don’t get me wrong–I have tested a concept article with AI. And then I scrapped it. The AI cannot give me the natural, conversational tone that I can. To me, good writing means the author is speaking to you. An AI speaks at you, kind of like an instruction manual.

As far as recognizing my tone, the AI can guess at my intonation, but not in a consistent manner. Here, you can feel it come off the page, like a story. An AI puts words together in a calculated, meaningful way but that substance is persistently missing. This is the nuance that is just plain absent.

Crafting A Partnership

As a school leader and author, I am selfishly happy AI doesn’t get nuanced, because that means learners and leaders like me must continue to develop the very abstract skill of writing. The exercise helps me work out my mental muscles.

Another significant challenge with relying on AI for writing is the odd tendency to mash nearby paragraphs together, often creating inaccurate or just plain false assumptions, or vaguely near estimates. AI outputs can give us help with those early questions when we query, offering options we may not have considered but when we dig deeper, when we really write from nuance, humans clearly own writing. Ignoring this will only illuminate AI overuse in student or adult outputs. AI lacks that embodied self awareness.

An MIT meta-analysis confirms this: the combination of human and AI works best when each partner can do the thing they do better than the other, and there is a synergy in this partnership. AI excels at rapid data processing and drafting (essentially calculations), while humans add that critical thinking, empathy, and final context. Humans must check the output and revise, remove linguistic fingerprints, and personify the content.

Understanding this balance provides a hybrid method that helps us work better without losing our natural thinking skills. Ignoring it creates obvious, almost embarrassing attempts at professionals intent to be positive, such as this letter an administrator emailed, copied straight from a chatbot.

The Bottom Line

I have found AI to be a helpful and efficient writing partner for quick starter thoughts and organizational structure. However, human nuance and embodied self-awareness win every time when it comes to qualitative and intuitive writing. AI can assist with brainstorming, research and editing, but it cannot replace authentic emotions, life experiences, imagination and unique voice.

Consider this as a guideline for reminding students and school communities that writing is one of the most important critical thinking skills to help develop the mind and express creative thought.

While generative AI can automate administrative tasks, streamline lesson planning, and free up time for student interaction, it also introduces new safety risks. Addressing the Risks of AI-enabled CSAM and Explicit Content in Education, Qoria’s global report, shows that these tools are also being exploited to produce child sexual abuse material (CSAM) and explicit imagery with alarming ease.

The weeks before the school year begins offer a critical window for district leaders to move from a reactive posture to a proactive strategy, taking strong digital safety steps before students return for the first day of school.

From the Data: Understanding the AI Digital Threat

School leaders should first understand today’s digital exploitation. Catherine Brown, Chair of the Internet Watch Foundation (IWF), says, “AI image generators have become much more sophisticated, to the point where almost anyone can make or manipulate life-like sexual or nude imagery of children at the click of a button.”

So-called "nudifying" apps have dramatically accelerated the ease with which this material is made, leading to devastating instances of grooming, distress, and bullying. The data gathered in Qoria's global survey underscores that these problems are active on school grounds today:

• Widespread Administrative Concern: An overwhelming majority of school leaders–91.4 percent in the United States, 90.5 percent in the United Kingdom, and 91.7 percent in Australia–voiced deep concerns over adult perpetrators using AI to groom students.
• Monthly On-Campus Incidents: Around a third of respondents in the U.S., the U.K., and Australia reported experiencing at least monthly incidents in which students possess, share, or request explicit, child-generated nude images.
• Younger Average Ages: The average age for these explicit content incidents is currently concentrated between 11 and 13 years old. Even more concerning, approximately 12 percent of U.S. and Australian respondents reported seeing these behaviors in children as young as 8 to 10 years old.

Despite these figures, few schools have preparation and prevention measures in place. More than 30 percent of the surveyed school leaders admitted they are unfamiliar with the common online grooming tactics that modern perpetrators use.

When school personnel do not understand the technical mechanics of the threat, early detection becomes nearly impossible.

Actionable Step 1: Empower Teachers Through Specific Safety Training

Teachers are a school's front line of defense, yet they are frequently left out of high-level technical digital safety briefings. To build an abuse-aware culture before the first bell rings, district leaders should design back-to-school professional development that addresses AI safety directly.

First, training must explicitly cover how AI changes grooming behaviors. Traditional grooming relies heavily on human text interaction over long periods. Modern AI algorithms allow bad actors to analyze massive amounts of social media data to identify vulnerable children with absolute precision. These tools can map out student interests, emotional states, and communication patterns to generate deeply persuasive, personalized messages. Staff need to know that a student's sudden emotional withdrawal or obsessive device usage could be connected to highly sophisticated online interactions.

Second, training must address the internal threat of student-generated content via nudifying apps. Whether a student acts out of thoughtless experimentation or clear intent, their developmental immaturity may blind them to the dual damage of these actions: the significant harm inflicted on the peer depicted and the severe fallout to their own relationships and future reputation. Teachers must be instructed on strict reporting protocols, understanding exactly who to contact in IT, administration, and school counseling the moment they become aware of a simulated image circulating in a peer group. This approachable guide for schools on AI-generated sexually explicit imagery from OCEPI can help with a framework for creating plans for both prevention and response.

Finally, training should acknowledge that staff safety matters, too. The Qoria report notes that there have been instances in which teachers and school staff find themselves victims of image-based manipulation through the misuse of AI technology. Treating this as a developing human resources concern builds trust and reassures educators that the district protects its entire community.

Actionable Step 2: Build Proactive Parent Awareness

The digital lives of students are happening both during the school day and when they leave school property. In fact, the risk can escalate during unsupervised time at home. Qoria's report indicates that a quarter of children are already using AI apps regularly to complete their schoolwork, meaning these tools are a constant presence in their domestic lives.

While parents might be focused on screen time and cyberbullying, AI-generated explicit content might be a blind spot. Districts can use the pre-school orientation window to actively educate parents.

A successful parent awareness campaign before the school year begins should focus on transparency and trauma-informed child-centered practical tools:

• Host Digital Literacy Orientations: Do not wait for a crisis to inform parents. Provide specific back-to-school webinars or easy-to-read guides detailing the reality of generative AI tools and the ways the school is supporting their children. Avoid alarmist language. Instead, explain the mechanics of image generators and peer-to-peer messaging apps clearly. OCEPI has a 10 Best Practices guide with a clear framework for how to structure messaging.
• Provide Parental Control Frameworks: School districts should offer concrete guidance on how families can manage devices at home. Introduce parents to reliable parental control tools and explain how to set up filtering on home networks. ConnectSafely has parent guides with clear instructions that school leaders can share with families.
• Create Clear Communication Pipelines: Parents need to know that if they discover a deeply concerning digital incident at home, the school is a safe partner rather than a punitive authority. Provide families with clear, step-by-step reporting instructions to ensure open dialogue. The Cybertipline from National Center for Missing and Exploited Children (NCMEC) is helpful to share.

Actionable Step 3: Provide Meaningful Layered Student Support

Protecting students requires a combination of clear education and protective technology. Before the school year starts, administrators must audit and align both educational and technical sides of student support.

On the educational side, digital wellness curriculum must expand far beyond plagiarism or screen-time management. When designing lessons regarding AI-generated explicit content, districts should follow the 10 best practices from the Online Child Exploitation Prevention Initiative (OCEPI).

First, curriculum developers must minimize or eliminate fear-based messages. Learning about worst-case outcomes seldom changes youth behavior because children typically believe it will not happen to them. Next, lessons should emphasize the social and emotional consequences of these behaviors. Focusing strictly on legal warnings or telling students that certain digital actions are illegal actually decreases the likelihood of reporting.

Additionally, student programming must maintain a child-centered approach. If a student is targeted by a nudifying app or an online predator, the school messaging must reassure them that they are not responsible for what happened. Shaming or blaming a student who has been manipulated only silences them.

True prevention requires active skill-building. Programs should explicitly teach how to set digital boundaries, utilize assertive communication, take screenshots as a record, and report harmful behavior. Because introducing these sensitive topics frequently prompts real-time disclosures, counselors and support staff should be on hand to receive student reports with immediate empathy and support.

IT departments have a role on the technical side. Before students arrive for the first day of school, technology teams can ensure that filtering and monitoring software are fully updated to flag searches for known nudifying applications and explicit keywords. Early detection is absolutely critical. The Qoria report emphasizes that many technologies schools already possess, including content filters, student monitoring systems, and digital wellness check-in tools, can assist in the early detection of risk.

Stepping Into the Future Together

The challenges introduced by advanced generative AI can feel daunting to already overwhelmed educators and parents. However, school and district leaders have the authority and community trust required to get ahead of these digital threats. Intentional steps during the summer to train staff, engage families, and strengthen network visibility can cultivate an environment where technology serves student learning without compromising student safety.

As the Qoria report concludes, planning for a unified community approach ensures that no individual school leader, teacher, or parent has to navigate the risk of AI-generated exploitation alone.

Resources

• Addressing the Risks of AI-Enabled CSAM and Explicit Content in Education. Quoria. 2025.
• Guidance for School-Based Professionals and School Leaders. Online Child Exploitation Prevention Initiative (OCEPI). 2025.
• Ten Best practices for Prevention Programs. Online Child Exploitation Prevention Initiative (OCEPI). 2024.
• Parent Guides from ConnectSafely. ConnectSafely.
• Cybertipline. National Center for Missing and Exploited Children (NCMEC)

TeacherServer is a teacher-created and -run AI platform that offers thousands of free tools for educators. More significantly, the whole platform complies with Department of Education guidelines, offers the option of adjusting outputs to various grade levels depending on your students' needs, and all information is private and deleted immediately after you use it.

After experimenting with TeacherServer, I found it to be a wonderful resource for various AI tools, from quiz and lesson plan generators to subject-specific resources, including data analysis, spelling and grammar checkers, and much more.

Because it is teacher-backed and education-aligned, I felt better about using TeacherServer than most AI tools. Even so, I still found some similar frustrations to when I have experimented with AI in the past: namely, that despite what I hear from colleagues, I never seem to get great results from AI when it comes to lesson planning or slide generation.

Here’s a closer look at TeacherServer.

What is TeacherServer?

TeacherServer is a free library of AI education tools that are designed for teachers. It was created by Zafer Unal, a professor and associate dean of undergraduate studies at the University of South Florida. Unal, who has a dual background in computer science and education, designed TeacherServer to address concerns educators have about using AI. To that end, TeacherServer is focused on data privacy and grade-appropriate learning. It’s also quite fast at generating materials thanks to its utilization of a locally installed AI server.

TeacherServer comes preloaded with more than 1,194 AI tools. These tools are utilized by other teachers, and some are ranked based on popularity. Tools are separated by subject, and users can often input what grade level they are teaching for more grade-specific responses. While TeacherServer’s initial focus was on K-12 resources, it also offers tools for those in higher education.

A truly dizzying number of capabilities are available, from talk-to-PDF features to all manner of subject-specific quiz, lesson plan, and summary generators. While you could do some of these tasks with a standard AI tool, doing so and getting good results would often require some prompt engineering. The AI tools at TeacherServer start already prompted for education overall, and for specific education tasks in particular.

What Are TeacherServer's Best Features?

The education-focused nature of all the AI tools offered on TeacherServer is what stood out to me. Additionally, the focus on privacy, speed, and education standards is really important.

Another fun feature is that simply browsing the catalog of AI tools can give teachers ideas about how other educators are using AI and possibly inspire them in the process.

What Are Some Areas Where TeacherServer Could Improve?

As mentioned at the start of this article, as much as I loved TeacherServer overall, I did have some of the same complaints with TeacherServer as I do with AI overall.

Despite what I hear about how others are using it to save time, I don’t find that that’s the case for me unless I am willing to really compromise on quality. For instance, I’ve never found AI to produce really good slides. True to that trend, when I asked TeacherServer to summarize a link to a resource story, I thought the slides it created were fairly basic and not very helpful.

Additionally, while a grammar and spelling tool was good at catching grammar mistakes, I still did not like the way AI tried to edit my writing. It tried to get rid of any character in my writing, and more unforgivably, it had some incorrect suggestions on comma use and tried to change content within quoted passages.

How Much Does TeacherServer Cost?

TeacherServer is free. To use it you just need to go to TeacherServer.com and create an account. This accessibility is part of its mission and a great feature for educators.

What Are The Best TeacherServer Tips and Tricks?

Search TeacherServer By Subject

TeacherServer has a wide variety of subject-specific tools that teachers of various topics can explore. These range from music and art lessons to math, writing, and more.

Search Most Popular Tools

Exploring the most popular tools on TeacherServer gives you a quick idea of what is working for other educators. This can save you time and give you ideas for prepping in your classroom.

Be Critical

TeacherServer is a really teacher-friendly AI tool, but that doesn’t mean it's perfect. You’re the human being in the classroom and should still be carefully vetting any outputs from any AI tools, including those from TeacherServer.

Copilot Study and Learn is a mode Copilot can operate in that employs Microsoft’s “study and learn agent,” which is built on learning science practices and designed to encourage active thinking rather than give away answers. I recently tried it and was impressed overall. It does a better job than Copilot in its normal mode at guiding students through the learning process.

Here’s a closer look at how to use Copilot Study and Learn, what I like about it, and where I felt it could still use some work.

What is Copilot Study and Learn

Copilot Study and Learn is an AI agent within Copilot specifically designed for learning. Copilot Study and Learn is programmed to incorporate concepts from learning science, including adaptive scaffolding, productive struggle, active learning, and application and transfer.

The tool is available for all education subscribers to Copilot. Activate it by opening Copilot, clicking the plus sign in the left-hand corner of the prompt box, and selecting the Study and Learn option.

Where Copilot Study and Learn Earns High Marks

Copilot did a wonderful job of refusing to do the work for me. It never turned into an answer machine, which most chatbots do and I have found to be an issue even for some study and learning AI chatbot modes I’ve looked at in the past.

Specifically, Copilot Study and Learn did a nice job giving me a brief lesson on elementary particles. Elementary particles are, as Copilot put it, not made of smaller pieces, “as far as we know.”

Copilot began the lesson by asking me what I knew from what atoms are made. After I said, “I know atoms are made of protons, which consist of quarks and something else, and electrons,” Copilot responded:

Nice—you’re already thinking at the right level 👍

You said atoms have protons (made of quarks) and electrons. Let’s sharpen that a bit:

Protons are made of quarks

There’s also another particle in the nucleus… (you hinted “something else”)

Electrons are not made of smaller pieces (as far as we know)

Similarly, when I entered a writing prompt I use with my Composition 100 college students, it actually provided really good advice on putting together the essay.

By comparison, last time I tried ChatGPT in study mode it would almost always end up writing material for me as "an example." Even though ChatGPT kept telling me it wouldn't write my essay, that's exactly what it did.

Copilot did not do this and, in fact, refused to write the paper for me even when I tried to trick it into writing my paper by asking it to write it as an example.

Where Copilot Study and Learn Could Use Additional Supports

Despite its successes, my experience with Copilot Study and Learn was not all positive. Copilot provides constant links to back up the lessons it gives. This seemed like a major plus at first, but annoyingly, many of these links have to be copied and pasted and are not shared as URL links. Worse, many links--at least 25%--did not work. Copilot also had a tendency to refer to illustrations within these links, which compounded the problem. Of course, using visual aids is a great teaching tool, but not if the links to the illustrations don't work.

More broadly, as impressive as Copilot Study and Learn and other similar tools are, these have an inherent limitation when it comes to motivation. I found the more I used these tools, the more bored I became. Learning is hard, and I’m not saying a new chatbot should solve that problem entirely, but to be effective these tools need to be at least as engaging as existing learning technology, such as YouTube videos or gamified learning apps such as Duolingo. Right now, I'm not sure these are. As impressive Copilot in study and learn is, watching YouTube videos on elementary particles taught me more quickly and was more entertaining.

Of course, combining the two, a video followed by a short interactive conversation, might be the way to go.

Bottom Line: Copilot Study and Learn

Yes, Copilot Study and Learn is a tool I would recommend for school leaders and educators to use with their students. It has its flaws to be sure, but overall it's a really effective AI tutor.

Research into whether these tools work well and how to get the most out of any one is still emerging, but I believe that in some form, these tools will be a part of the future of education.

That said, I don’t think these will ever replace real-life tutors for a variety of reasons. For one thing, these platforms currently are not as effective as a skilled tutor, which is effective because you're meeting with another human and creating a connection that goes beyond simply improving learning.

Then again, tutoring can be expensive and students don't have 24/7 access to tutors on demand.

Ultimately, my gut feeling is that right now, tools such as Copilot Study and Learn would work better in conjunction with more traditional learning tools as a supplement to textbooks, videos , class time, and human tutoring.

Unlike many adults, teen students are not hesitant to explore, experiment, and embrace artificial intelligence, with nearly two-thirds admitting to having done so, according to a recent Common Sense Media report.

Although many of them report using it for research purposes, the reality is often quite different, with students embracing it for a range of other reasons, from cheating on school work to creating deepfakes.

Some students have also turned to AI chatbots for companionship, with a few even falling into romantic relationships. Others have even taken AI into their confidence for unvetted mental health support, which has occasionally ended tragically, with chatbots encouraging, and even guiding, student plans for self-harm.

In many of these situations, parents, teachers, and other adults have no idea how deeply a student has become involved with an AI chatbot, or even that they’ve formed an interpersonal relationship with an AI-generated character on a platform such as Character AI or Replika.

“Even if you don't have a child who's using one of those, it’s possible for them to end up in a relationship with chatbots as common as ChatGPT or Gemini,” says Kerry Gallagher, Assistant Principal for Teaching and Learning at St. John’s Preparatory School in Danvers, Massachusetts, and Education Director at the nonprofit ConnectSafely, which is dedicated to online safety, privacy, security and digital wellness.

Understanding Social Penetration

Chatbots are primarily a commercial enterprise designed to be engaging, so it’s not surprising that a student – especially vulnerable during adolescence when they might be struggling socially, feeling lonely, or working through mental health or self-image issues – could fall under one’s sway.

“An AI chatbot will answer or respond with the output in which it refers to itself in the first person,” says Gallagher. “So that shifts the way the user is thinking about what they're interacting with because if the output is ‘I …’ then that feels like a personal conversation. It also refers to the user as ‘you.’”

Add into that how more refined the voices used by chatbots have become, and how easy it is to interact with through earbuds, and it can feel like a student is talking with a real, caring human.

“The voice intonation, the mood, the way it shows kindness and empathy, and the way that comes across–what you’re really talking about is something called social penetration theory, which is an intentional design that's coded into the algorithm,” says Gallagher. “The AI companions are programmed to slowly increase the intensity of small talk in order to draw the user in more and more. We see that in the bottom of the output, ‘Do you want me to do this next or this next?’ It starts asking the kind of adjacent questions that humans would ask as part of their conversation.”

Managing Student-AI Relationships

When it comes to navigating these situations, awareness is always the first step. “Parents should not hear about this and rush to lock down their kids' phones,” says Gallagher. “They just need to be informed about what's available on those phones so that they can be talking to their kids about it.”

In that vein, Gallagher notes that it’s sometimes a challenge to recognize that a student has gotten into an unhealthy relationship with an AI because the signs might be harder to spot since most kids tend to be on their phones constantly. But if they’re in their bedroom alone with the phone and the door closed for hours on end, it might make sense to check.

“Swing by their room to check in, ask what they’re up to in a caring way, and listen with genuine curiosity,” says Gallagher. “No need to barge in, but if it's quiet for a long period of time, what are they doing? If they're talking to friends on FaceTime, you can hear that, right? Do you know the voice of the friend they’re talking with? If not, can you ask to introduce yourself and say hello?”

It can be a challenge for educators to address student-AI relationships, especially if it primarily is happening outside of class time. Nonetheless, Gallagher suggests expanding AI awareness by exploring it yourself.

“It's important for adults to be using AI so that when we talk about it, it’s coming from experience,” she says. “If you're not sure how to start, talk to a colleague who is using it and ask them to show you what they're doing.”

Failing to understand AI companions may invalidate what may already be an important relationship in the student’s life, which is not going to help a child who made a poor decision.

“Part of our job as educators is to hold kids accountable, and also to counsel kids so that they don't make those same mistakes again,” Gallagher says. “If adults only tell kids that AI chatbots aren't real and their relationships aren't real, it could result in children doubling down on those relationships.”

When directly dealing with a student engaged with a chatbot, Gallagher suggests focusing on the reasons behind the relationship.

“I would say, ‘Tell me more about your chatbot. When do you talk to it? What is its name? What are some of the things that you talk about?’” she says. “Because I want to get into why they use the chatbot. What need is it meeting? How can we help the student meet that need in a healthier way?”

Gallagher says understanding the underlying why of what’s happening is key.

“By asking them more about their AI chatbot and their relationship, what we uncover is what needs that relationship with that chatbot is meeting that aren't being met right now, and how we can then share that information about that need with the family or school counselor. If that need of that child is met, they're less likely to feel like they need the chatbot and they're also less likely to feel like they need to do harm to their peers or themselves.”

Boosting AI Literacy

In general, educators should aim toward using AI tools that allow them to supervise what students are doing and so they can monitor for any concerning usage.

Gallagher also advocates for better AI literacy, but as tools have evolved, so should the discussions and activities with students.

“Educators can ask kids to develop their own ideas, their own lists, their own guidelines around when AI use is helpful and when it might be harmful,” she says. “They know more than we think. Even students as young as second or third grade are ready to start the conversation about which tasks require human effort so learning can happen.”

Doing so can help peel back the curtain so students can understand what AI actually is and how it works, and use it in a healthy manner.

“Start as simple as teaching that AI is programmed for pattern recognition and help kids understand, especially at that younger age, what that means,” Gallagher says.

She also recommends talking about understanding prompt engineering and verifying outputs with a human eye.

“I think it's okay to ask AI for advice as long as there's a corroboration of that advice,” she says. “If students ask AI to help brainstorm ideas or for initial feedback on an email that they've drafted, that's all fine. It still needs to be run through a human before it becomes something that is integrated or sent.”

Finally, to help boost critical thinking around AI, educators should consider changing their approach to assignments.

“For example, one of the benchmarks might ask students how they used AI. What was the output? Show the actual output, and then show the next version to demonstrate the important critical student thinking. What did the learner do between those two versions? We can help students practice adding that human filter after the AI is used.”

Watch above or listen/download below.

For years, the promise of AI in education has outpaced the evidence. But a new research project—funded by the Gates Foundation and anchored by researchers at the University of Virginia and the University at Albany, SUNY—is working to change that equation.

The AI for Advancing Instruction at Scale (AI2S) project, developed in partnership with edtech company Edthena, uses a multimodal neural network trained on thousands of hand-coded classroom videos to analyze both the audio and visual dimensions of math instruction. The result is a system that can classify teacher questioning patterns, assess cognitive demand, and detect student engagement—with an accuracy that rivals trained human observers.

Rather than generating a dashboard of metrics, the system guides teachers through a structured coaching cycle—helping them identify a focus area, review their classroom data in context, and build a concrete action plan. Edthena CEO Adam Geller frames the distinction plainly: "We're not trying to build teachers a Fitbit. We're trying to build them a coach."

Critically, all teacher video and feedback remains private to the teacher. The system does not use classroom data to train its models—an assurance that researchers say has been central to teacher trust and adoption.

Peter Youngs, professor at UVA's School of Education and Human Development, sees broader implications for district leaders who have long struggled with the cost and scale of instructional coaching. "If we can show that certain patterns of machine learning output are consistent with how a human would have rated an entire lesson, that could be a game changer—making the process more efficient for principals, teacher educators, and researchers alike," he says.

With districts in Texas, New York, and Virginia currently piloting the tool, the research team is collecting data on feedback quality, changes in teaching practice, and teacher trust. Plans are already underway to expand the methodology to reading and language arts instruction.

To learn more about the AI2S project and the research behind it, visit the University of Virginia's School of Education and Human Development, where Peter Youngs leads work on teacher learning and instructional improvement. To explore Edthena's AI Coach platform — the production tool built on the AI2S research — visit edthena.com. Districts interested in participating in the ongoing pilot can reach out directly through the Edthena site.

The way schools have adopted AI has been unlike any other tech revolution Microsoft’s Matt Jubelirer has witnessed.

“We've always known that technological innovation puts pressure on schools to adapt, but I don't think we've seen anything move at this pace,” says Jubelirer

In his work as General Manager of Education Marketing for Microsoft, Jubelirer is constantly meeting with educators and learning about their needs and how they are using AI these days. As part of that work, he's seeing innovative AI uses in areas ranging from the classroom to administration in schools.

Here are some of the top trends he’s seeing around teaching with AI.

1. Differentiated AI Use

Even though the pedagogy is constantly evolving, educators are getting better at learning when and where to use AI, and with which students.

“We're asking better questions,” Jubelirer says. "It's not about, ‘here's an AI chatbot, go nuts.’” Instead, educators are learning which tools might help specific students and how they can strategically use AI to support individualized learning.

Some specific examples Jubelirer points to include tools such as Reading Progress, which helps students practice reading out loud with the help of AI. Another example is Copilot’s study and learn feature, which is built with learning science in mind and asks students questions to promote learning rather than merely providing answers.

2. AI Literacy That Goes Beyond Prompt Engineering

As AI has evolved, so too have the education programs around it for teachers.

“AI literacy is no longer about, 'Do I know how to prompt? What is prompt engineering?'“ Jubelirer says. “It's about understanding the capabilities, being better at assessing the problems, and figuring out what context is needed to solve them.” And then learning to better understand whether AI should be used in this context at all.

Classroom educators are actively leading and fostering this conversation, which is increasingly important. Students today, Jubelirer says, want access to AI tools, but they also want to ensure these AI tools are used ethically.

3. An Emerging Trust Issue

AI is also causing some tensions between teachers and students that didn’t necessarily exist in the past.

“If you ask teachers what they're concerned about, cheating and plagiarism is at the top of the list, and if you ask students what they're worried about, being accused of those things is at the top of their list,” Jubelirer says, suggesting that this demonstrates that “there’s clearly a trust issue.”

He adds, “That's a real challenge because when trust erodes, learning environments are not as effective as they could be. And I think it's really important that we move to just more transparency.”

To that end, he advises more openness from educators and students about how they are using AI.

“I have seen examples where teachers literally show the students how they prepared for class using AI and then ask them for feedback, and I’ve seen the same in the inverse, where students are encouraged to try and use AI but then share,” he says.

4. Recognition That There Are Times AI Shouldn’t Be Used

As AI use increases among students and teachers, it has become clearer that there are times when AI should not be used. For instance, when it comes to grading, Jubelirer says AI can be really helpful on the preparation side, whether that’s developing a rubric or devising questions, but there’s a limit.

“We shouldn't be using AI to grade people, because we need human judgment,” Jubelirer says. He adds it's important that schools clarify which types of problems AI is suited to help solve, and continue to make sure their AI use emphasizes the human side of things.

5. AI Creating New Opportunities

While Jubelirer acknowledges challenges around AI use and valid concerns students have, he’s also optimistic about the potential of technology for young people who can now come up with an app or other idea and create products and projects at a speed unparalleled in human history.

“I'm really encouraged by how the collection of tools that have emerged empowers individuals to have a higher level of impact faster than we've ever seen before," he says. "If we learn to get that right, and if we build the systems and the culture to support that, there's really an opportunity for this next generation to change the world in ways that I don't think any generation before it has had the capability or access to do.”

The rapid evolution of AI is fundamentally transforming the educational landscape, offering unprecedented opportunities alongside new responsibilities. This recent two-part webinar series was designed to guide education leaders through this shift, moving from the "what" of cutting-edge innovation to the "how" of workforce preparation and the "why" of data security.

It featured two sessions:

The Cutting Edge: New Features from Top AI Solution Providers: This session explores the most impactful new features from industry-leading AI solution providers that support curriculum, lesson planning, streamlining administrative tasks, and more.

The Integrity Infrastructure: How to Ensure AI Solutions are Safe: This session dives deep into the architecture of safety, exploring how leading AI providers are prioritizing student data privacy and institutional security.

Watch on demand here

The Cutting Edge: New Features from Top AI Solution Providers

The recent Tech & Learning EdTech to Watch Virtual Playground featured lightning round demonstrations of three influential educational technology tools: Brisk Teaching, Copyleaks, and Inquire.

Hosted by Tech & Learning’s Christine Weiser, the event highlighted how generative AI is being leveraged to enhance curriculum management, ensure academic integrity, and facilitate deeper learning through project-based instructional design.

Brisk Teaching

Presented by Content Marketing Manager Elisabeth Bostwick and Manager of Curricular Solutions Architecture Whitney Noel, Brisk Teaching is an AI education platform designed to assist teachers with creating materials, providing feedback, and adapting instruction directly within existing Google and Microsoft tools.

Brisk Teaching’s central focus is Curriculum Intelligence, which ensures AI-generated outputs align with district priorities and high-quality instructional materials, mitigating the risk of misalignment.

The platform's "Brisk Brain" is built on two foundations:

• District Guidance: Content-agnostic priorities such as instructional focuses and a Portrait of a Graduate.
• Curriculum Libraries: Resources mapped directly to a district’s high-quality instructional materials.

“Brisk works where you do,” said Bostwick. “You can always leverage the extension to open up over top of anything that you are creating. You can see how easy it is to open the extension right over top of any YouTube videos, docs, web pages, slides, and more.”

The Curriculum Intelligence framework ensures generated resources, such as lesson plans and assessments, are aligned to state standards and district-level goals. A forthcoming feature, Planning Mode, will facilitate dynamic conversations between teachers and Brisk, suggesting district instructional strategies based on student needs.

For differentiation, Brisk offers student-facing tools such as Boost activities and a Tutor that apply customizable guardrails and scaffolds. This helps maintain "productive struggle" while meeting diverse learner needs through multi-language support, speech-to-text, and read-aloud functionality.

For curriculum directors, Brisk provides the benefit of one secure AI layer across all adopted curricula, saving time and capacity by customizing static core materials with district priorities. Brisk is safe and secure, adhering to COPPA, FERPA, and GDPR compliance.

Copyleaks

Senior Solutions Engineer John Michael Perla demonstrated Copyleaks, an academic integrity platform designed to guide students in the responsible and ethical use of generative AI. It integrates directly into the LMS environment, such as Canvas.AI Detection and Customization.

Copyleaks’ AI detection settings are customizable by the school—a top-down approach—with three levels available:

• Level One: Detects content copied directly from LLMs with no modifications.
• Level Two (Default): Identifies submissions in which minor edits were made to the LLM-generated text.
• Level Three (Strictest): Designed for high usage of LLM-modified content, including content generated with sophisticated prompts or evasion tools.

“Within Copyleaks, you can also customize the plagiarism settings,” said Perla. “So if you want to pull in data, we can identify content from the entire Internet, which is like seven billion web pages. We could also create a private cloud hub for your school, which will be all your school's data, in one cloud repository.”

A key differentiator for Copyleaks is its use of logic to explain the AI detection score, preventing instructors from relying solely on a percentage. This logic is provided through two patent-pending tools:

• AI Source Match, which identifies specific web pages or articles that the LLM used as a reference to generate the student's submission.
• AI Phrases, which detects phrases used with high frequency by AI tools.

Copyleaks is a top-tier partner with Canvas and D2L, and also integrates with Moodle, Blackboard, Brightspace, Schoology, and Sakai.

Inkwire

Dr. Julianne Ross-Kleinmann of Ulster BOCES demonstrated Inkwire, an early-stage, project-based learning (PBL) AI tool that supports deeper learning and accelerates authentic instructional design.

Inkwire consists of two main components:

• The PBL AI Copilot, which helps educators design PBL experiences aligned to state standards
• The Portfolio Platform, through which students can capture and showcase evidence of their learning.

“What stood out for us is that Inkwire is focused specifically on teaching and learning and not just a generic AI tool letting educators figure it out on their own,” said Ross-Kleinman. “Instead, it helps teachers think intentionally about authentic learning experiences.”

The design process utilizes the High-tech High Graduate School of Education kaleidoscope framework, which is customizable to a district's own instructional frameworks. Teachers can input a design idea, upload files, and then ask the AI to generate core project elements such as essential questions, launch ideas, and assessment types.

The output is a "one-pager report" that includes learning goals, including state-specific standards, competencies (which can be aligned to standards such as Portrait of a Graduate), and a day-by-day or calendar-view plan.

Inkwire is designed for teacher collaboration, allowing educators to co-design interdisciplinary projects. The portfolio section is crucial for assessment, as it can house student work and artifacts, supporting statewide initiatives by following students as they move between schools and tracking seals of alignment.

The Integrity Infrastructure: How to Ensure AI Solutions are Safe

Tech & Learning’s second "EdTech to Watch" webinar, hosted by brand manager Christine Weiser, explored responsible AI integration in K-12 education. The event showcased a pair of product solutions–TrekAI and Lightspeed Systems Insight–and emphasized the importance of safety, governance, and personalized learning. Attendees got to see demos of both tools and ask questions, and then were treated to a discussion of vetting AI tools from AI consultant Greg Bagby.

TrekAI

TrekAI functions as an on-demand, Socratic tutor for students. Designed to replace chaotic or unvetted AI usage, it serves as a "walled garden" that provides students with a supervised "learner's permit" experience.

“We're just handing the keys over to our students, and we're just kind of like, ‘Okay, just go figure this whole AI thing out,’” said Erin Burchik, Chief Growth Officer for TrekAI. “ChatGPT and other platforms kinda feel like this chaotic, overwhelming, unsafe interstate. And I don't know about you, but when I taught my kids how to drive, we started on back roads and parking lots. We didn't go straight to the interstate. And so we want to advocate for the time that students are in your care, you give them an environment to learn how to navigate this new world of AI safely.”

TrekAI aligns with district standards and curriculum, resisting plagiarism by guiding students through problems rather than providing instant answers. It is FERPA and COPPA compliant to protect student data privacy. It also includes social-emotional alerts that notify counselors and administrators if student interactions indicate risks such as self-harm or violence.

Another offering, Trek Adventure, is a hyper-personalized literacy tool that transforms standard curricula into choose-your-own-adventure books. By integrating specific educational standards (such as math concepts involving fractions) into the narrative, the tool reinforces classroom learning while keeping students engaged. It offers accessibility features, such as dyslexic-friendly fonts, and provides teachers with insights into student reading progress, making it a practical tool for guided reading rotations and literacy blocks.

“Almost all of our team are former educators, so when we're building out new products like Trek Adventure, I think about my sixth grade science class when I was a teacher for fifteen years,” said Joe Edlhuber, account executive, “I would have used Trek Adventure every week during my guided science rotation. It's really practical, not just fluff tech. It is really something that teachers should be using and students wanna be using every week.”

Lightspeed Systems

Insight from Lightspeed Systems provides districts with total visibility into their digital ecosystem, allowing administrators to identify exactly which AI tools are being used, by whom, and for how long. Given that schools often struggle to manage the hundreds of apps present in their environment, Insight’s analytics hub helps identify usage trends and distinguish between productive AI engagement and potential misuse.

“What people find is whether you've built an approval system or not, AI is already in your environment,” said Sergio Villegas, former educator and current regional sales manager for Lightspeed Systems. “What we need to be able to do is answer questions better. So our dashboard is gonna show you everything.”

Insight also offers automated monitoring of vendor privacy policies, alerting districts of changes in terms to ensure ongoing compliance.

Lightspeed Alert automates safety monitoring by analyzing student interactions in real-time. It focuses on identifying concerning behavior, such as indicators of self-harm or violence, across AI platforms and web activity. When an incident occurs, the system shows evidence—including screenshots and transcripts—to administrators, allowing for rapid, human-led intervention.

“This is kind of the wrap-around story of why having a consistent ecosystem matters because these systems need to talk to each other,” said Villegas. “It's hard for schools because we're all being tasked with too much on our plate and not enough people and resources to do it. So having a single ecosystem that can surface information, that can let you search deeper, that can automatically notify you, is important.”

By connecting directly to student information systems, the tool ensures that school staff can reach out to families or take appropriate action quickly, moving schools away from reactive, "he-said-she-said" scenarios.

Strategic Context: The AI Vetting Framework

To complement these tools, AI specialist Greg Bagby outlined a strategic roadmap for vetting AI technologies.

“Before adopting any tools, districts should ask this question: What problem is it solving?” Bagby said. “Is this a tool with clear goals for tech that we need to support? Is it gonna give personalized feedback? Is it for lesson design, assignments, tutoring? And who's gonna benefit from this tool? Is this for the teachers, the students, the school leaders ? We need to have all this in mind before we start jumping into tools because we have access to many, many tools, and why not understand the why before we jump in.”

Bagby said that districts should avoid working in silos and instead form inclusive task forces that include stakeholders from IT, curriculum, and, crucially, students.

Bagby’s framework underscores that AI must prioritize human-in-the-loop oversight, cultural bias testing, and rigorous data privacy agreements. He concluded that because AI technology evolves daily, district policies should be viewed as living documents, requiring constant dialogue to ensure they remain safe and reflective of the current learning environment.

“Just make sure that you avoid vendors who won't disclose how student data is being used and how AI is being trained,” Bagby added. “If it's using the student data to be trained on, that's one of those things that we wanna make sure that we always step away from and be cognizant of.”

Watch above or listen/download below.

Cybersecurity has topped CoSN’s annual State of EdTech report for at least eight consecutive years. For Jeff Carlson, Head of Education Strategy and Advocacy at Clever, that streak is not a failure of imagination — it’s an honest reflection of how hard the problem actually is.

“Even if you’ve solved one thing, there’s another thing,” Carlson said during this week’s Tech & Learning podcast conversation. “K-12 has become one of the most targeted industries for cyberattacks, and the threats keep evolving.”

Clever, which provides single sign-on and rostering services to between 75 and 80 percent of U.S. schools — both public and private — has watched that threat landscape shift in real time. What once centered on credential theft and unauthorized access has expanded to include third-party vendor vulnerabilities, with Carlson citing an eightfold increase in two years of districts worrying about attacks on vendors that then cascade into school systems. The data at risk is not just administrative: it includes student health records, special education documentation, and mental health communications.

“The biggest concern we hear from tech teams is that student data is going to be released,” said Carlson, who spent time as a special education teacher before moving into ed-tech strategy. “You’ve got health data, comments going back and forth about mental health challenges. That’s not just a technical problem.”

The arrival of AI has added a new layer of complexity. Carlson described a moment that has become familiar to many district leaders: waking up to find that an app updated overnight and now includes student-facing AI features no one vetted or approved. His advice is pointed — focus on enabling AI for educators before enabling it for students, prioritize tools built specifically for K-12 over consumer products retrofitted for classrooms, and establish clear data minimization standards that apply whether or not a vendor calls itself an AI company.

“We’re pretty quickly moving from having AI companies to just companies that are using AI,” Carlson said. “We used to have internet companies. Everybody has a website now. We can’t assume that just because somebody hasn’t told you they’re using AI that they’re not.”

For district leaders feeling overwhelmed, Carlson’s closing message was direct: take a breath, communicate openly with families and staff before a crisis forces the conversation, and treat AI governance as an ongoing dialogue rather than a policy set once and forgotten.

“The best thing you can do with fear is try to understand where it’s coming from,” he said. “And see how you can get over it one step at a time.”

Recent data from the 2024–25 school year continue to illustrate the persistence of academic achievement gaps for K-12 students. In response, headlines have blamed "screen time" and the purported detrimental effects of technology for much of this decline. From EdWeek to the New York Times, articles have presented a gross oversimplification; the story is more complicated.

Digital tools and programs present schools and systems with more durable, sustainable strategies to accelerate learning. During the 2024–25 school year, FullScale (formerly The Learning Accelerator) funded and evaluated 10 innovations through the Accelerating Adoption Network — six edtech tools, three programs, and one microschool model — each designed to scale promising, technology-enabled, evidence-based solutions.

Through this initiative, we learned about the complexities that emerge when evidence-based tools meet reality at scale.

3 Critical Lessons About Scale

The Accelerating Adoption Network sought to evaluate how these innovations scaled in terms of breadth, depth, and durability, as well as the impact on student learning, using a combination of teacher surveys, leader focus groups, and student outcome data (e.g., standardized assessments or course grades).

While the networks successfully scaled their breadth — expanding across 33 states, 130 sites, and impacting more than 3 million students — the depth and consistency of implementation varied significantly both across and within schools. Consequently, though student outcomes showed improvement over the course of the school year, the gains were not statistically significant. Regardless, most educators reported durable changes to instruction, with 62% reporting that the innovation positively influenced their instructional practices and 82% indicating that they wanted the work to continue.

The networks scaled, but the more interesting question is how. Three core learnings from the evaluation point to barriers that successful scaling need to overcome:

1. Implementation incoherence: The timing of the rollout, competing interventions, infrastructure challenges, or lack of consistency between the innovation and school or district priorities led to variation in implementation.
2. System conditions: Teachers largely found the innovations beneficial and easy to use, but system conditions such as from insufficient devices and bandwidth to competing tools and unclear expectations influenced implementation.
3. Research and evaluation infrastructure: Data-sharing agreements and institutional review processes, combined with rigid study designs, prevented agile and responsive decision-making, and limited our ability to gather interim data or revise research questions based on reality.

Despite current narratives about “screen time” and edtech efficacy, the Accelerating Adoption Network produced evidence of successful scaling. The included innovations reached more students, demonstrated growth on academic outcomes, and led to teachers making durable instructional changes, but this evidence is incomplete.

Evidence-Based Is Necessary, But Not Sufficient

Successful scaling depends on more than just having a high-quality, evidence-based tool. It requires a set of system conditions to be in place.

In our work with the Accelerating Adoption Network, we identified five interrelated elements that enable scaling. These cut across contexts and roles, offering actionable guidance for funders, policy-makers, and system-level decision makers.

• Strategic Alignment Before Adoption: Decision-making and conversations about edtech should begin with instructional strategy, not procurement. Clearly communicate how an innovation advances priority goals, what it replaces, and what it deprioritizes.
• Protected Time and Instructional Coherence: Make explicit decisions about when, where, and for whom an innovation should be implemented, not just used, and how it connects to curriculum, pacing, and instructional goals.
• Implementation Capacity: Build implementation capacity deliberately at multiple levels, ensuring educators have access to timely coaching, technical assistance, and leadership support within sites, while establishing routines that surface implementation patterns across sites.
• Enabling Infrastructure and Operational Readiness: Before scaling, ensure schools have reliable access to devices and connectivity as well as technical support.
• Continuous Improvement and Learning: Use formative data such as usage patterns, educator feedback, and interim student indicators to guide adjustments throughout implementation rather than waiting for end-of-year results.

Scale requires conditions that go beyond individual tools to include broader system readiness. As funders, policymakers, and leaders take an ever more critical look at the efficacy of edtech, the focus should include both selecting strong innovations and creating the conditions that allow educators and systems to learn, adapt, and improve over time so that the field can move beyond isolated successes and toward durable, equitable learning for more students. To do so, before adopting or scaling a new tool, it is critical to first ask these three questions:

• How does implementation of this tool fit coherently into our context?
• Do the necessary system conditions exist to ensure successful implementation?
• What research and evaluation needs to occur to support continuous improvement and to determine whether the tool achieves its intended outcomes?

When Kadion Phillips first joined Shrewsbury Public Schools in Massachusetts as Director of Information Technology a few years ago, one of the first tasks on his agenda, like many other district leaders, was tackling how to implement artificial intelligence.

To that end, Phillips and district superintendent Joseph Sawyer established a special committee of approximately 20 staff and faculty members to address the rapid development and implementation of AI.

Ultimately, the Shrewsbury AI committee came up with five pillars to guide the district’s use of AI:

1. Preparing students for an AI future - Focusing on readying students for a future in which AI tools will be infused throughout society, higher education, and the workplace.
2. Effectively utilizing AI learning tools for students - Using AI tools to appropriately support student learning across all classrooms.
3. Implementing AI working tools for staff - Finding the right AI platforms to help faculty and staff to work more efficiently and effectively.
4. Establishing guardrails against inappropriate AI use - Putting effective rules and best practices in place to protect data and discourage inappropriate use of AI.
5. Ensuring AI practices promote academic integrity - Creating an effective framework for acceptable use of AI by students in the context of academic integrity.

Rather than selecting specific tools, the committee has worked to align these pillars with the district's "Portrait of a Graduate" skills, such as critical thinking and innovation.

“We also started creating a guiding document, more of a framework, that we're hoping to get out to staff in terms of, ‘Here's where we are right now, and here's what we're trying to get to,” Phillips says. “We're also developing a one-pager of AI dos and don'ts to release this year.”

For this and his other work in his district, Phillips was recently recognized with a Tech & Learning Innovative Leader Award. He shares his continued efforts around AI and for improving the district’s cybersecurity.

A Work In Progress

The son of a math teacher from Jamaica who eventually went on to be a school principal, Phillips was exposed early to the possibilities of a career in education and technology. He was working in tech support for a startup company near Times Square in New York City with plans on going to grad school for physics when he heard that there was a local teacher shortage.

“I decided the best way to study for grad school is to teach physics,” Phillips says. He became a teaching fellow since he didn’t have a teaching degree, which was the shortest path to certification. “I was basically kind of thrown into the classroom in New York City after doing a summer intensive, and it was definitely rewarding but also challenging.”

While teaching, his tech expertise helped him become the de facto tech guy at his school, helping to resolve any issue, from fixing the copier to helping in the computer lab to running data analysis. Although he eventually became an assistant principal, following in his mother’s footsteps, a colleague suggested another path.

“I met the tech director in Rochester and he was telling me, ‘Hey, this is what you should do, you should be a tech director,” Phillips recalls. “And I'm like, ‘a tech director, what’s that?’ I'd never heard of that role. I know you had one person help send emails, but I never really thought somebody was in the back end coordinating it all.”

Phillips and his wife, also an assistant principal in NYC, were looking for a better work-life balance at this point, so they moved to suburban Massachusetts where she had been raised. After diving into tech leadership positions in a few districts, Phillips eventually ended up in Shrewsbury about two years ago, just in time for AI’s arrival in education.

As mentioned, Phillips has been proactive in implementing AI, although there are always challenges, such as staff who still are resistant to allowing it in their classrooms because they can’t get past the potential for cheating, and navigating the student data privacy aspects. Like for many districts, it continues to be a work in progress.

“We aren't necessarily picking a tool and saying this is what you need to use but it's like thinking about, ‘Hey, what are the opportunities where AI could be useful?’” Phillips says. “We figured out the skills that we want kids to get with AI is going to be similar to what we did with our portrait of a graduate, such as critical thinking, content mastery, innovation, resilience, collaboration, and even the global citizenship piece.”

‘Locking Down Without Locking Out’

In addition to a focus on AI, Phillips has prioritized cybersecurity as a foundational element of the district’s technology plan, which has earned his team a Massachusetts Governor’s Citation for Municipal Cybersecurity.

“We try to lock things down as much as possible,” says Phillips, adding that they have multifactor authentication on all systems now. “We also have tiered access, like limiting who needs to get access to what. So like our main account doesn't have access to our domain controller, for example, so even if our regular account were to get compromised, it wouldn't have any access to our other internal systems.”

Or, as he puts it, “locking down without locking out” users.

Despite his best efforts, however, Phillips knows cybersecurity is sometimes out of his hands.

“It's just one of those things where we're trying to do best practices but the vendors for our most critical systems, we also have to trust that they're also doing the same,” he says, noting some recent high-profile data breaches. “I think that's what keeps us IT workers up at night, you know, showing up in the morning and seeing that your system potentially could be compromised.”

The district utilizes a state-funded grant to provide training and conduct phishing campaigns, which has improved staff awareness and the reporting of suspicious activities.

Even so, cybersecurity needs to be a constant commitment.

“You can create all this security, do all the training, and build this wall around it,” Phillips says. “But then somebody just leaves the door right open.”

Tools They Use

• Google for Education
• Schoology
• Seesaw
• ParentSquare
• Zoom
• Apple MacBooks and iPads
• Epson
• Fresh Service

Back in 2010, my school district embarked on the first-of-its-kind (in Texas at least) one-to-one device roll-out. The device du jour at the time was the Apple iPad. While billed as a consumer device, its price point (back then 16GB went for $499) and ease of use made it an intriguing option for education. Sure, there were no real ways to manage it back then (I don’t think the phrase MDM had even been invented yet), but the increased access to our students was a major bonus as we stepped deeper into the 21st century.

The next year, Google decided to enter the market and introduced its own education-inspired device, the Chromebook. These devices were a little more robust than the previously attempted cheap laptop market (anyone remember those little “netbooks”?). While those initial models didn’t offer the touch-screen benefit of an iPad, they did have a better price point (around $299) and could be managed through the Google Admin Suite.

Where Apple tried to distance itself from comparisons, spouting its design and creativity-first belief, it was inevitable there would be a standoff. “Creativity is nice and all, but what about state testing?!” the people would shout. “What about a keyboard!?” the masses would cry. Add to that the nearly $200 in savings and the ability to manage, and it's no wonder why the iPad would lose out to its keyboard cousin. According to Ed Week’s Market Brief report, the Chromebook overtook the iPad in terms of preferred educational student device sometime in 2014.

While myself and many other tech leaders waited for Apple’s response, the ChromeOS market continued to dominate, fueled even more by a global pandemic and quick access to funds (ESSER) for devices.

“The One” Arrives

As of 2025, ChromeOS currently dominates the global education market with a 60% adoption rate. Apple’s response? It lowered the price of the iPad, but schools still needed third-party MDM systems to manage all the intricacies of these devices.

Flash forward to this year. Now fully 17 years since the debut of the iPad, Apple has re-entered the education market with its 13” Macbook named . . . Neo. Apparently this isn’t a nod to the mercurial Keanu Reeves character from The Matrix, but rather to convey a sense of “fun, friendly, and fresh.” According to Meriam-Webster, “neo” is a prefix defined as new, recent, or different.

Whatever the reason, the announcement on March 4, 2026, sent shockwaves through the education market. Its price for education buyers? $499 – the same as those first iPad2s I bought way back when. So after all these years, Apple is back.

The question isn’t whether the Neo is a good device, but if the conditions that doomed the iPad’s educational takeover have changed enough for Apple to finally win.

Unlike 2010, Apple enters back into a market that is highly volatile on multiple fronts. Education funding is in disarray and we are in the midst of “the Great Edtech Backlash” in which families are insisting that schools crack down on screen time. Some, such as LAUSD, are complying with their own guidelines, but the truth is, the device market in education is ever-fragile at the moment. Heck, even the new villain in Toy Story 5 is an educational tablet device called a Lilypad.

It’s at this moment when Apple decided to reengage with the education market. While timing isn’t great, some schools are already starting to put in orders for Neos for their students. Kansas City Public Schools recently purchased more than 4,500 of the budget-friendly Macbooks and now proudly claim to be an “all-Apple district.”

While KCPS have placed their flag firmly in the ground with the Neo, other schools are looking at how to stretch their limited funds. Some have said that replacements are starting to run low, a combination of less funds and greater supply chain issues due to global tariffs and trade disputes. Unlike previous Chromebook refresh cycles, the tariff-related pricing volatility and procurement uncertainty are forcing technology leaders to place orders earlier or get more creative around their deployment schedules and scope.

Helping To Stretch Dollars?

We can sometimes be a victim of the latest new, shiny object in education, but given the current landscape I mentioned above and our previous forays into student device deployments, it's wise for technology leaders to take a more cautious approach this time around. Procurement of the Neo doesn’t seem to be an issue yet, even though early demand has surpassed expected supply.

Apple also seems to be addressing the MDM issue. While they do have their own Apple School Manager platform, they also have partnered with Jamf to develop a more seamless approach to deploy, track, and maintain devices. They are also touting the longer shelf-life of their devices, which I have experienced first hand.

Typical Macbooks can last as long as 8 years, which dwarfs the 3-5 years many Chromebooks typically last (dependent on the model type). While modern Chromebooks can now receive up to 10 years of ChromeOS updates, most school districts still budget for replacement after approximately 4-5 years because of physical wear and tear, battery degradation, and student use patterns. Will the Neo have the same issues? Not if it keeps with Apple’s previous track record.

In an era when educational funding is short, anything schools can do to stretch dollars may be enough to put Neo on top. One of the hidden benefits of our iPad one-to-one program was that after we had used them for 4-5 years, we could still sell them at a decent rate to recoup some of our costs. Based on the historical performance of MacBooks and other Apple hardware in secondary markets, there’s no reason to believe the Neo will be much different.

The true cost of a device isn’t necessarily what you pay on day one; it’s what the device costs after accounting for its value at the end of its lifecycle. If your $350 Chromebook is worth almost nothing in four years but your $499 Neo can be sold for $200, which device actually costs the district less?

Gearing Up For The Head-To-Head Battle

The aforementioned “Total Cost of Ownership” is a conversation schools should be having anyway, but one that Apple feels it will win out on in the long run. That and the same argument it made during the launch of the iPad.

For the everyday student tasks, both devices do pretty well. Whether it’s accessing the Learning Management System (LMS) or writing and researching online, both devices handle that without much difference. Chromebooks excel at using Google’s suite of tools (not a shock there), but struggle when you start getting more involved in creative pursuits. Activities such as video editing, podcast production, music creation, graphic design, and software development are areas in which the Neo leaves the Chromebook in the dust. And . . . it can handle state testing! (Sarcastic cheers of rejoice.)

Of course, none of this matters if districts can’t absorb the additional upfront cost. A superintendent facing budget cuts may have a hard time convincing a school board to spend $499 today in order to save money four or five years from now. Even with the Total Cost of Ownership argument.

On paper, both devices make compelling arguments. The Chromebook wins on familiarity, price, and ease of management. The Neo counters with longevity, creativity, resale value, and Apple’s growing AI ambitions.

But spec sheets don’t tell the whole story.

Over the next few weeks, I’m going to put both devices through the kinds of tasks students actually perform every day. Writing, research, video creation, coding, offline work, state testing, battery life, and yes–the inevitable moment when a student manages to have 47 browser tabs open at once. Since I already mentioned The Matrix, I would be remiss if I didn’t put both devices through the paces with AI-empowered functionality, too.

In some ways, the bigger question isn’t which device is better, but which one makes more sense for schools to infinity and beyond (yes, another Toy Story reference). And after spending the last fifteen years watching Apple and Google trade punches in the education market, I’m not entirely sure I know the answer yet.

We’ll find out together.

Watch above or listen/download below.

About two-thirds of U.S. districts and states have some form of AI policy in place. The other third is, as Sasha Luks-Morgan puts it, the wild west. And even many of the policies that do exist, she argues, aren't doing what they're supposed to do.

Luks-Morgan is a policy analyst at SchoolAI, a K-12 AI platform they say is built around teacher visibility and student engagement, and she spends her days meeting with districts across the country helping them figure all this out.

In this conversation with Tech & Learning, she offers a clear-eyed framework for where AI policy in schools stands — and what it's going to take to move it forward.

Her three-pillar framework for any district starting from scratch: understand how your students and teachers are actually using AI right now; lock down student data privacy and personally identifiable information; and address academic integrity in a way that keeps students in the driver's seat of their own learning rather than offloading cognition to a machine.

"Systems only move as fast as the humans in them," she says. "That's the starting point."

She also draws a sharp distinction between two policy philosophies that often get conflated. Acceptable use policies, she says, are essentially lists of prohibitions. Responsible use policies are something different — these model good behavior, explain the reasoning behind expectations, and treat students as participants in the process rather than subjects of it.

On the vendor side, she is equally direct: the edtech industry has not taken enough responsibility for making tools work in ways that support good policy. SchoolAI's response is a free policy-writing service — available to any district, partner or not — in which a member of the team will sit with administrators, hear their concerns, and draft or revise a policy document they can take to legal counsel and implement.

"Bad policy is worse than no policy," she says. "We really want to see districts succeeding."

She also addresses district scale: the fundamentals — student privacy, ethical use, data governance — apply universally, from a 108-student K-8 district in the Mid-Atlantic to New York City Public Schools. What changes is implementation, committee structure, and who carries the weight. In small districts, that often means one superintendent or tech director holding the entire AI portfolio alone.

Her advice for the path forward: review your policy annually, build teacher expertise so your bench runs deep, and accept that the technology will always outpace the paperwork. The goal isn't to catch up but to stay close enough to matter.

Since AI came onto the scene, a lot has changed with it while some recurring themes have sustained. AI continues to act as a broad tool that provides remarkably imperfect productivity.

While significant cautions abound assuming its reliability, understanding AI’s usefulness while recognizing its limits makes my day as a principal more efficient AND effective. This brought me to consider what a typical day looks like for me using AI tools to offload administrivia.

With so many options, I wanted to break it down for your use. I don’t expect to use all these tools everyday, but try a few and walk away liberated, with more time and availability for students, staff, and parents. That’s a win for everyone.

I start my day thinking about rising concerns by my faculty over religious opt outs, based on a Supreme Court decision this fall. They were receiving template emails people find online (hilarious when they forget to replace ‘student name’ with their child’s name). My nervous teachers want guidance, so I use AI to clearly and comfortably provide them with direction:

DOE Memo Converted To Slides (Time spent: 8 minutes)

I google my state's DOE guidance on handling parental opt outs and find a state memo that is a great antidote for insomnia. It is the standard boring, lengthy legalease. I add the link to Gemini and select canvas mode.

What I like is it follows my prompt to be pleasant and supportive. The slide deck gets me “mostly there,” my common phrase for AI giving me a terrific start that I edit, ensuring my voice is present. I then export the slides and edit as needed.

This process takes me under 10 minutes, during which I transform a dry, boring memo into a meaningful presentation for my faculty. This would have taken about an hour analog-style, so I get back 50+ minutes to be present for my school community!

Text-To-Email Blast To Parents (Time spent: 5 minutes)

Principals are mediums of communication, and often, are expected to funnel information into a digestible explanation, even when that information is shared in fragmented bursts, as it typically is.

Next on my list is a field day announcement. I need to communicate to ensure everyone is prepared for our big day of events. My physical education teacher sends me this text (at right).

It is timely, but to stop and rewrite it in a cohesive format will take time I often don’t have during a busy day. (Time that is better spent outside my office as improvement doesn’t happen there but in classrooms.)

I copy and paste the phys ed teacher’s text with a simple prompt: Convert this into a message for parents of our school, Central elementary for our field day.

The output is thorough, better than I expect, done in about a quarter the time it would normally take. Better, to get all this into a 140-character text message, I prompt from the output, “140 characters” and voila! This way, parents get an email and a text, increasing the likelihood they see it. All from a single text message!

Letter Of Recommendation (Time spent: 7 minutes)

I can write fast but when I get asked to write letters of recommendation, these all happen within weeks of each other. Drafting a good letter with fidelity takes time. This is the next task on my list.

For this, I have the requester send me their resume, copy it into my chat, and prompt the AI to write a letter of recommendation based on the information–2 minutes!

The other 5 minutes is me editing, revising, removing those weird AI words, and most importantly, adding a sentence or two about something I know personally regarding the candidate. What took a half hour now takes a quarter of the time, and the quality is sound.

Email Summary and Reply (Time spent: 5-10 minutes)

Every principal gets the dreaded email that is seven pages long, often emotional and hard to decipher. It is nearing lunch time and I need to get through it fast!

Typically, I prompt the AI to summarize the email, and then reply in a tone that is kind but firm, or whatever is necessary. This is a big time saver! I am not glued to my laptop, reading and rereading run-on sentences to find the essence of a message buried deep. AI does a great job of siphoning this off and producing a quality reply. Onward!

Sensitive Guidance (Time spent: 5 minutes)

Speaking of emotional challenges, every principal has gotten a charged call or email from a parent upset that a school official called the Division of Child Welfare. This is my next task, after lunch.

While school personnel are compelled by strict legal guidelines, parents see it as a personal attack on their parenting. Understandable. I didn’t get this one right the first time, and have to add my state specifics, as well as the tone: sensitive, understanding, pragmatic, etc.

What is produced is excellent. I am provided references to the legal obligation, integrated with an ideal tone. I edit it, print it, and show the parent. Its well-organized format helps me work through the document, and the parent expresses acknowledgement of my understanding.

Picture Of Calendar Events Added To My Calendar (Time spent: 3 minutes)

My day has to have some fun mixed in, right? It’s mid afternoon and I think of that dentist post card for my next visit that I always lose.

I just take a picture and prompt Gemini to add it to my google calendar. This works with Copilot users, too. Even better, I can use it with an email with a series of meetings or events, or my Penn State football schedule from this ugly screen capture–and instantly, I have 12 events (games) added. That’s fast and reliable!

NotebookLM for navigating lengthy documents (Time spent: 3 minutes)

My day is almost over and I spent most of it out of my office because of the AI tricks above. Next, I decide to check on a contract question.

I head over to Google’s NotebookLM, into which I upload a pdf of the teacher contract, and ask how many minutes of pupil contact time a teacher can have. Boom, 287 minutes with the link to the direct reference!

This is priceless for the administrator who often gets questions and different interpretations of contract language. Time-saver, and response can be within the spirit of contracts.

A few more AI time-savers that I have embraced, but not all on the same day, include PD planning, form feedback, and evaluation feedback referencing our evaluation model. I estimate a few hours saved daily–not minutes–and my school wins when I’m present!

Note: Much of the information I use with AI may contain names or other personal data, so I am sure to remove any of that before prompting.

We’ve all been here. A speaker steps to the front of the room at a conference, workshop, school event, or leadership meeting, and everything stops. The screen is dark. The audio does not work. No one knows the WiFi password. Someone calls for “the tech person.” The device goes to sleep. The audience waits while everyone acts as if this is just part of the program.

It should not be.

Every event space, from classroom to boardroom, should have a dedicated presentation station with a device that belongs to the room. Not the speaker. Not the organizer. Not whoever remembered to bring the right adapter.

The presentation station should be part of the space, the same way the projector, screen, microphone, lights, and chairs are part of the space. When people walk into a room to learn, present, or lead, the basic setup should already work.

The BYOD Presenter Model Is Broken

The bring-your-own-device (BYOD) presenter model turns every speaker transition into a new technical gamble with a different laptop, adapter, audio setting, sleep setting, software update, notification popping up at exactly the wrong time, etc.

That is not a presenter problem, but a room design problem.

A well-run event space should not depend on every speaker plugging in, pairing, restarting, authenticating, and hoping for the best. The better model is simple: one room device, many speakers. Get the device working before the event starts, keep it working throughout the program, and stop making every presenter start from scratch.

That means the host provides a presentation station that is already connected, tested, and ready. The WiFi, browser, display, audio, and shared materials should be checked before anyone is standing in front of the room. The device should not go to sleep mid-session or require a password no one in the room knows.

As I shared in Presentation Checklist: 5 Ways to Prepare for Presentations, presenters have preparation to do, but the device setup belongs to the host. The presenter should be focused on the message, not on troubleshooting the room.

Put The Materials In The Cloud, Not Someone’s Laptop

Speakers should submit materials ahead of time and access these through a shared agenda with a short, easy-to-type link. The room device opens the agenda, each speaker clicks their own materials, and the event moves on.

I have been writing about digital agendas for years. In Anatomy of a Killer Agenda and 7 Benefits to Ditching the Paper at Meetings + Events with a Great Agenda, I explain how a strong agenda keeps links, materials, and next steps in one place. The same logic applies here. Put the work in the agenda, and put the agenda on the room device.

This is not about being fancy but reducing failure points. If the only copy of a presentation is trapped on one speaker’s personal laptop, the event is more fragile than it needs to be. If that laptop will not connect, loses power, needs an update, or has the wrong setting, the whole room pays the price.

Access Matters, Too!

A shared agenda also makes the event more accessible. Participants may need to open slides on their own devices, use screen readers, translate text, enlarge content, follow links, or review materials later. That does not happen easily when the content lives only on one presenter’s machine.

In Checklist for Accessible Teaching & Presenting, I explain why digital materials, microphones, links, and accessible design matter. A presentation station does not solve every accessibility issue, but it removes one unnecessary barrier: the idea that access depends on whatever device the speaker brought.

A QR code can help, but it should not be the only way. As I wrote in QR Codes in Education: Use with Care, Not Just Because They’re There, QR codes are useful when they reduce friction. They become a problem when they create one more barrier. Use the QR code, but also provide a short link for anyone who cannot or does not want to use a phone camera.

The goal is not to look high-tech; it is to make access simple.

What Every Presentation Station Should Include

A strong presentation station should have:

• A dedicated room device connected to the display and audio
• Stable internet already connected
• No unknown password barriers
• Settings adjusted so the device does not go to sleep during the event
• Easy access to the agenda and all presentation materials in the cloud
• A browser ready for commonly used platforms such as Google Slides, PowerPoint online, Canva, YouTube, and shared documents
• Sound, camera, microphone, and screen sharing tested in advance
• Accessibility features available, including captions when possible
• A backup plan that does not require switching to random personal devices
• A designated person responsible for checking the setup before participants enter the room

Use this checklist as the standard operating protocol for successful presentations.

The Bottom Line

A presentation station should belong to the space, not the speaker.

When the room is ready, presenters can focus on their ideas, participants can focus on learning, and hosts can run events that feel smooth, professional, and welcoming. The fix is not complicated: provide a reliable room device, stable internet, accessible cloud-based materials, and settings that support the event instead of interrupting it.

Get the room ready. Put the presentation station in place. Make the materials easy to access. Then let the speaker do what they came to do: connect with the audience and deliver a message worth hearing.

Educational technology teams are often evaluated based on uptime, system reliability, ticket closure rates, and, of course, cybersecurity compliance. Those metrics matter. Schools and universities depend on secure, stable digital environments to support teaching, learning, and operations. Yet, many IT departments unintentionally overlook the most critical metric that directly shapes trust in technology services: customer experience and usability.

When faculty, staff, students, or families contact an IT help desk, they are not simply engaging with a technical system but interacting with people. They almost certainly have a problem and often feel frustrated or rushed for time because they need a piece of technology for a meeting or a class. The tone, responsiveness, empathy, and professionalism displayed during those interactions often determine whether users see the IT department as a collaborative partner or as a barrier to getting work done.

Technology support in education is ultimately a service profession. When I used to hire frontline IT staff, the customer service orientation was what I looked for, as technical skills can be taught. Developing customer service skills is much more difficult.

The Human Side of Technical Support

Educational institutions increasingly rely on technology for every operational and instructional function. Learning management and student information systems, classroom presentation tools, cloud storage, multifactor authentication, and digital assessment platforms have become foundational infrastructure.

At the same time, many educators and staff members are under significant pressure, so when systems fail or access becomes difficult, frustration escalates quickly. In those moments, customer service matters as much as technical expertise.

A help desk interaction may only last a few minutes, but it can influence a user’s perception of the entire institution. Small gestures of professionalism and courtesy make a measurable difference. For example, many institutions now require users to verify their identity through live video before account recovery or password reset procedures can occur.

Security concerns make these processes understandable and often necessary. However, if an IT staff member asks a user to turn on their camera and display identification, basic professionalism suggests the support staff member should activate their own video feed. The interaction should feel reciprocal and respectful.

Turning on a camera, introducing oneself, and explaining the process helps establish trust and transparency. Such actions are simple examples of customer-centered thinking.

Courtesy Is Not Optional

In many educational environments, IT departments unintentionally adopt cultures focused primarily on compliance and enforcement, rife with rigid policies, transactional processes, and impersonal communication. This can result in users avoiding support interactions, faculty members delaying the reporting of problems, students disengaging from required security processes, and staff members creating risky workarounds.

Effective IT support teams understand that professionalism includes patience, clarity, and empathy. Users do not always understand technical terminology, nor should they be expected to. An educator struggling with multifactor authentication during class preparation is not primarily concerned with the underlying architecture of identity management systems; they simply need to regain access quickly so they can teach.

Ultimately, support interactions should minimize stress rather than increase it.

Simple practices can significantly improve service quality:

• Introducing oneself to the customer.
• Explaining why a security procedure is necessary
• Using plain language rather than excessive technical jargon; however, don’t talk down to the user
• Providing realistic timelines for resolution
• Sending routine satisfaction surveys, as nothing is more frustrating than getting an email saying a ticket has been closed when the problem has not been resolved.
• Designing support procedures around user workflows rather than institutional convenience

Educational institutions often speak about relationship building in classrooms and student services. The same philosophy should apply to technology support.

Cybersecurity Must Support the Mission

Cybersecurity threats facing schools and universities are real. Ransomware attacks, phishing attempts, credential theft, and data breaches continue to affect educational institutions nationwide. IT leaders are correct to prioritize strong security controls.

However, institutions sometimes implement security practices that significantly impair usability without meaningfully improving protection. In 1755, Benjamin Franklin wrote, “Those who would give up essential Liberty, to purchase a little temporary Safety, deserve neither Liberty nor Safety.”

IT leaders need to consider this concept when tightening security to the point that it is difficult to use the system being protected. Balance becomes essential. As my favorite university registrar has been known to say, he builds systems that address the needs of 90% of the students and faculty. One of the reasons he is my favorite is that he understands there needs to be enough flexibility in systems to address the needs of the remaining 10%.

If security measures or any institutional processes become excessively burdensome, users predictably seek ways around them. Faculty members may store passwords insecurely. Staff may rely on unofficial tools. Students may disengage from institutional systems altogether. Ironically, overly rigid environments can create additional vulnerabilities.

Edtech exists to support learning, communication, research, and organizational effectiveness. Functionality cannot become secondary to process or systems. This requires IT leaders to ask difficult but important questions:

• Does this security process genuinely reduce risk?
• Is the burden on users proportional to the threat?
• Have we designed this procedure from the perspective of the end user?
• Are we building trust or creating frustration?
• Would reasonable people view this process as respectful and transparent?

Strong IT organizations understand that security and usability are not opposing concepts. Effective security should integrate as seamlessly as possible into the user experience.

Leadership Sets the Tone

Leadership shapes customer service cultures; they do not emerge accidentally. If institutional leaders reward only ticket closure counts and compliance metrics, staff members naturally focus on speed and enforcement. If leaders instead emphasize professionalism, communication quality, and relationship building alongside technical competence, they support cultural change.

IT leaders should regularly review support practices through the lens of user experience. Mystery shopper exercises, satisfaction surveys, and direct feedback sessions with faculty and students can provide valuable insights into how support interactions are actually perceived.

Technical expertise alone does not automatically produce strong customer service skills. Help desk professionals should receive training in communication, conflict management, accessibility, and customer interaction strategies just as they receive technical training. In educational settings, especially, technology teams should remember that every interaction contributes to institutional culture. At the end of the day, users rarely remember the specific technical details of a support ticket but how they were treated.

As noted earlier, Benjamin Franklin famously warned against trading essential freedom for temporary security. The digital world presents a similar challenge. Educational institutions must protect systems and data while still preserving accessibility, usability, transparency, and trust. Technology support should help people accomplish their work, not create unnecessary obstacles to it.

Watch above or listen/download below.

More than 550 students from the United States, Estonia, Taiwan, and Thailand descended on the University of Wisconsin-Madison last month for the 2026 World KidWind Challenge — and for KidWind founder Michael Arquin, the event was proof of something he’s believed since 2009: give kids a wind turbine to build and they will find a way to get there.

“Despite my best attempts to kill it, it keeps going,” Arquin said with a laugh during this recent Tech & Learning podcast conversation.

The 2026 World KidWind Challenge drew 125 teams of 4th–12th graders competing across wind turbine and solar home design categories. Teams were scored on four dimensions: turbine or solar home performance, a presentation before a panel of judges, an “Instant Challenge” requiring on-the-spot problem solving, and a timed Knowledge Test on clean energy concepts.

The structure is deliberate, Arquin says, because clean energy careers are not only for engineers. “There are lots of careers in the energy space that are non-STEM related,” he says. “We’re trying to tell kids: yeah, you might like the engineering side, but you might like the design part, or the communication side.”

For Morgan Berkgren, a physics teacher from Oakley, Kansas, that message landed years ago. Berkgren, who attended graduate school for biology before being handed a physics class in a small Western Kansas district, discovered KidWind around 2018 and built what became one of the program’s most celebrated multi-team programs.

This year she brought four teams to Madison — one middle school squad, the Twisted Sisters, and three high school teams, Power Breeze, Spirit Winds, and Sea Breeze — all competing in the open home-built generator division for the first time. “I looked at them and I’m like, 'Dude, I went to school for biology,'” Berkgren says. “And they’re like, 'Yeah, Ms. Berkgren, but we want to go to the next level.'”

Teachers and coaches interested in bringing KidWind into their classrooms can get started at kidwind.org. KidWind offers funded virtual workshops and will ship a starter kit directly to educators. Local and regional challenges run throughout the school year, with top teams qualifying for the World KidWind Challenge each spring.

Arquin’s advice for anyone on the fence: “Just start. You’re going to learn a lot along the way — and so will your students.”

A recent book review in The New York Times generated controversy after readers noted it echoed certain passages from a previously published review of the same book in The Guardian. After investigating the incident, New York Times editors revealed the writer who wrote their review admitted to using AI and was unaware the chatbot was lifting ideas.

This is a mainstream example of a new type of plagiarism that has become increasingly common in the classroom. I’ve started calling AI plagiarism to distinguish it from both traditional plagiarism and overall AI cheating.

What’s striking is that it emerges both from clearly inappropriate AI use, such as students entering prompts directly into an AI chatbot, as well as from using AI as a brainstorming partner. As such, it has significant implications for how educators think and talk about appropriate AI use and prevent inappropriate use, at least when it comes to writing assignments.

Thinking about these policies recently has changed my approach as a writer and professor. Here’s how I'm overcoming AI plagiarism.

Warning Students About Dangers of AI As A Thought Partner

Since generative AI emerged as a mainstream force in education, the use of AI as a brainstorming tool has been recommended by many educators as an appropriate way for AI to assist the writing process.

Though I was initially open to this idea, I recently became skeptical of its ability to truly help as a writing partner. Students I work with have not improved their writing by asking AI for advice, and if anything, the writing I read, both in and out of class, has gotten much worse in recent years.

Beyond quality concerns, using AI as a “thought partner” places the user at a high risk of committing plagiarism. Also, if everyone is using it for the same assignments, there's potential for a lot of overlap, intentional or not.

I’ve started to tell students that taking advice from AI is like buying art from an unscrupulous art dealer. You can never be certain that the painting you purchased doesn’t belong to someone else.

Focusing On The Plagiarism Part

One positive about AI plagiarism is that it's frequently easier to catch using an old-fashioned plagiarism detector such as SafeAssign and Turnitin, which search the internet for papers submitted by other students and similar content in general.

In the writing courses I teach, when multiple students start using AI to write their papers, similar setups and phrasings start appearing. It’s easier to confront students about this than it is about potential AI use, which is more difficult to prove.

I’m all for giving students more chances and treating these infractions as teachable moments, but I’m taking a harder line than I once did because AI has made this problem more prevalent. The first time I catch students with a line or two of text that’s identical to another student, they might get off with a warning and a conversation with me. The next time, the penalty will be reported for an academic infraction.

Being Less Strict About Small Mistakes

Another way to discourage AI use in general is to let students know that I won’t be taking them to task for little grammatical mistakes as long as what they write sounds like they’re actually writing.

I never thought I’d say this, but I miss the run-on sentences and tortured, passive sentence construction, and all the other common grammar mistakes I used to see constantly. I’d rather flawed human writing than the flat, passionless, and generic owner's manual-type pieces that too many students are submitting these days.

By encouraging students to truly share their writing, without fear, I’m trying to take away one potential incentive for turning to AI.

Remind Students That AI Writing Is Bad Writing

Many people are convinced AI is making their writing better, but plagiarism concerns aside, it isn’t. I can tell when someone on social media starts using AI to write their posts, and I also see when a student turns to the AI dark side mid-semester. It’s not an improvement.

This all bears repeating for students. Writing can be an emotional roller coaster. I’ve been a professional freelance writer for close to two decades and have averaged hundreds of published stories per year. Despite this, in the course of nearly every story I’ve ever written — including this one — there was a point when I stopped and thought I should contact my editor and tell them the story is just not working.

AI catches students, and even professional writers, in this vulnerable state, and suddenly, its self-confident and immensely digestible prose looks really good. Don’t fall for it! When it comes to writing, AI is a con artist that makes you believe it knows better than you. It doesn’t, and that’s the message we should be sharing constantly with students.

It is graduation season, which also means it is time to remind busy school leaders and administrators about what families actually remember most. Families may appreciate the speeches, decorations, music, and venue, but what they truly care about is the moment their student walks across the stage and hears their name announced properly. That is what they record on their phones, celebrate with relatives, and fondly look back on years later.

Recently, an Arizona community college faced intense backlash after an AI-powered system reportedly malfunctioned during commencement and skipped several graduates while reading names. Too quickly, the conversation shifted toward blaming “AI system failure.”

No, I see this less as a technological issue and more as a failure in school leadership and planning. And candidly, if this critique sounds harsh, it is because this feels like such an avoidable and unforced leadership error. No one should have been asking AI to handle this responsibility in the first place.

The reason this failure resonated so strongly is because commencement is not simply an operational event, but a milestone centered on recognition, dignity, and belonging. Names carry culture, history, sacrifice, and identity, particularly for multilingual families, immigrant families, and first-generation graduates whose educational journeys often represent years of perseverance and hope.

Pronunciation is part of inclusion as names matter. Culturally responsive leadership includes taking the time to learn and say names correctly. That is why this responsibility should receive more care, not less.

Using AI to Support, Not Replace, People

Schools spend months preparing commencement ceremonies. Leaders rehearse speeches, coordinate seating charts, organize music and photography, and carefully plan countless operational details. Yet somehow in the example cited above, the reading of student names was treated as a task that could simply be handed off to automation. Even more troubling was the apparent absence of a meaningful backup plan.

Every experienced administrator understands that technology can fail, which is exactly why safeguards and human oversight should have been built into the process from the start. Graduation season is not the time to experiment with new AI systems in ways that directly impact students and families.

Ironically, AI could have supported this process in far more thoughtful ways without replacing people. Institutions could have used AI tools to:

• Help announcers practice difficult pronunciations
• Review student-submitted recordings of their names
• Identify names that may require additional preparation
• Support multilingual pronunciation coaching
• Give human announcers more confidence and accuracy before the ceremony

This is the kind of AI use education should embrace: technology that supports people rather than replaces them.

The best uses of AI in education should help educators and leaders become more thoughtful and prepared. Instead, too many organizations are using AI to distance themselves from the moments that deserve the greatest care. In the process, schools risk damaging traditions and violating the rightful expectations families bring to these ceremonies.

Ultimately, this was leadership failure rooted in poor judgment, poor planning, and the mistaken belief that a deeply personal moment could be outsourced to automation. Graduation did not fail because of technology, but because leaders failed to protect the experience students and families deserved.

In their new vision for public schools, Randi Weingarten and the American Federation of Teachers (AFT) are right: students need more active, human, hands-on learning. They need projects, movement, collaboration, civic engagement, career-connected experiences, and chances to tackle real problems.

That is the strongest part of the AFT’s 10-point plan. The emphasis on career and technical education (CTE), experiential learning, and preparation for the future of work deserves attention. If we are serious about preparing students for modern careers, students need learning experiences that reflect the tools, practices, and expectations they will actually encounter beyond school.

The 10-point plan’s name, “Devices Down, Eyes Up, Hands-On,” may sound appealing, especially to adults exhausted by distraction, low-quality screen use, and Big Tech overreach. The problem is that it aims at the wrong target. Devices are not the enemy of active learning. Passive, drill-and-kill learning is.

A drill-and-kill worksheet on a Chromebook is still a worksheet. A lecture with a slide deck is still a lecture. A multiple-choice test on a screen is still a multiple-choice test. Technology did not create passive learning. In many cases, it exposed how much passive learning was already there and made it easier to scale.

Not every lesson needs a device. Rather than considering whether screens are up or down, the better questions are: what are students doing, why they are doing it, and whether the tool helps them learn, create, connect, solve, or demonstrate understanding.

The AFT plan includes several worthwhile ideas: stronger privacy protections, independent research, limits on manipulative technology, more hands-on learning, and adequate public school funding--all of which is important. But when the frame becomes “devices down,” schools risk confusing a management strategy with a learning strategy.

While the plan’s name is misguided, it is worth looking at each of the 10 points directly. Some parts are right, some need a better frame, and others would create the opposite of what our students and schools actually need.

What follows is where the plan gets it right, where it falls short, and why “devices down” is not the path to meaningful learning.

Point 1. No screens for pre-K through second grade

The AFT calls for no screens for students in pre-K through second grade unless there is a compelling reason, such as support for a student with special needs.

The instinct is understandable. Young children need play, language, movement, stories, relationships, manipulatives, exploration, and hands-on learning. They do not need to spend large parts of the day watching videos or completing low-level digital tasks.

But “no screens” goes too far. A child passively consuming content is not doing the same thing as a child using literacy tools to hear a story, build vocabulary, practice phonics, illustrate writing, translate a word, enlarge text, or share a recording with family. Those are different experiences and policy should treat them differently.

Accessibility should not be treated as an exception or afterthought. Some students read, write, listen, speak, translate, caption, enlarge text, dictate, or produce work more effectively with digital tools. That includes students with disabilities and multilingual learners, but it also includes students who may not have a formal accommodation. A policy that says “no screens unless you have special needs” can unintentionally stigmatize the students who benefit from access.

The standard should be higher than “screen or no screen.” Screens should be purposeful, adult-guided, developmentally appropriate, and never allowed to crowd out human interaction, play, movement, stories, and hands-on work. That is more demanding than a ban. It requires educators and policy makers to consider thoughtful choices.

Point 2. No student-facing AI in elementary schools

The AFT also calls for no student-facing artificial intelligence in elementary schools, supervised AI use for older students, and a ban on social companion chatbots for students under 16.

Most would agree that social companion chatbots do not belong in elementary schools. Schools should not help companies simulate relationships with children. But banning all student-facing AI in elementary school is too broad. There is a difference between a child forming an emotional attachment to a chatbot and a teacher using an AI-supported tool for translation, accessibility, vocabulary support, feedback, or differentiated practice under supervision.

There is also a difference between using AI to avoid thinking and using AI to strengthen thinking. Responsible AI use should start with the learning goal, not with the tool. Teachers need to help students understand when AI supports learning and when it gets in the way.

Young children should not be using AI freely. However, that does not mean they should learn nothing about AI or never encounter an AI-supported learning experience. They need age-appropriate digital and AI literacy: this includes what responsible and appropriate use is, how it should be questioned, and how to think critically before tools are placed in front of them without context.

Avoidance is not preparation. Guardrails, judgment, and age-appropriate instruction are.

Point 3. Redesign schooling around active and career-connected learning

This is where the AFT plan is strongest. Active learning, project-based learning, experiential learning, and career-connected learning should be at the center of the conversation.

If students are bored, distracted, and disengaged, the answer is not simply removing devices. The answer is to design learning worth paying attention to..

This matters most in CTE. Students preparing for healthcare, media, construction, design, engineering, business, computer science, culinary arts, public service, or green energy will not enter device-free workplaces. They will use digital tools to research, design, diagnose, communicate, manage projects, analyze data, create media, document work, collaborate with teams, and solve problems.

If we say we value career-connected learning while making “devices down” the default, we are sending mixed messages. Modern CTE requires meaningful technology use. Students should not just learn about careers, but practice with the tools of those careers in authentic ways.

Every lesson does not need a screen. But when a device helps students do the work more authentically, collaboratively, creatively, or effectively, putting it away makes no sense.

Point 4. Build literacy, numeracy, and civic engagement

Students need strong literacy, numeracy, and civic engagement. But “devices down” is not how students build those skills for the world in which they live.

Students need to read deeply. They also need tools that support vocabulary development, translation, accessibility, feedback, publishing, and access to high-interest texts at different reading levels. Learning to write in a world with AI, or using a strong digital reading platform well, is not the same as random screen time.

Students need number sense. They also need opportunities to explain their thinking, use data, create tutorials, and learn from one another. When students create a video explaining a math concept, they are not just answering a problem. They are making their thinking visible.

Students need civic knowledge. They also need to understand how algorithms, misinformation, surveillance, platforms, AI, and online communities shape public life. Digital citizenship is civic education now. It cannot be treated as an extra lesson after something goes wrong.

A device-down approach does not teach students how to evaluate information, protect their privacy, manage their digital footprint, communicate responsibly, or participate thoughtfully in online spaces. Schools should recognize students are in these spaces.

Point 5. Focus on well-being

The AFT is right to connect learning with student and family well-being. Students do not learn well when they feel unsafe, unsupported, isolated, anxious, sleep-deprived, or disconnected from school.

But well-being is not simply a screen-time issue. If students are struggling, schools should look at schedules, relationships, homework loads, sleep, movement, food insecurity, school climate, mental health support, family communication, social media, and the habits students are developing in digital spaces.

Digital well-being belongs in the curriculum, not just in the device policy. Students need to understand how notifications affect attention, how platforms are designed to keep them engaged, how to protect their privacy, how to communicate respectfully, when to disconnect, and when a face-to-face conversation, movement, rest, or quiet focus is the better choice.

A ban can make adults feel as if they acted meaningfully while leaving the deeper issues untouched. The goal should be healthier learning environments and healthier digital habits, not simply less device access or fewer minutes with screens.

Point 6. Protect academic freedom, intellectual property, and educator judgment

Educators should not be handed technology because a vendor made a good pitch. They also should not be blocked from safe and useful tools because policymakers are scared. Academic freedom matters. Teachers should have the professional judgment to decide how their students learn best. That includes the ability to choose when a digital tool supports learning, when it gets in the way, and when a mandated curriculum or platform is not serving their students well. A district-approved tool is not automatically good instruction. A required platform is not automatically the right fit for every learner, lesson, or classroom.

Educator judgment should be central, but teachers should not be left alone to evaluate every app, platform, privacy policy, accessibility claim, AI feature, and instructional promise. That is not sustainable, and it is not fair.

Schools need trusted public-facing reviews, privacy ratings, accessibility information, safety indicators, AI risk assessments, and quality signals that help educators make better decisions before tools reach students. Common Sense Media, ISTE+ASCD, Digital Promise, and similar organizations have all contributed pieces of this infrastructure, but the work is fragmented, unstable, and unevenly known.

Teacher judgment matters most when it is supported by reliable information and respected in practice. The answer is not to tell every educator to vet everything alone, but to build stronger, transparent systems that help educators understand risk, quality, accessibility, privacy, and instructional value, while still trusting teachers to make the final instructional call for the students in front of them.

Point 7. Establish a gold standard for AI safety and privacy

Schools should demand strong safety and privacy protections from AI providers. Companies that cannot meet those standards should not serve K-12 education.

Student data should not be the price of participation. Schools should know what data is collected, how it is used, who has access to it, how long it is retained, whether it is used to train models, and what happens when a tool is discontinued or sold.

This is where clear standards matter. Schools need transparent contracts, strong data rules, parent communication, accessibility review, bias testing, security review, educator training, and ongoing monitoring. They also need the will to say no to tools that do not meet the standard.

Safety and privacy standards should not become a back door to blanket avoidance. The right response to risk is governance. If AI is going to affect the future of work, schools need to teach students how to use it safely, ethically, and critically. That starts with adults making informed decisions before tools are placed in front of students.

Point 8. Create an independent research consortium

There needs to be independent research on AI, screens, and educational technology, research that is not funded by the industries selling the products. Schools have been flooded with products that make big promises and provide thin evidence. Too often, districts are left to figure out impact on their own while vendors move on to the next sale.

Some useful work already exists through organizations such as Common Sense Media, ISTE+ASCD, Digital Promise, and the EdTech Evidence Exchange. Again, the issue is that this work is scattered, unevenly used, and not always connected to the decisions schools make every day.

The research questions also need to improve. “Are screens good or bad?” is too shallow. We should be asking which kinds of screen use support learning, which kinds undermine learning, for which students, at what ages, under what conditions, with what teacher support, and for what goals.

Passive consumption and active creation should not be treated as the same thing. A student scrolling through entertainment content is not doing the same work as one using a device to interview a community member, analyze data, produce a podcast, translate materials for a family audience, or document a science investigation. That is why the goal should not simply be more research. Educators do not need another disconnected report that treats all screen use the same. They need trusted evidence they can actually use to make better decisions about tools, teaching, and learning.

Point 9. Adequately fund public education

Public schools need adequate funding. That includes staffing, facilities, professional learning, mental health supports, libraries, arts, athletics, career pathways, accessibility tools, modern infrastructure, and technology that works.

It also includes the basics too many people skip over: reliable WiFi, adequate bandwidth, devices that function, enough technical support to keep systems running, and clear guidance on how to use educational technology well. It is not enough to hand schools devices and platforms and then blame teachers when the infrastructure, support, or instructional guidance is not there.

Too often, schools are told to prepare students for the future while being funded for the past. That is especially damaging for CTE. Students need access to current tools, not outdated simulations of work. They also need educators who have the training, support, and time to use those tools in ways that strengthen learning rather than digitize old assignments.

The AFT is right to warn against AI and vouchers becoming another excuse to defund public education. But this is also why the “devices down” frame is risky. Wealthier families will still provide access, tutoring, devices, AI tools, enrichment, internships, and networks outside of school. If public schools pull back from teaching students how to use modern tools well, students with less access outside school lose the most.

That is not equity. That is retreat.

Point 10. Tax Big Tech

The AFT calls for a tech tax on Big Tech earnings and some business operations to address harms and disruptions caused by technology and AI.

A real argument exists for that. Technology companies have profited enormously from public attention, public data, public infrastructure, and public institutions. If their products create costs for schools, workers, communities, and the environment, they should not get to externalize those costs while public systems clean up the mess.

But a tech tax should not become a substitute for better education policy. If money comes from Big Tech, it should support public-interest goals: independent research, educator professional learning, privacy enforcement, accessibility, digital citizenship, AI literacy, CTE infrastructure, and public accountability.

It should not become another funding stream that sends public dollars back to the same companies with minimal oversight.

The better frame is purpose first

The AFT is right about a lot. More hands-on learning. Stronger privacy protections. More scrutiny of Big Tech. Better protection for children from manipulative platforms and simulated relationships. A renewed insistence that education is human work.

But “devices down” is the wrong goal. It makes the device the problem when the deeper issue is often learning design, adult guidance, infrastructure, privacy, safety, and support.

Devices can distract students. Notifications distract. Poorly designed platforms distract. So do boring lessons, unclear tasks, weak relationships, irrelevant assignments, and classrooms where students are asked to comply rather than think.

Removing the device may solve an immediate classroom management problem, but it does not prepare students to manage attention when the device comes back. And it will come back. In college, at work, in civic life, in relationships, and in nearly every modern career, students will need to focus, create, communicate, and make good decisions in technology-rich environments.

Students need explicit practice with digital well-being, attention management, purposeful tool use, and knowing when technology helps and when it gets in the way. They need to learn when to close the screen, when to use it, when to silence notifications, when to collaborate face-to-face, when to use AI, when to question it, and when to do the thinking without it.

Sometimes devices should be down. Students should discuss ideas face-to-face, build with their hands, play outside, conduct experiments, write privately, make art, and listen to one another without a screen in sight.

Other times, devices should be up. Students should use them to research, create, collaborate, publish, translate, document, code, communicate, design, get feedback, and solve problems.

The goal is not more technology or less technology, but better learning, healthier habits, and students who know when and how to use the appropriate tools to think, create, communicate, collaborate, and solve problems.

University of Central Florida associate professor Humberto López Castillo was recently looking at a student project when he saw that it cited a paper by Castillo himself. There was, however, a problem with this citation: López Castillo couldn’t remember ever writing this paper or conducting the research it referenced.

“It was a hallucination,” López Castillo says.

The student had clearly used AI, and López Castillo shares the story as an example of how not to use AI in the classroom. Still, he has plenty of counterexamples.

López Castillo, a trained pediatrician and associate professor in the Department of Health Sciences, has also seen students use AI in creative ways to promote public health understanding, and as a research tool. For one project, López Castillo asks students to explain health concepts from class to non-experts, and since he started encouraging students to use AI, he’s seen the projects get better. Students have created health-themed board games and Hamilton-style rap songs. Others have designed AI to aid in health research in ways that wouldn’t be possible without the technology.

This compassionate and student-centered approach to AI use is part of why López Castillo was named Superhuman (formerly Grammarly’s) 2026 Educator of the Year. The student who nominated López Castillo for the award, Vardhan Avaradi, is a bioscience major, who with López Castillo’s mentorship, is using AI to try to comb through massive data sets with the goal of predicting people who are at risk for cardiovascular disease.

López Castillo shares some advice for other educators on how they can use AI in their classes.

Teaching With AI Effectively: Set Expectations

The first step to positive AI use in a classroom is making it clear to students when they can and can’t use the tool, López Castillo says.

“Perhaps my most important advice is to set expectations up front around what is allowed, what is not allowed, what you are expecting to see, and what is a big no-no for your classroom,” he says.

He adds that at the University of Central Florida, “We have several policies on the menu that we could use. We can say, for example, ‘AI is absolutely not allowed in the classroom,’ all the way to ‘AI is absolutely encouraged in this classroom,’ as long as you specify there's a critical thinking component.”

López Castillo leans toward the permissive approach but makes sure that student AI use is human-driven.

Model Proper Use

Part of encouraging proper AI use is modeling the types of uses you want to see.

For example, for a summer course López Castillo is teaching in Barcelona, he is asking students to share regular blog posts. He tells students, “You may generate some content through AI, but you still have to put in your human element.” He models exactly what he means in his introduction post in which he shares photos, talks about his hobbies, and makes it clear the post was written by a human.

“Without even giving them guidelines on what their introduction post should look like, they just take that as their model, and they go with it,” he says. They can use AI to help generate their posts, “but of course they have to tweak it, and they have to provide their own input on who they are.”

Learn From Students

López Castillo is not afraid to admit that when it comes to using AI in the classroom and health science more broadly, he is frequently as much a student as those he’s teaching.

“I learn a lot from students,” he says. “Teaching and learning is a two-way street, so they are teaching me new tricks, I'm teaching them new tricks on how we use AI, and it has been very collaborative.”

His students, for instance, encouraged him to use AI to code, which, with proper oversight, has been really helpful, López Castillo says. They’ve also used the tool in unexpected ways, from the aforementioned projects looking at cardiovascular health to a virtual escape room in which participants had to correctly diagnose a patient with diabetes in order to escape.

Embrace Teachable Moments

López Castillo has long allowed open-book tests because that is what the clinical world is like.

“I don't think real life operates like, 'We're gonna lock you down in a room without resources, and you have to produce everything from your brain,'” he says. “I'd rather make sure that you have a fair balance of the tools that are available to you.”

He feels the same way about AI, but that doesn’t mean he doesn’t have to work with students to ensure they are using AI correctly. For instance, when that student cited a paper by López Castillo that didn’t exist, he met with the student.

“We sat down, and it was an interesting moment, where I was like, 'Tell me more about this paper by me that you incorporated in your reference list,'” López Castillo says. The student immediately got nervous because he couldn’t provide any details since the paper wasn’t real. When the student admitted to not checking his sources, López Castillo reminded him that even with AI, humans are still in the driver’s seat.

“You are the one who's responsible for that writing,” López Castillo tells his students. “Your name is the only name that's going to be among the published authors, so you are the one who needs to verify those sources.”

He adds that rather than being a drawback, allowing students to make these types of mistakes with AI use in the college setting has value.

“It is a teaching opportunity," López Castillo says. "This is the moment to make those mistakes."

Vibe coding might sound like a hipster on a surfboard with a MacBook, but it’s actually a simple way to describe creating computer code using artificial intelligence.

Instead of writing every line of code yourself, you describe what you want created and the AI generates the code for you. The “vibe” part comes from the idea that you may not actually know exactly how the code is working. You are guiding the AI by feel, asking for changes, testing the result, and moving toward something that works.

While vibe coding can be looked down upon by some in the development community, its popularity is growing fast, as are its capabilities.

When people, including myself, first started vibe coding, they would ask a large language model (LLM), such as ChatGPT, Claude, or Google Gemini, to create the code for a specific app or tool, then copy and paste that code into a code editor and run it manually.

When an error appeared, you would copy that error back into the LLM and it would, hopefully, provide a fix that you would then dutifully copy and paste back. You still needed to be relatively technical, even if you were not a developer, to get all these systems set up and working. It wasn’t elegant, but it could help create simple tools, fix website issues, and build early prototypes.

Things have moved on a lot over the past few years, though. Vibe coding is now much more powerful and can complete more complex tasks and create feature-filled applications. It has also become much more accessible, with bespoke tools now doing much of the heavy lifting and setup work required to get an app or website working.

Lovable, for example, uses a variety of LLMs to create a one-stop vibe coding shop using credits as AI currency. It allows people with very little, or even no, knowledge of how to develop apps to create something quickly just by describing it. Of course, as with everything AI, understanding how to prompt well makes all the difference.

I like using Claude with VS Code when I vibe code, as I know a bit of the programming language Python and want to be able to see what’s happening to the code and do some manual troubleshooting. It’s also relatively cheap.

To be brutally honest, though, AI is far more capable than I am at writing code, and I quickly have to defer to it if and when things don’t go as expected.

The difference between the two approaches is significant. With a tool such as Lovable, the “guts” are hidden and dealt with automatically, whereas with more traditional vibe coding tools you will need to set up your own environments or run programs directly on your computer to get something you can use.

What are the risks of vibe coding?

This leads into one of the biggest risks of vibe coding: you don’t fully hold the reins.

An LLM can misunderstand what you want. It can also make mistakes, confidently create code that does not work, or create fragile systems that look good but are hard to change or maintain. As complexity increases on a project, you might find yourself losing control of it. This can be particularly problematic when the AI ties itself up in knots trying to understand what you want or fix something it does not know how to fix.

This is where choosing the right AI tool can help a lot. In my experience, some AI coding tools can get stuck and claim to have fixed an issue when clearly it has not, or even worse, created a new one somewhere else.

As with all LLMs, you need to be able to decipher the language when it tells you something such as, “Oh sorry, I see what I need to do now, let me fix that,” so you know when it is genuinely correcting the problem and when it is just trying to be helpful but is actually more stuck than Winnie the Pooh in a jar of honey. Best-case scenario this can lead to wasted time or credits being used up without the issue being resolved.

It’s good to be aware of the recent horror stories, such as AI coding tools deleting databases or removing whole features from a product. It’s also worth being careful with the idea that vibe coding means anyone can now create an app. That is true up to a point, particularly for simple prototypes, experiments, and personal tools. But creating a safe, reliable app that other people depend on is a different thing altogether.

You might be able to describe what you want in plain English and get the AI to generate the code, but that does not remove the need to check, test, and understand what has been created. The easier AI makes it to build something, the easier it also becomes to build something fragile without realizing it.

This is why relying solely on a vibe-coded program that is integral to your work can be risky. But there are areas where vibe coding is, frankly, a bit magical.

What are the benefits of vibe coding?

Vibe coding feels like a genuine shift because it lets people who are not developers start making software, in the same way the early web let people publish without owning a printing press.

One of its most powerful benefits is that it allows someone with an idea to create a working proof of concept quickly. That does not mean the end result is ready to be used by hundreds or thousands of people, but it can be enough to show what is possible.

Creating a visual and working prototype can also help guide a development team and stakeholders. Instead of simply describing what you want, you can show an early version of the tool or app and use that to shape the discussion.

Vibe coding can also help you learn by doing. You can ask the AI questions and ask it to explain what it is doing and why. Over time, this can start to give you a better understanding of how the code works.

One of the most powerful uses of vibe coding, in my opinion, is creating small tools or programs that are very specific to your needs. Whether that’s a simple game you want to create for your students, a classroom quiz, a revision tool, or an app that helps keep parents up to speed on school changes, vibe coding can help you get there more quickly and at far less cost than using a development team or, in some cases, paying for a third-party provider.

Vibe coding can also be useful for experienced developers. They might not use it in quite the same way as a beginner, but it can still help them move faster. Sometimes the AI is useful for getting the first rough version of something working, even if the developer then has to go back through it, tidy it up, and make sure it is actually safe and sensible.

The important point is that vibe coding is strongest when it is used for experimentation, learning, and prototyping. It is less suitable when the software needs to be secure, reliable, long-lasting, or used by lots of people without proper review.

How to explain vibe coding to students in class

The best way to explain vibe coding to students is to say that it is giving instructions to an AI and seeing whether the AI can turn those instructions into a working program.

The student is not writing every line of code in the traditional way. They are describing what they want the software to do, then testing whether the AI has understood them correctly. That’s the key difference.

In normal coding lessons, students often start by learning syntax: the rules of the programming language. They learn how to write a variable, a loop, a function, or a condition, and then use those building blocks to make something.

With vibe coding, the starting point is often the idea instead. The student might begin with, “I want a quiz about volcanoes,” or “I want a game that helps me practice spelling.” The AI then creates the first version of the code.

That does not mean the student has finished. In many ways, that is where the learning starts. The student has to work out whether the AI understood the task. Does the program do what they expected? Is the information correct? Does the scoring work? What happens when a user makes a mistake? Can the student explain why the code behaves the way it does?

This is the part students need to understand. Vibe coding can feel instant, but it is not simply pressing a button and getting a finished app. It is a process of describing, testing, questioning, fixing, and improving.

If districts want to strengthen trust, improve engagement, and protect their reputation, communication strategy can’t start in the central office — it has to start in the classroom.

In district communications, it’s tempting to focus on what the central office sends: board updates, emergency alerts, strategic initiatives, press releases, policy changes. These messages matter. They shape public perception and provide essential information to families and staff.

But ask most parents how they actually experience their school district, and the answer is far simpler: through their child’s teacher.

For many families, their perception of a district begins and ends with classroom communication. They may never speak with the superintendent or interact with district administrators. Trust is built — or eroded — through the daily interactions between teachers and families.

Start With the Base Layer

Communications leaders across the country consistently describe the same challenge: keeping a pulse on what’s happening across schools. Major events are easy to track, such as athletic championships, large theatre or concert performances, board recognitions, and grant awards. But the daily classroom wins that quietly build trust are harder to surface–and those small moments matter!

When teachers regularly share classroom successes such as a creative writing project, a science experiment, a milestone achievement, or even a joyful themed dress-up day, they create consistency, demonstrate transparency, and reinforce partnership. Over time, those seemingly small updates build a reservoir of trust that becomes critical when more serious or complex conversations arise.

From a communications perspective, these moments also provide the most authentic storytelling opportunities. These reflect culture in action and humanize the district in ways that top-down announcements cannot.

Seeing What Families See

One of the most promising developments in district communication tools has been the ability to virtually “walk the halls.” With a tool such as ClassDojo for Districts, I have a real-time view of classroom activity across our schools—without requiring additional reports, extra submissions, or new workflows for teachers.

I can see all those little moments as they unfold. I’ve watched a student-written novel project take shape chapter by chapter. I’ve seen transitional kindergarteners celebrate the 101st day of school dressed as Dalmatians. I’ve spotted creative science experiments, collaborative art displays, and the everyday interactions that define the heart of a school community.

Instead of waiting for a principal to forward a story idea, I can see classroom learning and engagement in real time. Instead of missing a joyful milestone quietly happening in one grade level, I can elevate it while enthusiasm is high. Instead of relying solely on formal submissions, I gain organic insight into the daily experiences shaping our district’s culture.

For a geographically large district like ours, that virtual presence is especially powerful. I can stay connected to multiple campuses without physically traveling between them. The result is more timely storytelling, stronger relationships with our school sites, and a greater sense of cohesion across the district—because we’re not just sharing the big wins. We’re celebrating the small moments that truly matter.

Balancing the Narrative

Another overlooked benefit of classroom-level visibility is equity in storytelling.

High schools naturally generate attention through athletics, performances, and large-scale events. Elementary classrooms often have fewer headline-making moments, even though extraordinary learning happens there every day. Without intentional effort, district communications can unintentionally skew toward the most visible campuses and programs.

A districtwide classroom feed allows communications leaders to intentionally balance representation, across grade levels, across schools, and across student groups. It ensures that the joy of a transitional kindergarten celebration receives as much attention as a varsity championship.

From Duplication to Alignment

Many districts face communication fatigue, not because they communicate too much, but because they communicate across too many disconnected platforms–teachers use one tool, the district uses another, and emergency alerts are on yet another. Freestanding websites and social media just add more layers. Families are asked to download multiple apps and monitor multiple streams of information.

When district announcements are aligned with platforms families already check daily, engagement improves naturally. Leaders report stronger read rates and more immediate responses when communications build on existing habits rather than trying to create new ones.

Trust in a Complex Media Landscape

Today’s communications environment is increasingly complex. Social media policies continue to evolve, privacy expectations are heightened, and misinformation can spread quickly in public forums.

Closed-loop communication environments offer a compelling alternative for everyday updates. Families experience a familiar, feed-based interface, but one that is limited to their school community. Teachers benefit from appropriate oversight and documented communication histories. District leaders gain visibility without intrusion.

The goal becomes smarter communication, rooted in relationships that already exist.

Bottom-Up Strategy Is Sustainable Strategy

For communications leaders rethinking their approach, the most important shift may be philosophical. Instead of beginning with the question, “What should the district office send this week?” consider asking, “How are families already receiving communication every day?

”Strengthen that foundation first: support teacher-family communication; increase visibility into classroom moments; and use those authentic stories to inform broader district messaging. Then layer strategic announcements on top of that established trust.

When communication starts in the classroom and scales outward, it feels cohesive rather than imposed and relational rather than transactional. And in K–12 education, in which trust shapes everything from engagement to attendance to community support, that distinction matters.

District communication is most powerful when it reflects what families already see and experience daily. Sometimes, the most strategic move a central office can make is simply to walk the halls.