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.”

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

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.

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.

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.

Edcafe AI is a rare tool in the world of AI as it, unlike many others out there, is specifically designed to support classes through the entire teaching cycle.

Built for educators, this AI tool works to offer support across the full teaching process, from lesson planning to assessments -- all in one easy-to-access platform.

The idea here is to offer a more time-efficient way to work, without jumping around between tools. From building a lesson from scratch -- differentiating for mixed abilities -- to tracking student progress, this tool should streamline that process for ease.

This guide aims to lay out all you need to know about Edcafe AI for use in your class.

What is Edcafe AI?

Edcafe AI is a digital AI-powered assistant for teachers to help across planning, teaching, and assessing lessons. By combining multiple tools, this offers a single platform for teachers to create, organize, deliver, and evaluate learning.

This allows teachers to plan curriculum-aligned lessons and then create classroom-ready learning materials. Assigning these to students, and assessing as well as tracking progress, is also built into the platform.

That all should mean teachers only need to open this one tool to get everything done, saving on time and effort -- so they can focus on more valuable teacher-student time.

How does Edcafe AI work?

Edcafe AI combines the power of artificial intelligence with the materials already being used by teachers. Current and sourced materials can be uploaded for use, all being enhanced to work with the AI ecosystem.

What that actually looks like is a teacher uploads a document, slides, or curriculum content. The AI then generates lessons, quizzes, or activities that can be shared using links or QR codes. Students can then work with these materials as teachers track engagement and performance in real time.

Usefully, there are feedback loops that allow teachers to see how students are responding, so as to refine future lessons based on student ability.

Since students don't need accounts, this is all super simple to setup and share so students can begin working with the materials right away.

What are the best Edcafe AI features?

Edcafe AI uses artificial intelligence to make the creation of materials fast and easy from full lesson plans, slides, and quizzes to reading activities.

Chatbots can also be created, with custom inputs, which are then able to assist students as they work through topics. They then can get personalized feedback and help as they work, without it costing teachers on time.

Those quizzes that can be generated are also able to be auto-graded, saving even more teacher time. This can be a quick way to assess student ability with any learning gaps easy to identify and work on improving.

Since Google Drive can be used with this system, it makes for an easy way not only to access and enhance current materials, but to generate and share more across the school.

Everything is end-to-end here, so teachers can work in one platform to get everything done. This is not only helpful for saving on time, but can also be far less mentally straining.

How much does Edcafe AI cost?

Edcafe AI offers a tiered pricing model, including a free option. The plans break down like this:

Free plan: Limited AI generations and core tools.

Pro plan: From around $7.99/month (annual billing) with expanded features.

Premium plan: From around $14.99/month with unlimited usage and school-level tools.

Custom options are also available for schools and districts, including onboarding and integration support.

Edcafe AI best tips and tricks

Start with your own materials
Upload existing lesson content to get more accurate, curriculum-aligned outputs.

Use chatbots for differentiation
Set up different chatbots for varying ability levels or topics to personalize support.

Generate then refine
Treat AI outputs as a starting point and tweak for tone, accuracy, and class context.

The transition from traditional instruction to AI-enhanced learning is often met with a mix of excitement and trepidation. However, for Dr. Gyimah Whitaker, Superintendent of the City Schools of Decatur (CSD), the path forward isn’t about racing to adopt the newest tool. It’s about moving “slowly to go fast”—building a foundation based on intentionality, policy, and, most importantly, the amplification of student voice.

Located just outside Atlanta, CSD serves a population of 5,400 students. The district faces a unique challenge: it was identified by Stanford University as having one of the greatest wealth achievement gaps in the nation. For Whitaker, integrating AI is an equity imperative.

To ensure all students graduate ready for a world that includes AI, leadership must be thoughtful, secure, and collaborative.

Moving Slowly to Go Fast: A Policy-First Approach

While many districts might jump straight into purchasing AI-driven curriculum tools, Whitaker and her team took a step back to focus on the framework. In collaboration with the national ILO Group, CSD chose to prioritize AI from a policy standpoint rather than just a curricular or workforce one.

“We have come along slowly because we have the safeguards in place around privacy for students and student data and what we call the 'Swiss cheese' for cybersecurity,” says Whitaker. “But at the same time, we understand our students need to graduate ready to engage in the world, and the world does include AI."

By focusing on policy first, the district ensures that safety and privacy concerns—raised by teachers, parents, and students alike—are addressed before tools are deployed at scale. This intentional movement allows the district to vet filters and privacy protocols, creating a secure environment in which innovation can eventually thrive without compromising data integrity.

Fostering a Symbiotic Workforce

Integrating AI also requires a shift in how the district views professional development. Whitaker envisions a symbiotic relationship between the digital natives who are more comfortable with AI entering the profession and the veteran educators who hold deep pedagogical expertise.

“My greatest hope is that we will begin to recognize that our newest teachers are able to help us and our veteran teachers are able to grow," says Whitaker. This reverse mentorship model acknowledges that while AI can assist with lesson differentiation or administrative tasks, it cannot replace the human element. Even as the district explores AI agents for cybersecurity and administrative support, Whitaker remains a staunch advocate for human connection, noting that while an AI might manage a calendar, it lacks the emotional intelligence to know when a colleague needs a break or a little encouragement.

3 Tips for District Leaders Implementing AI

For administrators looking to begin their own AI journey, Whitaker offers three pillars of advice:

• Don’t Be Afraid of the Unknown: “Understand that you don’t know what you don’t know,” she says. “This is a work of unknown. But that’s okay. Be comfortable in that.”
• Lean into the Tension: Every district feels the pull between the benefits of AI and the doom scroll of digital distraction. Use that tension to spark honest conversations with stakeholders.
• Identify Your Entry Point: Whether you start with a curricular focus, a workforce support strategy, or a policy framework, ensure it aligns with your district’s specific needs.

A National Stage for Student Voice

One of the most innovative aspects of CSD’s strategy is the elevation of students into high-stakes leadership roles. By appointing both a Student Representative and a Student Apprentice to the School Board—a rarity in Southern school systems—CSD has sent a clear message: students don’t just observe; they lead.

This commitment to student agency served as the perfect prerequisite for an invitation from the National Superintendent Association (AASA) to join the National Leadership & Innovation Fellowship Focused on AI and the Future of Learning. Hosted in partnership with Day of AI and MIT RAISE, this fellowship invites designated superintendents from every state to receive cutting-edge AI training at MIT, and allows them to bring two students to the Edward M. Kennedy Institute in July for an immersive experience focused on ethical AI use, systems-level leadership, and policy.

The Edward M. Kennedy Institute features a full-scale replica of the U.S. Senate chamber. Participating students will act as “senators” for their respective states. Their objective? To develop a National AI Policy.

To prepare, students are completing 8 to 10 hours of pre-work, analyzing artifacts that the Institute will synthesize into a working template. This sets the stage for an authentic legislative process, mirroring the deliberation of the Senate.

The resulting student-developed policy will be distributed nationally to all superintendents by AASA. This model holds the potential to be replicated in local districts, providing a framework for involving parents and stakeholders in governing this powerful technology.

“I think the most amazing part about this event is I honestly have no idea what the kids are going to come up with,” says Jeff Riley, Executive Director of Day of AI and former Massachusetts Commissioner of Education. “Kids are digital natives. This is an opportunity where their voices can really be heard as leaders in the field.”

Whitaker was thrilled to receive the invitation, viewing it as a natural extension of CSD’s core mission. “The stars really aligned,” she says. “This experience will take the work we do at CSD to elevate student voice to the next level."

By giving students a seat at the table both locally and nationally, CSD ensures that AI policy isn't just something done to students, but with them. This leadership experience ensures that the district’s Portrait of a Graduate includes the ability to navigate and actively govern the technologies of the future.

As Whitaker says, the ultimate goal is both simple and profound: “To ensure you’re graduating students who are ready to engage in the world knowing that AI is there.”

A recent Sports Illustrated article described how some high school football players are using artificial intelligence to fabricate highlight plays in recruiting videos. Generative tools can now insert touchdowns, tackles, or explosive runs that never actually occurred, creating a misleading picture of an athlete’s performance.

At first glance, the story reads like another example of technology being used to gain an unfair advantage. But in many cases, these videos are not as convincing as students might think.

Experienced recruiters, coaches, and even knowledgeable fans can often tell when something is off. The flow feels wrong. The movement does not match the context of the game. What appears impressive at first glance can quickly raise questions.

The real problem is that students may not realize how easily these videos can be spotted. They may overestimate how convincing these tools are and underestimate how quickly credibility can be lost. When that happens, it is not the technology that carries the consequences, but the student–and their integrity.

The Stigma of Consequences

To be fair, authenticity challenges in sports are not new. Highlight reels have always been curated, with athletes and coaches selecting the best moments, angles, and plays. Recruiting videos have never been neutral.

At the same time, experienced evaluators bring a trained eye to the process. They understand the rhythm of a game, the timing of movement, and the mechanics behind each play.

Manipulated clips raise immediate suspicion as it is not that hard for trained adults, and even knowledgeable fans, to recognize one. And when one is exposed, students are the ones who carry the consequences. The loss of trust, the damage to reputation, and the stigma of being caught can follow them well beyond a single video.

In that sense, integrity and reputation are not secondary concerns, but the foundation of the entire process.

Discouraging Shortcuts

AI did not create this challenge, but it has made it easier to act on poor judgment. What once required significant effort can now be done quickly and with little technical skill. The tools are more accessible, and the temptation is greater.

And that makes the response even more important.

Since I am not a recruiting expert, my suggestions here may be overly simple or not fully informed. Still, it seems reasonable that technology could help protect integrity. For example, recruiting platforms or video services might use AI tools to detect manipulated footage or verify highlights against full game recordings uploaded by schools. If AI can generate fabricated highlights, it can also flag these or confirm authentic ones. Even a basic verification layer could serve as a deterrent. Technology alone, however, is unlikely to solve the issue.

Coaches and athletic programs play a critical role in shaping how athletes understand the purpose of highlight reels. AI can be used to analyze performance, break down game film, and identify areas for growth, which support student development. Fabricating plays does none of that, so helping athletes understand that distinction is part of the work.

The cultural layer also cannot be ignored. Highlight videos now live on social media, where attention can become its own reward system. Likes, shares, and visibility can create pressure to produce more impressive content. When attention becomes the incentive, exaggeration can follow.

Coaches and programs can push against that pressure by reinforcing that highlights are meant to reflect real performance, not manufactured moments. Clear expectations and consistent messaging can discourage shortcuts before any take hold.

Opening Doors

Integrity in high school sports is not enforced by rules alone, but is shaped by culture, expectations, and the adults who guide young athletes. When those signals are clear, athletes are more likely to understand that how they represent their performance matters as much as the performance itself.

AI did not create the integrity challenge in high school sports, but has made the consequences more immediate and visible. The real question going forward now becomes if student athletes will understand how easily manipulation can be detected and what is at stake when it is.

In high school sports, performance may open doors. Integrity and reputation determine whether those doors stay open.

Google is attempting to position Gemini as a leader for AI tutoring when it comes to standardized test prep for the SAT and other tests. Earlier this year, they announced a partnership with the Princeton Review to allow practice tests followed by instant feedback from Gemini that can tell a student where they excelled and highlight areas they might need to study more.

The feature builds on Gemini’s learn mode. Previously called “Guided Learning,” this is a tutoring mode built on a collection of AI models called LearnLM that were specifically designed with teaching in mind and built with principles of education science.

Here’s a closer look at Gemini’s tutoring and test prep abilities.

Using Gemini To Prepare for Tests

For SAT prep, Gemini can generate full practice tests for free. All you need to do is enter a prompt such as, “I want to take a practice SAT test,” and Gemini will generate a test on it. This test will utilize resources from the Princeton Review, a test-prep company that draws on real SAT questions to generate practice tests.

In my experience, this feature works just as advertised. It took me only minutes to generate a quick practice test, and I also had the option of having Gemini create sample SAT questions. It quizzed me and offered encouragement when I got questions right and great explanations when I got questions wrong. This is a tool that seems like it would be helpful for students preparing for this test.

This type of access to high-quality practice tests and tutoring is a great way to increase access to better test preparation methods for people from all economic backgrounds as Gemini is available for free. I also suspect Gemini’s AI coaching could be helpful even for those who get one-on-one tutoring.

Google Wants Gemini To Be A Tutoring Leader

This recent foray into test prep is part of Google’s larger effort to make Gemini the go-to AI for tutoring. So far they seem successful in these efforts to me.

Gemini in learn mode has outperformed other similar tools in certain studies. For instance, researchers compared Google Gemini's and ChatGPT's effectiveness for enhancing the academic writing skills of English learners in two Thai universities. The study found “both tools effectively improved linguistic accuracy and essay structure,” however, “Gemini was found to work better than ChatGPT in multimodal feedback and source integration.”

This tracks with my own experience using both tools. While any AI tool has its limitations and subtle inaccuracies can be a big problem for test prep, Gemini in learn mode is the best AI tutor with which I have interacted. And Gemini continues to improve its tutoring features. In addition to the SAT test, Google recently announced Gemini can provide vetted tests for the Joint Entrance Examination, a standardized engineering entrance exam used in India. It also seems likely Google will expand Gemini’s data set to include other standardized tests going forward.

Overall, I’m once again really impressed with Gemini’s tutoring abilities. My graduate school options were somewhat limited because I was so afraid of taking the GRE that any program that required its completion was ruled out. It’s possible had a tool such as Gemini been available when I was younger, I would have felt differently, though I am very happy with the graduate program in which I ended up attending.

As regular Tech & Learning readers know, I have mixed feelings about AI. But I’m impressed by this feature. Using Gemini to prepare for standardized tests feels as if it could have been a game-changer for me personally and may indeed be a game-changer for many students today.

You can watch the video above or download/listen below.

Andrew Marcinek remembers the moment social media stopped feeling like a community. It was around 2015 and 2016, when he was finishing a stint at the U.S. Department of Education and coming back into schools. He was also headlining Tech & Learning events. I’ve been lucky enough to keep in touch with Andrew since then. He continues to keep a keen eye on this incredibly complicated and harrowing landscape.

His personal reckonings from that time became the foundation for his book Teaching Digital Kindness, and it's the lens through which he's watching the current wave of lawsuits against Meta and other social media companies. For Marcinek, these cases aren't a surprise — they're a long-overdue reckoning. The comparison to Big Tobacco, he argues, is apt: years of denial, a product engineered to be addictive, and mounting evidence of harm to the most vulnerable users.

The burden of dealing with that harm, he notes, has fallen almost entirely on schools — institutions that were themselves manipulated by the same algorithms and given no roadmap for what to do.

Marcinek's prescription isn't a phone ban or a screen purge. It's intentionality — building technology programs in schools with deliberate purpose, communicating transparently with parents about how and when devices are used, and treating the current AI moment as a second chance to get the foundational work right.

"Schools need to lean in and have conversations around AI," he says. "If you're not, you are doing your school community a disservice — sending kids out into the world without knowledge of probably one of the most powerful technologies in anyone's lifetime."

He points to employer signals as the clearest argument for AI literacy: companies, including Microsoft and Anthropic, are explicitly looking for workers who understand how to use AI alongside strong soft skills. Schools that treat AI primarily as a cheating threat, Marcinek contends, are asking the wrong question — and risking the same institutional failure that defined the social media era.

A second book, tentatively titled Untangled, is in progress — it focuses on how individuals and institutions can find their way out of the algorithmic knots of the last two decades. I’m looking forward to it.

Artificial intelligence has rapidly shifted the instructional landscape. Tools that can generate explanations, draft essays, and summarize complex topics are now readily available to students. This accessibility has led some to question whether deep instructor content knowledge still holds the same importance. The answer is an unequivocal yes.

AI amplifies access to data, but it does not replace the need for human expertise to validate, structure, interpret, assess, and model disciplinary knowledge. In many ways, the instructor’s role becomes more dependent on strong content knowledge because they serve as the critical mediator between AI-generated information and meaningful learning. Many generative AI tools even articulate it at the end of a response, such as what Google’s Gemini recently told me: "AI may provide inaccurate information, so double-check its responses.” It is not a piece of window dressing; it is a sincere and serious warning.

For instance, in a recently published text about using AI in the classroom, I found an example in which AI was comparing the concerns around excessive civilian deaths in Gaza in 2024 with those at the Battle of Gettysburg. The example made it through the editorial process, but it lacks historical analysis. Someone with a strong historical knowledge base would know that, though Gettysburg remains the bloodiest battle in the Civil War and the Civil War was the bloodiest war in American history, only a single civilian death has been documented. Many historians articulate that the number is most likely underreported, but Jennie Wade remains the only documented civilian death in a three-day battle that caused 50,000 military casualties. The concerns about Gaza included that the military and civilians were often interspersed, and most of the fighters were not regularly uniformed troops; therefore, opposing troops were often unsure of their enemy. This was not an issue at Gettysburg, a battle between two regularly uniformed and disciplined armies, making the AI comparison factually inaccurate.

Information Itself Does Not Equal Understanding

One of the most persistent misconceptions in AI-rich environments is the assumption that access to data equates to understanding. AI tools can produce fluent, coherent responses across nearly any subject area. However, these outputs are fundamentally probabilistic, often reflecting patterns in data rather than verified truth or disciplinary consensus.

Students often lack the background knowledge needed to distinguish between accurate synthesis and plausible error. Without a strong knowledge base, they are vulnerable to accepting AI output at face value, which creates a new instructional imperative. Teachers must understand their content deeply and be able to identify subtle inaccuracies, omissions, and misrepresentations in AI-generated material.

Content knowledge becomes the filter through which AI is evaluated. Without that filter, both instructors and students risk confusing fluency and responsiveness with accuracy.

From Content Delivery to Knowledge Architecture

As with the advent of previous technologies, it seems that some believe AI will reduce the need for instructors to function as primary sources of information delivery, not unlike how a speaker at the opening of Heidelberg University in 1500 anticipated teachers being replaced by books. Obviously, that has not come to pass, and instead, new sources of information delivery have reframed the role of the teacher.

Effective instruction now centers on the design of coherent knowledge systems. Strong instructors sequence content intentionally, emphasizing foundational concepts and building toward more complex applications. This structured progression is not something AI inherently provides. AI delivers fragments, and instructors need to construct the architecture for the overall arc of any content. Fully understanding vertical and horizontal curricular alignment (how courses fit into the bigger picture) is essential to effective instructional development.

Students require guidance in understanding what matters within a discipline, how ideas connect, and why certain knowledge is foundational. These decisions depend on deep familiarity with the field. In this sense, content knowledge is about knowing how knowledge within a given subject is organized.

Interpretation and Context in an AI Environment

AI responses often lack context and may provide correct information but fail to situate it within appropriate contexts. This limitation becomes especially significant in disciplines in which interpretation is central.

Instructors with strong content knowledge bring nuance to the learning process. They can explain competing perspectives, highlight areas of debate, and connect ideas to real-world applications. They can also help students understand when AI outputs oversimplify complex issues. This interpretive role is essential and ensures that learning moves beyond surface-level engagement and into deeper disciplinary understanding.

Assessment in the Age of AI

Assessment practices are under increasing pressure in AI-rich classrooms. Traditional assignments that rely on recall or basic synthesis are easily completed with AI assistance.

This reality demands a more sophisticated design. Instructors must create tasks that require application, judgment, and contextual understanding, which are areas in which content knowledge is indispensable. Designing meaningful assessments requires a clear sense of what constitutes deep understanding within the subject area.

Evaluating student work also becomes more complex as instructors must distinguish between superficial correctness and genuine comprehension. This distinction is only possible with a strong grasp of the content. AI raises the stakes of assessment since it makes shallow tasks obsolete and elevates the importance of disciplinary expertise.

Modeling Disciplinary Thinking

Students do not just need information but also to learn how experts think. AI can generate answers, except it does not model the reasoning processes behind those answers in a way that supports learning.

Instructors play a critical role in making their thinking visible by demonstrating how to approach problems, analyze evidence, and construct arguments within a discipline.

This modeling extends to the use of AI itself. Students must learn how to question AI outputs, refine prompts, and evaluate responses–all skills are grounded in content knowledge. Without this, students cannot effectively engage in this process.

Equity and Access to Knowledge

The implications of content knowledge extend beyond instructional effectiveness and are deeply connected to issues of equity. Students who enter classrooms with a strong background knowledge are better positioned to use AI tools effectively. They can ask better questions, recognize stronger answers, and build on existing understanding. Students without that foundation are more likely to rely on AI passively.

This dynamic risks widening existing gaps. Knowledge-rich instruction becomes a critical lever for equity by ensuring that all students develop the background knowledge necessary to engage meaningfully with AI and other information sources.

Instructors’ expertise allows them to identify essential knowledge and ensure it is taught systematically and coherently.

The Reinforced Role of the Instructor Moving Forward

The net effect of AI integration is a reinforcement of a teacher’s role, as it increases the premium on content knowledge because it raises the stakes of interpretation, validation, and meaningful use. Instructors are now becoming validators of accuracy, designers of knowledge systems, interpreters of complexity, and architects of assessment. The presence of generative AI makes these roles more visible and more critical.

Educational conversations about AI often focus on tools, policies, and academic integrity, but none of this should overshadow a more fundamental truth: Strong instruction in an AI-rich classroom depends on strong content knowledge.

Investing in teacher expertise is essential. Professional learning, curriculum design, and instructional planning must all prioritize deep disciplinary understanding. Technology will continue to evolve, and as it does, strong teachers remain key to effective instruction.

A recent analysis from Stanford University looked at more than 150,000 prompts from more than 4,400 K-12 teachers interacting with AI. The research provides a rare look at how teachers are actually using AI in practice, and most of these uses are fairly positive.

Teachers, the research suggests, are generally using the tool to help them build curriculum content and as a sounding board, all with an eye toward better helping students.

The research was conducted in partnership with SchoolAI, an AI-powered educational platform. Though it was based on data from Fall 2024, it still provides a compelling snapshot of how, where, and when many students are turning to AI.

Cynthia Chiong, Principal Research Scientist at SchoolAI, recently shared some of the highlights of the research with me.

1. Ways Teachers Are Using AI: More Than 40% of Teacher Prompts Focus On Curriculum

Chiong says this is a good sign, as it suggests teachers are using the tool to explain learning and not just automate tasks. In separate conversations with educators, she has found that many of these efforts are geared toward personalizing learning for students.

"For example, these three students here really want to focus on the sun,” she says. "While these other students are struggling with something else or are interested in another topic."

Chiong says that for a lot of teachers, being able to meet all these students where they are is the biggest draw of using AI.

2. AI As A Sounding Board

The research found that about 1 in 7 prompts involved a teacher reflecting or processing without making a specific request. This includes sharing a frustration, thinking through a challenge, or working something out.

Chiong says that these types of conversations and others teachers had with AI show that it’s an active process for educators. They might start with a question such as, “'How might I assess this math project that we've been working on?' but then they immediately go to a next thread of, ‘Okay, now, how do I put this back into a lesson to teach?’” she says.

Chiong adds, “So it's not like a one-and-done, but they're really thinking through the trajectory of learning for their students, and that was really cool to see.”

3. More Than 50% of Prompts Ask It To Generate Materials Such as Lesson Plans, Assessments, or Feedback

Teachers obviously find AI helpful when it comes to generating class material, but Chiong suggests that the data also shows teachers are not just plugging in a prompt and settling for the first output.

“The teachers are really driving the content,” she says. “It's not that the AI is like, ‘Oh, here is a lesson, this is how you should teach it.' But rather a teacher is saying, ‘Hey, I really want to teach something about evolution or whatever,' and that idea of how to teach it is still coming from that teacher entering that prompt.”

4. Half of Teacher AI conversations Include Fewer Than 10 Prompts

Teachers, by and large, are keeping their conversations with AI short, sweet, and to the point.

“They have a good sense of what they want to teach, and they have the curriculum already,” Chiong says. “So it's more a matter of translating what they already do into this new AI platform, and so it shouldn't take all that much time, and it is meant as more of a time saver and not a time taker.”

As artificial intelligence moves from a futuristic concept to a fundamental classroom tool, education leaders face a dual challenge: harnessing its immense potential while safeguarding the students they serve.

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WITH THANKS TO OUR PARTNERS

Syracuse University has gone all in on the AI revolution, deploying Claude AI to 30,000+ students, faculty members, and staff. Along the way, school leaders say they've developed effective AI use cases both in the classroom and beyond, ranging from test practice to course schedule management.

Syracuse University Chief Digital Officer Jeff Rubin, and Andrew Joncas, Assistant Vice President for enterprise data and artificial intelligence, recently shared how they were able to develop effective uses for AI, get student, staff, and educator buy-in, and talk through and work to rectify some of the concerns around AI use at their institution.

Using AI To Increase Text Scores By Adjusting Questions

Rubin knows firsthand that just adding AI to the classroom doesn't always enhance learning. When he first started using Claude AI himself, he had the idea of uploading his recorded lectures and then having Claude generate practice questions for students. The thinking behind this strategy was sound as he teaches an intro to information technology course with several hundred students enrolled in it. “Personalized learning is always hard, and really trying to meet students where they're at is challenging in a large class,” Rubin says.

Students have long wanted detailed and varied practice test questions, but creating enough questions to meet the demand was a challenge. “Without AI, it is actually really hard for my brain to come up with thousands of different ways of asking questions,” Rubin says. But once he shared his previously recorded lectures with Claude AI, the tool was able to generate a virtually unlimited supply of multiple choice questions and work through them with students.

In theory, this seemed perfect. However, in practice, when Rubin surveyed his students, he found that those who were extensively using Claude AI to study weren’t doing any better than the others. After meeting with members of Syracuse University's education department, Rubin realized that this lack of results was likely because Claude AI was asking students multiple-choice questions that didn't require them to think. He reworked his prompt to Claude AI so it would ask short answer questions and then engage with students about what they got wrong, all drawing from his lectures.

“It'll throw out a term that we've talked about in class in the form of a question and ask, basically, what do you know about this?" Rubin says. "Instead of just picking an A, B, C, or D, the student would type out their response. And then what Claude does is it grades the response and basically says, ‘Hey, this is a B type of response. Here’s the things you got right,’ but Professor Rubin also said, ‘These are the things you should know about.’”

This time Rubin saw an immediate impact with initial exam scores jumping by 12 points on average. While he just started using the system this semester with students, he believes the positive trends he has seen so far will continue.

Other AI Uses and Buy-In

Other uses for AI at Sryacuse include Clementine, a recently launched course search tool powered by Claude Opus. This system queries millions of rows of institutional data in real time and provides results tailored to each student’s schedule, grades, and goals. Another tool the university also deploys allows staff members to efficiently search for potential data about university donors.

The university has also launched a pilot program using Claude Code, Anthropic’s agentic coding tool. Joncas says they’ve been surprised and pleased by the interest in this tool from various members of the university community.

He adds that while sometimes there is pushback in using AI among coders who take artistic pride in their work, he says meeting with stakeholders and showing them what the tool can do often alleviates these fears. He’ll often demo it by having Claude Code quickly create a tic-tac-toe game.

“It sounds ridiculous, because nobody needs to make tic-tac-toe. It's already been made a thousand times,” he says, but the demo shows people the tool’s potential. “They see the light and they go, ‘Oh, I could do that now.’”

Handling Academic and Environmental Concerns

AI ethics is a big topic these days, from concerns about the technology’s environmental impact to potential misuse and AI plagiarism from students and faculty members. Rubin and his team have invited conversations around this at various AI training events, including some focused on AI at work more generally and others specifically built around AI and teaching pedagogy.

“We also started a whole AI governance, which is led by somebody in IT but is inclusive of folks from around the university,” Rubin says. “We talked about things like AI and mental health. We talk about AI and data privacy. AI and job loss, AI and sustainability, all things that are big, deep topics.”

As far as concerns around inappropriate AI use by students, right now Syracuse University syllabi include a statement on AI that essentially is either you can use AI in this class, you can't use AI, or you can use it under these circumstances.

“That, to me, is a band-aid. It's not an answer,” Rubin says. “The answer is, 'How do we think about AI from a pedagogy standpoint?'” He adds, “If you integrate AI from a pedagogical standpoint, we're going to have fewer of these concerns."

In that vein, instead of having students write essays on some aspect of World War II, he'd like to see more active assignments. For instance, "'You are a commander of this unit who went into this situation in World War II. What would you do?’” he says.

In the meantime, Rubin also advises faculty members to be forthright with how they are using AI.

“We have a responsibility to let students know if we're using AI. Are we using AI to generate our lectures? Are we using AI to generate our exams?” he says. “I don't think there's something wrong with it. I just think there's a level of transparency that is needed to show, 'Hey, we're still owning this, but I'm using AI in certain aspects of it.’”

AI enthusiasm amongst educators hasn’t evaporated, but it's not as intense as it once was, says Carl Hooker, educator and author.

The former district tech director regularly speaks to educators around the country (often during Tech & Learning regional leadership summits) and polls them on their feelings about AI. He says that while school leaders are still interested in the potential of AI, they are being more cautious about how they are spending money on AI products. They are also more concerned about AI's impact on the environment.

In many ways, this waning enthusiasm is familiar to educators from previous tech innovations. It follows the Gartner hype cycle, a graphic of how at first a new technology is embraced, then people pull away from the tool when it doesn't quite live up to the promise. Ultimately, society learns how to best make use of the new technology.

With AI, Hooker says, there was initially intense excitement. “I think we hit that peak, probably, in the summer of '24; now we're in what they call the zone of disillusionment,” he says. AI’s limitations are becoming clearer, and educators are realizing that simply throwing AI at various problems is not always an effective solution.

On top of this, financial analysts are increasingly warning of a potential AI bubble burst based on the scale of investment and theoretical limits to the return on that investment. Time Magazine recently noted that analysts predict more than $5 trillion being invested in AI by the end of the decade, a staggering number that far outpaces the AI industry’s profits. For instance, Time Magazine notes, "OpenAI and Anthropic have annualized revenues of about $25 billion and $19 billion."

AI will be a part of education going forward, but the ways schools use it and engage with it is evolving, says Hooker, who is a proponent of using AI in ways that support deep learning.

The AI Bubble: Fewer Screens and Less Screen Time

One reason some districts are focusing less on AI, Hooker suggests, is funding. “We just ran out of all of our ESSER funds a year-and-a-half ago to replace all those devices that we bought, so now we have to figure out what to do with that,” he says. “Schools are shifting away from one-to-one.”

Beyond funding realities, there is a growing effort in many states and districts to prevent students from using their phones during school time. “Cell phones have been banned in 21 states,” Hooker says. “So now you're saying we don't have money for more devices, plus you can't bring your own.”

Generative AI went mainstream at the tail end of Covid, just after most districts had invested in one-to-one devices, which allowed schools to implement the technology in ways they wouldn’t have been able to otherwise, Hooker says. “If that doesn't happen and generative AI happens, we're like, 'Oh, it's cool. The high school kids get to use it.’”

The Future of AI In Schools

The AI hype cycle reminds Hooker of previous hype cycles around other technologies.

“I've lived through a couple of different eras with Web 2.0 and then mobile devices. I was a tech director when all of those came out, and everything was the App Store, and everybody was buying apps; it was this huge explosion of everybody spending money on that,” Hooker says. “Then it went from that to software as a service, and everybody needed to get subscriptions to everything, which is kind of where we're still at."

He notes that past tech bubbles never burst, "But they definitely deflated.”

That’s what he sees happening with AI: It will remain a thing in education, just not necessarily THE thing.

“It doesn't mean there's not going to still be adaptation, because there's still lots of schools that haven't adapted it at all,” he says. “I just think the fanfare and the buzz and the hype are probably way past their peak, and we're going to eventually get to a settling point.”

What that will be like is anyone’s guess. Hooker notes the field is still discovering what AI “looks like in a highly impactful way when it comes to learning. So we're still kind of figuring that out, because we're all learning this at the same time.”

In other words, AI is here to stay in education, but the AI gold rush in education may have run its course.

AI hype keeps getting bigger and the offerings more complex. It’s being pitched to schools from every direction, but what really matters when it comes to choosing the right tool for your school?

Some AI tools promise to reduce teacher workload while others claim to revolutionize tutoring, feedback, or family communication. You’ll also find platforms that use AI focused on district operations, reporting, and data analysis. The common thread is that these all look great when demoed–believe the hype, and you’d think they could transform a school overnight into some sort of tech-enabled teaching utopia.

And this is why it’s important to slow down, ask better questions, and focus on the practicalities.

Sure classic questions such as which platform is best or what are other schools using are fair, but not the right place to start. Schools need the right fit for their students, staff, systems, and priorities. That means being more pragmatic.

So before investing in AI, school leaders should ask different questions. What problem are we actually trying to solve? Who will use it? What data will it store? Do we have the capabilities to use it effectively? How much control will we have? What happens if we get this wrong?

Those questions matter more than the flashiest demo.

Start with the problem, not the product

The first question is the simplest: What do you actually need an AI for?

Identify your goals. Do you want to reduce teacher workload, support tutoring, help teachers adapt materials for different students, speed up admin tasks, improve family communication, or analyze attendance or grades more effectively?

If leaders can’t explain the problem they’re trying to solve, they’re not ready to buy a solution.

That seems obvious, but it’s an easy mistake to make. A platform may be enticing because it generates lesson ideas in seconds or gives polished responses on demand. But if it doesn’t meet staff and student needs, it will become underused and hard to justify.

Build practical use cases first and ignore the peer pressure to bring in AI without good reason.

Ask whether your current ecosystem already covers most of your needs

If your district is already invested in Google, Microsoft, ChatGPT, or another approved environment with AI capabilities, then take stock before adding more tools.

This matters because AI doesn’t exist in a vacuum. It can sit within all sorts of systems from identity, email, and documents to permissions, policies, and support structures. If you already have an approved AI in those systems, then it’s an opportunity to discover what features and functions you might not be using to full potential yet. Identify the gaps that will add real value before considering new solutions.

Adding more tools without a clear rationale can lead to overlap, confusion, and needless costs.

Be clear whether this is for the classroom or the district office

That said, a single AI tool may not be sufficient to meet all your needs. Not every school AI tool does the same job, and schools should be wary of products that claim to do everything brilliantly.

Some tools are geared toward teaching and learning–think MagicSchool, Khanmigo, or teacher-facing uses of platforms such as Google’s Gemini, Microsoft’s Copilot, or OpenAI’s ChatGPT.

Others sit much closer to school and district operations, such as PowerSchool, SchoolStatus, attendance tools, reporting systems, family communication platforms, and products built around data rather than classroom practice.

This distinction matters. A tool that works well at a district level may offer little in the classroom. An AI that teachers love to use in class may be a headache for IT and governance.

Before buying, ask the simple question: Is this mainly for teaching and learning, or for operations? That will help you refine your list of candidates.

Ask whether staff have the knowledge to use it well

School leaders need to think beyond a few professional development sessions when it comes to embedding a new AI tool into existing systems and workflows.

A school can buy the perfect AI tool and still struggle if staff lack confidence or digital skills. Be honest about whether teams and individuals have the basic knowledge to use the tool well, question its weaknesses, and stay within policy.

That includes knowing:

• What the tool is good at
• When not to trust it
• What data should never be entered
• How to spot weak, biased, or invented outputs
• When human review is essential

This isn’t just about knowing where to click, but about digital and AI literacy, critical thinking, and understanding AI’s limitations. This will be an ongoing task as new releases and updates will regularly offer new functionality but also potentially new issues.

It’s important to question whether you have in-house knowledge and discipline to use AI responsibly. If your school has clear guidance and confident staff then it will get more value from AI than one with patchy, uncertain usage.

That question applies differently across the organization. Senior leaders, classroom teachers, support staff, IT teams, and students don’t all need the same depth of knowledge, but they do all need enough understanding to reduce risk and use the tool effectively.

Ask data questions early

Privacy and data use needs to be included from the start.

Schools should ask:

• What data goes into the tool?
• Where is that data stored?
• Is it used to train the model?
• How long is it retained?
• Who can access it?
• Can it be reviewed and deleted?
• What visibility do administrators have?

These aren’t just compliance questions, but trust questions as well.

If staff aren’t sure what they can safely enter into a system, adoption becomes inconsistent very quickly. If leaders can’t explain how the tool handles student data, it becomes much harder to build confidence among teachers, families, and other stakeholders.

AI tools can look simple and user-friendly, but schools still need to understand what’s happening behind the scenes before they commit, especially since student data in these tools is extremely sought after by hackers.

Take extra care if the tool analyses student data

The appeal of AI for some schools goes beyond lesson planning. It’s the potential to spot patterns in attendance, grades, behavior, or assessment data more quickly than staff could on their own. In theory, that means spotting issues earlier and acting faster.

That may be useful, but it’s also one of the riskiest areas.

Schools should ask:

• What data is being analysed?
• How reliable are the patterns or predictions?
• Is the tool flagging signals for staff to review, or is it making clear recommendations that teams might feel pushed to follow unquestioningly?
• To what extent does it reinforce biases or overstate weak correlations?
• How transparent is its reasoning and confidence, particularly when it comes to suggestions?
• Who is ultimately accountable if the AI flags something incorrectly or misses a student who needs support?

While an AI can help surface useful signals about attendance, attainment, intervention, or student support, it should not sit in a technological black box. Human professional judgment still matters. In fact, it matters even more once AI is involved as school leaders need to be clear about where that line sits and ensure an overreliance on tech doesn’t lower standards.

And this is where schools need to be especially careful not to confuse pattern detection with judgment. For example, some AI detection tools used to check whether work has been generated with the aid of AI can be extremely aggressive, leading to a much higher percentage of false-positives than vendors claim.

Look at real-world accuracy, not just the demo

Which leads us into claimed accuracy. AI tools can seem utterly authoritative until you question outputs, which can be unreliable in the situations schools care about most.

The real question is whether the AI meets the school’s requirements. Can it help teachers draft useful classroom materials? Support student revision? Summarize factual information without adding hallucinations or confusion? Can it save time without creating extra checking work?

Schools should also think about the cost of mistakes.

An AI-generated worksheet with a few weak questions may be inconvenient. A tool that mishandles attendance information, behavior summaries, grading, special education support, or safeguarding-related communication creates a much more serious problem.

That’s why buying decisions should be informed by realistic testing. Schools should test tools against real-world scenarios and decide when human oversight has to remain absolute.

Ask whether it improves the work or just speeds it up

Don’t judge AI by how quickly it completes tasks. Instead, ask whether it improves the quality of the work, supports better decisions, and reduces pain points, especially in areas of low-value admin and bureaucracy.

A tool that saves time on paper may still be a poor fit if it encourages generic planning, weak feedback, or an overreliance on samey AI-generated drafts. The risk isn’t just introducing minor mistakes into lesson plans; it’s a gradual reduction of originality and human judgment.

AI should be there to either improve current work or reduce repetitive or time-consuming admin tasks. What it shouldn’t do is replace real value, expertise, subject knowledge, or meaningful interaction with students. Faster isn’t better if the work becomes thinner, flatter, and less human.

Check the admin controls before the marketing claims

One of the least glamorous parts of AI procurement is also one of the most important: control.

Before signing for anything, anyone buying an AI should ask:

• Who can use the tool?
• Can access be limited by role or age group?
• Are staff and student experiences separated?
• What is logged and flagged?
• What can administrators turn on or off?
• How does identity management work?
• Does it fit with existing sign-on and rostering systems?

A strong school AI product isn’t just one that produces impressive outputs, but one that leaders and IT teams can actually manage.

Think beyond the pilot

Many schools are willing to trial new tools. The harder question is what happens afterward.

How much training will staff need? What guidance will be required? Will policies need updating? How much support does the vendor provide? What will this look like after one term, not one week?

A successful rollout goes beyond choosing the right platform to whether the school has the capacity to use it well over time.

If a tool requires constant explanation, provides unreliable results, or adds another layer of work for already stretched staff, its value may look very different once the initial excitement fades.

Look at the full cost

So you’ve asked all the right questions and made a choice. Now you need to understand the full cost of the solution, and for that you need to look past the headline license fee.

Some costs will be monetary, others related to time and effort. Training, integration work, admin time, support, security review, policy development, and the time staff spend learning a new system should all be considered. A low-cost tool can end up feeling much more expensive if it adds unnecessary complexity or doesn't end up delivering value.

One of the most useful procurement questions is: “What will this cost in practice over a year, not just on paper at the point of sale?”

Ask what happens if you do nothing

This may be the most overlooked question of all, but before buying an AI, schools should ask: “What would happen if we didn’t buy this?”

Would an existing tool already cover much of the need? Could a simpler process change solve the same problem? Is the issue actually about training, workflow, or staffing rather than missing technology?

This question helps schools test whether the purchase is genuinely necessary or simply feels timely because AI is dominating the conversation.

What happens when schools don’t ask these questions?

When schools rush into AI purchases without asking the harder questions first, the same problems tend to appear: You end up with a solution looking for a problem; staff use it inconsistently; the benefit is overstated; data risks are misunderstood; confidence drops.

The most awkward outcome is that a school becomes dependent on a tool it doesn’t fully trust or understand. By the time the limits become clear, the system may already be embedded.

Leaders may also want to think ahead to related issues, such as parent trust, equitable use, academic integrity, and how they will evaluate impact over time. Those questions may sit beyond the initial purchase decision, but shouldn’t be an afterthought once a tool is already in place.

That’s why the early questions matter so much. These are not there to stunt innovation or slow change but to stop schools from making avoidable mistakes.

Since the early days of ChatGPT, educators have talked about how AI can be used as a writing tutor or coach that doesn’t write for students but helps them organize their ideas and clean up their efforts.

I liked this idea when I first heard it. I’d love to live in a world where every student has access to a high-quality writing instructor. But in practice over the past few years, I haven’t noticed my students' writing improving; it's been the opposite. While the grammar has gotten much better, the content has suffered.

Today, in my classes, on social media, and through many emails I receive, I see writing with fewer technical errors, but this comes at a terrible cost. Generally, our writing is more boring, less individualized, and conveys far less meaning than it did a few years ago. The cause is AI, of course, but it’s more than that.

I think many of us have uncritically bought into the idea that AI can be an effective writing tutor. I’m no longer convinced it can be, and here’s why.

AI Is Not Actually Good At Writing

Even though people are taking writing advice en masse from ChatGPT and other AIs, we all acknowledge, subconsciously at least, that AI is terrible at writing anything original. That’s why we call AI-generated content AI slop, and when we read something and say it sounds as if AI wrote it, we are not complimenting it. Why then are we asking it to give us extensive notes, revisions, and suggestions on our writing?

AI doesn't transform our writing into William Shakespeare; when it takes on the role of a writing tutor, it’s still that same unimaginative writer. The fact that so many people are taking advice from a robot writer whose writing they hate is just another example of the cognitive dissonance of our times, and I'm genuinely puzzled by it.

AI Doesn’t Partner With You, It Takes The Wheel

A common refrain is that AI can help a student or educator brainstorm or help clean up their ideas. In this way, it can work with you like a good writing tutor, providing instant and unlimited feedback on your work.

This is another concept that sounds okay in theory but doesn’t hold up in practice. Unlike a good writing tutor, AI has a tendency to take the wheel of your writing. Even models specifically tuned toward teaching tend only to start by asking questions before offering detailed examples of what your answer can look like. Often, these suggestions are bland and lifeless, but even if these were more creative, this process is hijacking the writing process.

AI Gives Too Much Feedback

Even if the problems mentioned above could be programmed out of future AI models, I’m increasingly convinced that having an on-demand writing coach on your shoulder isn’t a good thing.

Throughout my career as a writer, I’ve benefited from feedback from editors and mentors who have read my work and altered and strengthened it--productive struggle. Seeing how they changed and improved my writing helped me get better as a writer, and continues to help my work. For example, this article will be better than the one I initially submitted, thanks to changes made by my editor.

Some thought AI could mimic this process, but I think that’s a false comparison. If I asked my editor what I should write after each sentence, and he sat next to me and rewrote my work as I was writing it, I wouldn’t be deserving of the byline associated with this story, and I probably wouldn’t earn many more writing assignments. (Editor's note: You would not!)

Even If AI Was Better At Writing, It Still Wouldn't Make Our Writing More Engaging

The fantasy author Brandon Sanderson once explained the problems with using clichéd writing in a way that really stuck for me. I’m paraphrasing here, but the idea is that clichés are not inherently bad; in fact, the first time someone hears one, it is often a great way of describing something, which is why it resonates. The problem is that over time and overuse, it becomes stripped of its power and meaning. For example, the first time I heard the phrase "X is like Y on steroids,” it was descriptive and funny. By the ten thousandth time I heard it, I started to groan.

The same thing is now happening with any writing techniques AI uses regularly. Even if it's non-objectionable to begin with, it starts to be intolerable as it spreads everywhere and everyone is doing the same thing.

Right now, AI writing is lifeless and stale to begin with, but even if it improves its writing voice, without more variation in style and tone, any of its techniques are destined to become overused almost instantly.

I do think AI’s writing will continue to get better, but I’m skeptical it can create the kind of variation necessary to uniquely power the voices of a majority of writers on the planet. And without unique writing voices, I’m not sure why we’re writing in the first place.

Last fall, Hollie Sisk, Assistant Coordinator of Instructional Technology at Effingham County School District, attended the Chicago Google Leadership Summit with education heads from all over the U.S. While she found it enlightening to learn where other districts were in their AI journey and how they were using Google tools, one piece of the experience struck her as something to bring back to her own Southeast Georgia district of 14,500 students.

“They took us into a room of three or four stations, with Chromebooks ready to go,” says Sisk. “We rotated through, learning the basics. I just sat there and thought our teachers really need this. ‘How can we implement this in our district?’ I marinated on that and when I came back, I pitched it to our tech director. From there, it blossomed into the ECSD Gemini School.”

In ECSD’s Gemini School, attendees rotate across four sessions through focused 20-minute labs on Gemini, NotebookLM, Gems, and Gemini in the Classroom, allowing them to quickly experience real classroom value without overload.

“It's not just a traditional ‘sit and get,’” says Sisk. “It’s more about creating an environment where it clicks for every educator because they have just enough exposure to spark their curiosity, but not overwhelm them with a lengthy PD session.”

Sisk tries to keep the PD engaging and moving. Instead of having the presenters going from room to room, attendees have to boogie to each round of learning, literally.

“We play disco music in between transitions. It helps them reset and refocus,” says Sisk. “They are going from absorbing a lot of information in 20 minutes from Notebook LM to custom Gems. They really need to turn their brain off and then get it ready for the next session.”

The impact has been immediate and measurable. Forty-five participant comments included consistent themes such as, “I didn’t want to go to another PLC, but this was amazing!” and “This is the first time AI makes sense to me.”

Most notably, a 31-year veteran teacher — who self-identifies as not tech-savvy — was so energized by the experience that she recorded a ten-minute video expressing gratitude and shared that it was the most excited she had felt about teaching in years. This kind of shift doesn’t come from tools, it comes from intentional, learner-centered leadership.

“I knew these tools could help them, but I didn’t want to pull them into a drawn out traditional PD to learn how,” says Sisk, who was recently recognized for this and other efforts with an Innovative Leader Award. “We really wanted to make it an immersive experience — a hands-on experience like I took away from the Chicago summit. Ultimately, my impact when building this was to give teachers back the gift of time.”

3 Tips for Creating An AI Gemini School in Your District

Sisk offers advice for others who are interested in creating such a PD program in their district:

• Create a relationship with the curriculum to ensure buy-in from the top down. “I needed our curriculum department's trust that we could make this work,” Sisk says. “Don't just try to push it out, really build those relationships and get them on board to support you.”
• Be consistent in the marketing. “I'm partial to branding–I try to focus on that not only for our social media account, but anything we're pushing out,” she says. “I stay very consistent which helps with recognition and so teachers feel they are part of something special.”
• Make PD fun and engaging. Training doesn't have to be a traditional “sit and get,” says Sisk. “You can have movement. You can have 25-minute round table discussions and then get up and move to a different table and a different topic.”

Fuel FOMO With a Marketing Moment

“I knew if we did a really good job marketing this, we'd have bought their trust,” says Sisk. “The next time we roll out a program, they will say, ‘Hey, the last thing they did was really fun. I want to come.”

Sisk embraces that marketing mindset for instructional technology—one that reframes tech not as “another initiative,” but as a value-adding experience educators want to engage with.

“My mindset wasn’t, ‘Let's promote another professional development,’ it was more like a product launch,” she says. “One special ed teacher shared how much time she saved writing IEPs as well as writing success criteria and learning targets. Excited teachers are calling a few days later, ‘Look at this song we created with the students after the session!’ Even our athletic coaches loved the training, which speaks volumes because they often hate having to come to PD.”

Sisk consistently highlights instructional technology successes through social media, reinforcing a culture that celebrates innovation and progress.

“One of the most powerful pieces of this is learning from other teachers,” she says. “It's not technology or curriculum presenting the material, it's teachers and media specialists. April Platt, Crystal Mealor, Jack Sinopoli, and Nick Exely are a few of our rockstar presenters.To me, face-to-face instruction is always more effective.”

One of the goals was to elevate the position of her media specialists and the value they bring to schools. Sisk asked each to earn their Google Gemini-certified educator designation to become the on-site expert at their school post-Gemini School.

Her leadership is shifting perception across ECSD. Instructional technology is no longer seen as optional or burdensome, but as practical, exciting, and directly connected to improved teaching and learning results for their students.

“One of the biggest rewards this year was the blossoming of that relationship between curriculum and tech departments, because they helped us reach out to leadership,” Sisk says. “They allowed us to interrupt their principal's PLC meeting and do a version of Gemini School just for principals. That sparked buy-in. Principals saw the value in their world as a principal and wanted their teachers to have that same experience.”

This PD experience has earned a 4.72 rating out of 5, based strictly on teacher feedback. The demand generated by Gemini School has exploded, prompting plans for expansion—clear evidence that Sisk’s work is not only effective but scalable.

“We are presenting at our board retreat in a few weeks because our board members are asking, ‘What is this Gemini School and why do we have it?’” Sisk says. “Everyone wants a little piece of it. That's exactly what we wanted to create.”

Tools They Use

• Gemini
• Creating custom Gems
• NotebookLM
• Gemini in Google Classroom

One Teacher’s Story

A social studies teacher friend of mine told me recently that AI has changed her work in ways she did not expect. She was an early adopter, using AI to strengthen feedback and make grading more efficient. At first, it worked, allowing her to generate feedback that was more specific, individualized, and actionable than a rubric or checklist alone typically allowed in the past. But as that became easier to produce, expectations shifted. Detailed feedback was no longer optional. It had quietly become the norm, less because of administrators and more because AI made it possible, and therefore expected by students and parents.

But easier did not mean lighter. She still had to review, vet, and revise everything. She was still accountable for every word.

“It’s better for kids,” she said. “But I’m more exhausted than before.”

That may be the irony of AI in schools. The very tool promoted as a way to save time can end up expanding the work.

What the Research Is Starting to Show

What this teacher described is not an isolated experience. A recent article in Harvard Business Review argues that AI does not reduce work, but intensifies it. Workers complete tasks more quickly, but instead of reclaiming time, they take on more. The result is greater cognitive load, longer work hours, and expanded responsibilities.

AI increases speed, but it does not necessarily make work feel lighter because expectations rise alongside efficiency. Schools are not outside this pattern. If anything, they amplify it.

When Efficiency Expands the Work

The introduction of AI into school environments often leads to a rapid shift in baseline expectations. As efficiency increases, so does the scope of work; lesson plans require deeper differentiation, communication remains constant, and data analysis becomes both more frequent and granular. This evolution redefines the standard for quality, where previously comprehensive work is now viewed as merely basic.

While higher standards can benefit students, a problem emerges when these increased demands are viewed as resource-neutral. Educators are often forced to manage expanded workloads without additional time or guidance. Rather than simply saving time, efficiency recalibrates what is expected of staff, effectively filling any newly created capacity.

This expansion of workload is evident across several key areas:

• Instructional Planning and Grading: The ease of AI generation leads to greater demands for personalized feedback and highly customized lesson materials.
• Stakeholder Communication: As parents utilize AI to craft increasingly formal and legalistic messages, educators face a growing burden of processing and interpreting these communications. Even with AI assistance in drafting replies, the cognitive effort required to process the original messages remains high.
• Administrative Documentation: The time saved through automation is frequently redirected toward increased documentation and administrative requirements.

The Loss of Natural Limits

Teaching has never been a light profession. Even before AI, educators managed heavy workloads that required constant planning, decision-making, and responsiveness. What existed, however, were natural limits within the work–time between classes, the pace of planning, and the effort required to produce materials created built-in boundaries. Those pauses did not reduce the workload, but helped contain it.

As the same teacher explained, she used to have small pauses between classes. Now AI makes it easier to fill those moments. She might answer an email between periods, draft something during lunch, or use a few free minutes to produce one more item before the next part of the day begins.

What has changed is not the behavior, but the unspoken expectation. Because AI makes more possible in less time, those pauses no longer feel like pauses, but become opportunities to produce more.

This is where the shift becomes most visible. Work moves faster and becomes more dense, with fewer natural stopping points, squeezing out recovery time and reflective space. Cognitive load increases and decision fatigue builds, making burnout more likely.

We often focus on what AI makes possible, but spend less time examining what it requires from the people using it. This is not just a question of adoption, but one of sustainability.

What Leaders Must Do

If AI is going to remain part of school systems, then leadership has to define and shape how it is used. It is not enough to benefit from faster work.

1. Clarify when AI should be used. Not every task should be automated. Leaders must distinguish between efficiency and professional judgment.
2. Set limits on what speed should trigger. Faster output should not automatically lead to more output or higher expectations.
3. Protect time for thinking. Schools need space for reflection and decision-making, not just continuous production.
4. Match rising expectations with real support. Training, time, clarity, and resources must align with what is being asked of staff.
5. Eliminate or streamline existing requirements. If AI enables more detailed feedback or expanded expectations, something else has to give. Leaders should identify tasks that can be reduced or removed, such as redundant documentation, excessive grading requirements, or rethinking how rubrics and checklists are used. Without subtraction, every gain becomes an addition.
6. Keep the human at the center. AI can support practice, but educators remain responsible for accuracy, judgment, and care.

Leaders cannot raise the bar and then leave people to clear it alone.

Closing Thought

AI does not remove work, it removes friction, and with it, some of the limits that once kept work contained, which can lead to better outcomes. Without thoughtful leadership, it can also lead to faster burnout.

Watch above or listen/download below.

When the school year began last fall, Kenzo Vanhaesebroeck knew something had shifted. Students at Pineville ISD returned from summer break already fluent in AI tools—using these faster and, in some cases, more efficiently than the adults in the building. But instead of just reacting, the administration has taken a proactive stance to work with the technology not obstruct it. I had a chance to talk to Kenzo about the details.

For a small district that had barely finished digesting the implications of the technology, it was a wake-up call. "With every other platform, we had a little bit of a heads up," Vanhaesebroeck says. "With AI, we didn't even have a chance to really put policy in place before Google, Meta, all these big companies just said, 'Hey, it's here, it's integrated.'"

That speed is what makes this moment categorically different, he argues. Social media crept in gradually. AI arrived overnight—embedded in platforms students were already using, accessible on devices already in their pockets. And new platforms keep appearing daily, often discovered by students before their teachers have even heard about it.

Vanhaesebroeck's response has been a philosophy shift: out with "acceptable use," in with "responsible use." The distinction matters because a named-platform policy is obsolete before the ink dries. A responsible use framework, intentionally written to be somewhat vague, can flex as the landscape changes.

The practical centerpiece of that framework is what he calls a "grading scale" for AI on assignments—a spectrum that runs from no AI permitted to AI-focused projects in which students actually produce content using generative tools. Teachers set the dial for each assignment. The policy backstops them either way.

On the faculty side, Vanhaesebroeck relies less on mandates and more on peer modeling. When a teacher is doing something innovative with AI, he puts that teacher in front of the skeptics. "It's not just cheating," he tells reluctant colleagues. "There's also good sides to this."

His advice to administrators watching from the sidelines: stay open, experiment carefully, and don't wait for a perfect policy before engaging. "Almost being a little too restrictive by being vague," he says, "is better than falling behind and not having policy at all."

Melissa Jacobs, the director of library services for NYC Public Schools, has recently noticed a somewhat unsettling trend in education.

More and more of the professional writing materials she sees from her educator peers seem to be written by AI.

“I think newsletters are more AI-driven. I think evaluations and summaries and narratives and things that used to be the individual creating are now being driven by AI,” she tells me recently.

I’ve noticed the same thing in my communications with students and, in some cases, from peers. I recently wrote about how, at the start of 2026, I started suspecting students were writing me AI-generated emails. Jacobs began noticing this uptick in AI use by educators over the past six months.

We discuss this growing and little-talked-about problem in education, and what can be done about it.

Using AI Doesn’t Always Make Things Easier

One area Jacobs has seen a big uptick in AI-style writing is on grant applications she oversees.

“The answers to the questions for a large majority of them appear to be very AI-driven, rather than, ‘This is my own thought, my own belief, my own answer to this question,’” Jacobs says.

Educators looking for a grant shouldn’t be outsourcing the grant-writing process to AI for many reasons, but one is that it does the opposite of making your grant application stand out.

Additionally, the application process is designed to help grant recipients succeed once they receive a grant. Part of that, Jacobs explains, is to help applicants plan out the program for which they will use the grant.

“If they're not doing the work in the beginning, then they're not going to be successful running the program if they do receive the funding,” Jacobs says. “They're missing out on a big component of it. We're asking questions not as busywork, but as a thought process.”

AI Shouldn’t Replace Thinking

In the AI era, it has become a cliché that AI shouldn’t be used to outsource thinking, but Jacobs believes some educators may need a reminder of that.

“I’m not saying it's not a good tool to use, but I am encouraging original thinking,” Jacobs says. She adds that some educators are using AI in just the way we try and discourage students from using it. “They're not using it as someone that they're editing with, but they're like, ‘Oh, okay, it's done. I'm just going to cut and paste this here.’”

She adds this approach can be seen in the quality of work they submit. “Their voice is not consistent across all of their work, if you're looking at multiple pieces, and I think that they’re not putting the effort into it.”

Like we constantly tell students, it may be okay to use AI in certain instances to help you figure out how to write, but you shouldn't use it to write for you.

We Need AI Work Culture Norms in Education And Beyond

Jacobs says that right now the use of AI by adults who are professionals can create awkward situations. She notes that it’s hard to provide feedback to someone who didn’t create the work that you're critiquing.

These are the kind of discussions and challenges educators have been navigating with their students since the debut of ChatGPT, but now are becoming more common among educators themselves. Jacobs wants to see this issue get more discussion, and for more workplace cultural norms to develop around AI use.

In the meantime, she believes educational leaders can look to the playbooks they developed around AI use and students.

“There has been a lot of focused attention on teaching students how to use AI effectively, but there has not been a lot of focus on how to use it effectively with colleagues,” Jacobs says. She adds that it might be time to change that.

Sometimes, facing multiple challenges at once can create unexpected opportunities for discovery and collaboration.

“A phrase that I use frequently with my team is, ‘How might we?’ instead of just immediately going to ‘That's not going to work within our system,’” says Jeremy Sullivan, Director of Innovative Learning and Student Supports for North Kitsap School District. “We’re always approaching it through that curiosity of ‘how might we,’ especially because one thing I've learned over the last three years is that a lot of previous ‘Nos’ or ‘That's not possible’ or ‘I have no idea how to make that happen’ has changed. And AI has helped bridge that gap, especially from a systemic analysis and a systems lens.”

Sullivan has worked to implement the district's technology integration effort–which is branded as NKSD-connected, and features four tenets: future-ready graduates, equity, collaboration, and innovation–into its multi-tiered system of supports (MTSS). North Kitsap School District enrolls about 5,100 students across approximately 110 to 120 square miles in rural western Washington State, including the Port Gamble S'Klallam and the Suquamish sovereign nations. Using AI to boost MTSS has helped to bridge distances and differences, and for staff to work smarter, not harder, in supporting students.

“AI has let us work in a way we've never been able to do, like quickly taking 10- to 20-page plans and analyzing outlier interventions, synthesizing action steps that certain buildings are taking so we can connect them so they have partners to help them understand the work that they're doing,” says Sullivan.

For this and other efforts, Sullivan–who came from Springfield Public Schools in Missouri to North Kitsap five years ago to help launch the district’s 1-to-1 initiative–was recently recognized with a Tech & Learning Innovative Leader Award at a recent regional summit.

Three Tiers of PD

Sullivan says the district started small with this effort, initially gathering school leaders over a few years to determine what an MTSS blueprint would look like. All the district’s elementary schools now have one, with the secondary schools close behind.

Sullivan and his team have been working with Tier 2 and 3 intervention teams across 10 buildings to use AI to sort through the large swaths of data they have, including student well-being surveys, academic records, attendance and behavior data, and other critical information. The district has partnered with Panorama, which offers a student success dashboard.

“We are data rich now but we are still information poor,” Sullivan says. “So how do we understand what this is telling us to the level where we can actually take action? And this year AI has really helped us with that because we're able to take these large data sets and identify trends and patterns, both at a systems level and a school level. We can identify that our students are still struggling with this but excelling in that. We can also see that our staff are struggling in this and excelling in that. So what does that mean? And then that can scale down to both the school level and the classroom level.”

Appropriately, the initiative’s support structure for staff follows a three-tiered model, mirroring the MTSS concept:

- Tier One includes district-wide professional development and staff meeting access for everyone.

“I did a training yesterday during which I was able to show them within five minutes how to click a button, attach a class, ask a question–a simple prompt–analyze the most recent SEAL and student well-being data, synthesize it, and tell me the key points that I need to know,” he says. “And just immediately they get the data broken down in a very easy to understand way. There's no filtering. It synthesizes it for them beautifully.”

- Tier Two involves 27 AI “lighthouse” teachers, early adopters working with smaller groups of teachers, following a shared leadership approach.

“The AI lighthouse classroom teachers come and get additional training from me and my team and then they help support in their buildings with that job-embedded support,” says Sullivan, adding that having that practical, hands-on experience accessible, along with coaching, helps foster implementation.“Those AI lighthouse teachers are the ones who really push it into the classrooms and work with their buildings to help them understand it all.”

- Tier Three focuses on individualized support, during which Sullivan and another instructional specialist are deployed to work with specific teachers or student support teams as needed.

“So ultimately, it's just thinking about how are you covering all of those tracks for your system because you're going to have people–that Tier One everyone needs to get–but then how are you helping support the 20% to 5% who need something more.”

Advice For Other Districts: “Teach To The Smiles”

Sullivan advises other districts interested in using AI for MTSS support to begin by listening to a diverse pool of stakeholders, including certificated and classified staff and principals, to understand current practices, desires, and systemic barriers. With that insight, you can assess the lay of the land.

“You can begin to develop your map because that's when you know where your rivers are that you're going to need to build a bridge, where the mountains are that you might need to do some drilling or some blasting to get through into that system,” says Sullivan. “But until you know the lay of the land, you have no idea how to structure your map to get to that end goal.”

He stresses the importance of starting small by identifying innovators and early adopters, focusing initial efforts on developing human capital and authentic ways to shoulder the work, noting that teachers are more likely to be open to new tools and methods coming from other teachers versus coming from IT staff. And then it's about scaling that influence through shared leadership with the lighthouse teachers.

When confronted with resistance during implementation, Sullivan suggests following the advice from one of his former supervisors.

“She just said, ‘In those moments, Jeremy, look around and find the bright spot. Find the smile. Because oftentimes the smile is the quieter one in the room, but the smiles are there. And in those moments, teach to the smiles,’" he says. "Change never happens if we only talk to the ones who are in right off the bat. The reluctant ones need to see other people change first, and see that they have success in that change before they'll feel safe doing it.”

Tools They Use

Sullivan shares some of the edtech tools he regularly uses.

• NotebookLM - "This is my current workflow game-changer. The source-grounding feature allows me to create Notebooks trained solely the resources from my district (policies, procedures, CBAs, handbooks, etc.). The Studio feature allows me to create and scale support resources for my system like never before. With the click of a few buttons, I can have infographics, reports, mindmaps, videos, and interactive podcasts focused only on what I want it to focus on. When you are a small district team, these creation capabilities are incredible."
• Google Gemini - "Since we are a Google Workspace for Education school district, Gemini is our native AI tool. Much like NotebookLM, the ability to create Gems trained on your own sources and instructions allow for the creation of task-specific tools that get to the meat of the work faster. Much of my role involves analyzing qualitative and qualitative data for our system. Gemini in Sheets helps me whip up the right formula or quickly identify measures of central tendency, frequency distributions, etc. When creating resources in Slides, I can often obsess about the visual layout (colors, fonts, sizes, etc.) WAY more than I should. Lately, I have been leaning into the "beautify this slide" feature in an attempt to let some of that control go. I add the text and pertinent imagery, and let Gemini make it visually appealing. Major time saver!"
• Panorama Student Success - "This is our district's data dashboard that serves as a home for all student data. Our district prioritizes meeting the needs of the "whole child," and this dashboard helps us do that by collecting the academic data as well as our behavior, attendance, assessment, social emotional learning, student well-being, and climate and culture data. We also utilize it to refer, program, assign, and progress monitor individual and group student interventions and to gauge the impact of the interventions we are utilizing. The dashboard is used in a variety of other ways at the district, site, PLC, MTSS team, and individual educator level."
• Panorama Solara - "I had to list this as its own tool even though it is born from our Student Success data dashboard. This is Panorama's AI tool that allows for direct interaction with data available in the Student Success dashboard. Teachers can attach up to 20 individual students from the students in the classes, attach a single entire class, or upload their own resource to use with the tool. The Intervention Planner asks a series of questions and then automatically programs the trackable intervention with the appropriate team members assigned in the dashboard. We have created an MTSS Agenda Tool that automatically fills out the agenda template with the appropriate data based on the students selected with guiding questions for the meeting directly aligned with the data. There are so many incredible use cases for this resource." 
• MagicSchool - "Our system adopted MagicSchool as our main teacher and student-facing AI resource. While I don't utilize the tools as frequently in my role, I do create custom tools for our system within MagicSchool. This allows us to train the tools in alignment with our adopted curriculum resources. For example, we recently launched a custom tool that aligns our K-5 CKLA curriculum with our K-5 RULER SEL curriculum. Teachers can easily ask instructional design, instructional strategy, differentiation, and other questions and receive answers that authentically integrate the academic curriculum with the SEL curriculum. Marry that with how Solara can quickly analyze their students' most recent SEL data, and you can begin to see how data analysis followed by purposeful planning that used to take hours can be accomplished in minutes, freeing the educator up to engage with students in deeper ways." 
• Dotstorming - "It is an oldie but goodie that I still use for quick group brainstorming that has voting and commenting features that allow it to be used to go from dialogue to discussion to decision." 
• ChatGPT - "This one will also be on this list because it was my OG when I first dove into the LLM world. It has been trained by me for over three years and just "get's me," ya know?" 
• Canva - "Love this tool, but it can be dangerous for me because there are so many options for creation and design. That being said, the ability to create polished visuals, with the assistance of AI if desired, is awesome. It can be used by staff and students. I love tools that help students demonstrate their learning in authentic ways for authentic audiences!"
• Audible/Amazon Music - "Throwing these on here because they keep me sane. We can't work all the time, and I love to unwind with a great book or podcast."

Schools have no shortage of data. Learning management systems record access patterns, assignment submissions, quiz attempts, and student activity. Digital tools capture usage trends, while help desks collect reports about recurring technical problems. Surveys, attendance records, and assessment platforms add even more information.

Much of this information, which educators often describe as dark or underused data, is unused or only partially examined, and may hold important clues about teaching, learning, and student support. However, the data itself has no value until it is engaged within an instructional or leadership context.

Generative AI has increased interest in making better use of these large collections of information. School leaders, instructional coaches, and technology facilitators are beginning to ask whether AI can help process extant data more quickly and identify patterns that would otherwise remain buried. That interest is understandable as AI can review large volumes of text, sort information into categories, summarize findings, and highlight trends that deserve closer attention.

The real opportunity is not simply faster analysis, but smarter analysis guided by professional judgment. This is where educators need to be careful. AI can help schools become more data informed, but it can also tempt them into becoming overly dependent on automated conclusions.

Education has always required interpretation, context, and human understanding. That has not changed, and in fact, human judgment might be more important today than before the expansion of generative AI.

Contextualizing Data

Numbers and patterns matter, but need context. A dashboard cannot understand a student’s home responsibilities. A large language model cannot fully grasp classroom culture. A summary report cannot replace the experience of a teacher or coach who knows the learners behind the data.

A data-informed approach is more useful than a purely data-driven one. Data-driven decision-making often suggests that the numbers point directly to the answer, while data-informed decision-making treats data as one important source of evidence among several. Educators still need to ask what the information means, what it does not capture, and how local context should shape the response. That distinction matters even more now that generative AI can synthesize so much information so quickly.

AI is especially useful during the early stages of inquiry as it can help instructional leaders look across multiple sources of existing data and spot areas worth investigating. A coach might use AI to review comments from teacher surveys and identify recurring concerns about student engagement. A technology facilitator might use it to sort support tickets into categories and discover that a particular platform is creating repeated confusion. A school leader might compare patterns in assignment completion, attendance, and platform usage to identify students who may need additional support.

These are meaningful uses of AI because each helps to identify patterns at scale. It does not remove the need for human interpretation.

A spike in late-night platform activity might look like procrastination, yet it might also reflect sports schedules, work commitments, family care responsibilities, or uneven internet access at home. Low use of a digital tool might suggest weak implementation, or that the tool possibly does not align with the lesson goals, or that a teacher found a better non-digital strategy.

Limited revision activity in student writing might signal low engagement. It might also mean students are drafting in notebooks, using a different platform, or receiving verbal feedback during class.

The pattern is only the starting point. The judgment comes next.

Asking the Right Questions

Instructional coaches and technology facilitators are especially well-positioned to support leaders in this work as they often sit at the intersection of classroom practice, digital systems, and professional learning. They can help schools move beyond surface-level dashboards and ask better questions about the data they already have. They can also help teams resist the urge to treat AI output as final truth.

One of the most important roles these educators can play is framing the right inquiry question before any analysis begins. Instead of asking AI to find everything interesting in a large data set, schools should begin with questions tied to teaching and learning, such as:

• Are students using feedback to improve their work?
• Are teachers using digital tools in ways that support differentiation?
• Are families receiving communication in forms they can access and understand?
• Are accessibility features reaching the students who need it the most?

Clear questions produce more useful analysis, and human-centered ones produce more ethical analysis. Generative AI should serve as an assistant, not a decision-maker. It can surface possibilities, summarize evidence, and identify trends that deserve discussion. It cannot determine the right instructional response on its own. That work still belongs to educators who understand the curriculum, the learners, the community, and the goals of the school.

This is where leadership matters. School and district leaders need to create expectations for responsible AI use in data analysis. Staff should know what kinds of data are appropriate to analyze with AI tools, what privacy protections must be in place, and how human review will remain part of the process. Teams should be encouraged to validate AI-generated findings against direct observation, classroom artifacts, and professional conversation. Trustworthy use of AI depends on thoughtful structures, not just powerful tools.

The schools that benefit most from AI will not be the ones that automate the most decisions, it will be the ones that use it to ask sharper questions, uncover overlooked signals, and support better conversations about teaching and learning. Those schools will use extant data more effectively without forgetting that education is a human enterprise.

Human judgment is not a barrier to innovation, but what makes innovation worth using in the first place.

I’ve always been uncomfortable with the grading part of being a professor. As a student, I loved learning, particularly writing, but dreaded the judgment of an instructor and the harsh disapproval of their red pen. When I started teaching, grading was my least favorite part of the job. I’d obsess over minor discrepancies and second-guess myself constantly. Even when I felt I was completely fair in handing out a less-than-stellar grade, I worried about whether the student truly deserved a permanent mark on their record for something minor, such as forgetting to include a cover page. But I plugged along as a reluctant grader anyhow.

Then came the age of AI.

I am constantly seeing AI-generated papers, AI-generated emails, and even other AI forms of written communication from students. This has made grading, particularly grading written work, seem more unfair.

In other words, generative AI has poured gasoline and lit a match on top of an already flawed grading system.

While I understand that grades are still necessary and grade my classes in accordance with the institutional policies of the colleges I teach at, increasingly, I’m convinced that the widespread nature of AI provides more of an argument against grades in favor of some other model, such as standards-based grading or mastery learning. Here's why.

1. AI Adds To the Randomness of Paper Grading

Most educators, myself included, believe we grade papers fairly. Unfortunately, data does not really back this. In fact, much research that goes back to the early 1900s has found that when educators grade the same paper, there is huge variation in the scores given; for instance, one 2011 study asked 90 different high school teachers in the same school district to grade the same paper according to the same rubric. Scores this identical paper received ranged between 50 and 96.

I’ve noticed similar variations myself when I’ve observed the teaching habits of other educators I’ve supervised. What earns a B in my class might earn an A or a C in a colleague's, or worse, a D.

AI further unbalances this already unfair system. It’s clear that when we assign take-home papers these days, we’re creating a moral conundrum. Some students will use AI and receive good grades, while others won’t and might receive a poor grade as a result. This is not what any educator I know wants, but it is the default system that has emerged for handling AI.

2. AI Undercuts Motivation Argument for Grades

One of the main arguments for grades is that without any, students won’t do any work. Again, studies don’t really confirm this. According to one review comparing grading to feedback without grades, students who received feedback without grades achieved more and were more motivated to learn.

That said, I’m willing to grant that fear of getting a bad grade does motivate some students. Even I admit that I would worry about getting quality work from my students without some aspect of the fear-based motivation that grades provide.

The problem is that AI has already undercut this argument. In many instances, students no longer fear grades. Instead of grades motivating students to write, grades are motivating students to use AI to do their work for them.

3. AI Makes Grading Useless As A Ranking System

Another rationale for grading is that it helps sort students into various abilities, both for employment and for the next level of education. I never loved this as motivation for grades. Helping companies decide who to hire may have its uses, but it’s not likely to show up as an answer if you ask educators why they got into teaching.

Regardless, given how widespread AI cheating is these days, I wouldn’t place as much weight on grades as I once did. What I'm seeing in the world of writing education is that more and more, grades are merely a measure of how effectively a student is using AI without getting caught.

4. AI Makes It Clear Teaching To the Grade Isn’t Best

Almost all teachers I know rail against teaching to the test, but how different is that really from teaching to the grade? In fact, I believe that part of the reason students are turning to AI so frequently is that they’ve been taught implicitly that the grade, not their learning, is what matters. Therefore, when a tool comes along that can get that grade without work, there’s no reason not to use it. That is at least the way many of my undergraduate students seem to feel.

On the other hand, with my graduate students, AI use is nonexistent. That’s because they are in graduate school to learn to write, not just earn a grade. Of course, duplicating graduate student motivation for undergrads and K12 students isn’t easy.

I also know that eliminating grades overnight and switching to a mastery-learning method isn’t plausible. That doesn’t mean we shouldn’t strive toward creating a new motivation for students; we may not succeed in this process 100%, but we can do much more as a profession to move classroom motivation away from grade-based fear-mongering. If we don't, I think our students will keep turning to AI, no matter how hard we try to catch it, and take-home writing assignments will become a sad remnant of the past.

As artificial intelligence continues to expand its presence in schools, the need to educate educators and support staff also continues to grow. And although many edtech solutions provide training around their specific AI tools, having a basic AI education program can be even more useful.

“Tools have their place,” says Courtney Bock, AI Implementation and Library Media & Technology Specialist for Mead School District in Washington State. “But if we don't first understand the how and the why, the tools are not as powerful.”

To help support the teachers, Bock has implemented a four-part AI professional development series in her district, from which she graduated and currently serves about 10,500 students in North Spokane County.

“It can be pretty isolating sometimes as an educator when they’re thinking, ‘Am I the only one that is not getting this?’” says Bock, who was recently honored with a Tech & Learning Innovative Leader Award. “And then they go to the PD, see other teachers, and realize, ‘Oh, no, you're struggling, too. Okay, great. We can struggle together.’"

The good news is that Bock’s program has been turning that struggle into success.

Boosting AI Confidence and Literacy

Each session in Bock’s after-school program is 90 minutes and offers STEM clock hours for certificate maintenance in Washington State. Each cohort of the program has averaged 25 teachers.

• Session 1 is "AI 101," providing foundational knowledge, vocabulary (such as "LLM"), how AI works, and its capabilities and limitations, remaining tool-agnostic.
• Session 2 focuses on cheating and AI, helping teachers to redesign assignments and integrate AI into assessments, acknowledging that students are using it regardless.
• Session 3 dives into using AI with students, featuring a more tool-specific approach.
• Session 4 concentrates on using AI to improve teacher workflow, increase efficiency, and save time.

In addition to the cohorts, Bock supports teachers through a monthly newsletter, regular in-person presentations across the district’s 10 buildings, and virtual weekly AI office hours.

So far, the program’s impact has been quantified by significant gains in educator readiness. Post-training data has revealed a 50% increase in teacher confidence regarding the use of AI tools directly with students, as well as a 48% boost in preparedness to teach AI ethics.

Advice For Other Educators

For other educators who are interested in creating a similar PD program in their district, Bock recommends first focusing on AI literacy as opposed to just tossing tools at teachers.

“When I first did a version of this a year ago beforehand, it was just AI tools because that's what people wanted, and I thought, ‘Yeah, I can show you how to use those tools. No problem,’” she says. “But because I'm in the space every day, I was using basic AI vocabulary, and they didn’t understand these basics. That was a real learning moment.”

Bock also tries to make sure that there's plenty of free additional resources available to spark those who want to pursue more learning or certification for a specific AI platform. She notes that Anthropic, Google, and AIEDU, among others, all have AI training for educators.

“Some are better than others,” she acknowledges. “But I think it's good to take it all in with a grain of salt and use what's best for you, and leave what's not.”

Bock also recommends keeping parents and families informed about AI use. “When we're going to be introducing things to their students, I think It's important to have parent and student voices as part of that conversation.” She stresses that students, in particular, should be included in creating district policies as they are already interacting with AI daily, often through platforms such as Snapchat.

She also suggests involving administrative and central office staff in PD efforts as many may be interested in using AI to boost efficiency or simplify basic tasks, if they aren’t already.

“A Whirlwind Of AI”

Bock, who spent two years away from education at a friend’s startup as a natural language model analyst before returning to a district, has been able to bring that experience into her current role, which focuses on supporting district-wide AI implementation in the mornings and being a K-5 specialist in the afternoons.

“In 2022 when ChatGPT became public, it was like my two worlds just came together, which was not on my bingo card at all,” she says. “And it's just been, as you can imagine, a whirlwind of AI ever since.“

Ultimately, Bock appreciates having a community of teachers to learn from and with.

“It's harder to learn things in isolation,” Bock says. “And I'm not saying you can't, you absolutely can. But when you have that community around you to support you, ask questions, look at what they're doing as different examples–that's really powerful.”

Tools They Use

Bock’s “AI Stack”

• Claude
• MagicSchool
• CanvaAI
• Wispr Flow
• Gemini/Studio
• NotebookLM
• Gamma

Confession time: I've fallen in love with AI music generators.

Google recently made a version of its AI music generator tool Lyria 3 available through Gemini. This is one of many AI generators available that can create real-sounding, if not particularly artistic, music in various styles in a few moments based upon short prompts.

These AI-generated clips offer some fun, if gimmicky, applications that I’ve started to consider using in my class. First though, a few caveats.

AI music, as impressive as it may be, is still a lifeless, soulless product that tends toward the corny and, at its best, sounds like uninspired pop. It therefore cannot replace music created by actual musicians who put their hearts and souls into their musical creations.

Even if AI music tools were better, it would still be morally questionable to use any commercially. Like other forms of AI “art,” these generators mimic and plagiarize the work of human artists without providing proper credit or royalties. But as a pastime and as a sonic sandbox, AI music generators are some of the most fun generative AI tools I’ve come across.

I believe these also offer some teaching opportunities, albeit in a gimmicky, "pass a few minutes of class time" type of way.

Ways To Incorporate Music AI Generators

I’ve been using AI music generators to create short songs inspired by the personality traits of people I know. For example, I had AI write a song about my sister's propensity to always request menu substitutions when she orders at a restaurant, plus some goofy songs for my kids.

These are admittedly not laugh-out-loud use cases, but I enjoy them and have started doing similar things with students.

Here’s a song I created to remind my writing students to start working on their papers earlier. Will this annoying, but somehow catchy, song help students actually start working on their papers sooner? Probably not, as starting to work on a paper early is a piece of pretty standard advice that most students already know. However, it’s maybe a more fun way of reminding them to do that.

I can also see creating songs based upon student research and various topics covered: young students, in particular, might have fun with this type of thing.

But as with most things AI-related, you can take all this too far. Using AI-generating tools, I started thinking about the potential power of an annoying jingle for memorization. I tried a few different ways to get Gemini to create a song reminding students of the basics of thesis writing. This is the best it did after I told it to do this in a traditional Celtic manner. I’m not sure if this song really has traditional Celtic vibes, and even I’d be too embarrassed to share this song with students.

Are All AI Music Generators More Or Less The Same?

No. After experimenting with a few various free ones, I found a wide range of quality with the same prompts.

Gemini is the only one I’d currently recommend. It’s user-friendly but limited and only creates 30-second clips. Other music generators could potentially outperform Gemini with prompt adjustments. The ones I tried did better with the instrumentals but struggled more with the lyrics, and that kind of defeated the purpose of the tool for me.

What About The Bigger Picture

When teaching with AI in general, it’s important to discuss the implications of this technology with students. Much as I’ve been enjoying using AI to “create” music, this technology unsettles me a bit. I have multiple family members who went to music school and work as professional musicians. I don’t think their jobs performing live music are currently imperiled by this technology or will be in the foreseeable future. However, down the road, I could see it making the already difficult profession of being a musician more difficult.

As I keep only half-jokingly saying, AI music generators and other similar generative AI tools may ultimately destroy art and therefore what makes us human on a fundamental level. But on the bright side, I do like using AI to create annoying musical jingles.

On a more serious note, AI music generators are a fun-to-use generative AI tool that, like other forms of AI, have some associated thorny ethical questions. Those aside, there are some potential classroom uses, and AI-savvy teachers will want to start exploring this aspect of AI in addition to others. They'll also want to start delving into some of those thorny questions with their students as we navigate the ever-changing realities of AI's impact on the world.

Watch above or listen/download below.

While Zoom calls are great and usually get the job done for our podcasts, nothing beats conversations in person. Recently I got the chance to do just that at the Kira 2.0 launch event held March 3rd in New York City. Company founders introduced what they call an AI operating system for education — a platform designed not to add to the growing list of tools teachers manage, but to replace the need for most of them.

Founded nearly five years ago, Kira has operated largely out of public view, working directly with teachers, students, and administrators to build and refine its platform. The launch marked the first time the company gathered customers, partners, and education leaders to publicly demonstrate what that work has produced.

The centerpiece of the announcement was the Student Atlas, a longitudinal data tool that gives teachers deep, real-time insight into individual student performance — mapping skills, identifying gaps, and generating personalized interventions automatically. The demo showed how a teacher could move from identifying a struggling student to generating a tiered intervention plan, a personalized lesson, and an IEP draft in a matter of minutes.

For Harl Roehm, a computer science teacher from Tennessee who came to Kira through a state-required CS endorsement program, the Student Atlas was the moment that stood out. "I started right about the time they put up the Student Atlas, and I don't think I quit for two hours," he said. Previously, Harl had to manually review student responses to identify patterns. "With the Student Atlas, tagging everything through the standards and allowing me to personalize that to each student — that's going to completely flip the script."

Brandon Chitty, an administrator from Broken Arrow Public Schools in Oklahoma, was enthusiastic but measured. He sees real promise in the platform's potential to centralize instruction, but noted that deployment will require strong leadership.

"You have to have powerful leadership of instruction to roll something out like this — one that gives teachers the right amount of agency, but also makes sure that students in Algebra I with one teacher are learning the same thing as students in Algebra I with another," he said. He also raised the stakes of getting AI literacy right across the board, noting that alternatively certified teachers in particular need structured guidance rather than an open-ended tool.

The platform also includes a Course Studio that uses AI to build full, standards-aligned courses from scratch. During the event, the Kira team showed how eight CTE and AP courses were developed in approximately 30 minutes — a process that one curriculum professional in attendance estimated would traditionally require a team of three people working over three years.

Both educators drew a sharp contrast between Kira and the wave of AI-enhanced tools that have flooded the market in recent years.

"A lot of other companies are an inch wide and a mile deep," Harl said. "When Kira came on board, it was like, 'We integrate everything from the beginning.' For a lot of these other companies, AI was an add-on. With Kira, it's fundamental to the design."

Chitty agreed, pointing to the consolidation potential as a long-term differentiator: "In its 3.0 or 5.0 stage, this could eliminate the need for an LMS, eliminate the need for a separate MTSS platform. I'm not tied to any company. I'm tied to student learning."

Kira also debuted an AI-powered assessment builder with built-in proctoring, grading, and real-time feedback tools that feed directly back into the Student Atlas. The company indicated that a class and school-level rollup of student data, as well as an updated AI tutor, are expected to launch within the coming months.

ChatDOC is an AI-powered tool designed to let users “chat” with various documents they upload. I recently wrote about research examining how college students use ChatDOC and similar AI tools to engage with assigned texts. This research found both positives and negatives to the technology, so I decided to check out ChatDOC myself.

After using it with a variety of research documents, I ultimately found it a useful tool for searching within a document for specific items and for doing something such as quickly summarizing a research paper. Though it was also easy to see how certain students could become over-reliant on it and just ask ChatDOC questions about an assigned reading rather than actually read the assigned text.

Despite this potential drawback, I think ChatDOC can be a useful tool for both students and teachers, in specific instances.

What Is ChatDOC?

ChatDOC is an AI designed to help users interact with PDFs of various types, be it research papers, short stories, or chapters from larger works. Users upload a PDF and then have the opportunity to “chat” with that document, that is speak with a chatbot that bases its answers off of the uploaded text.

ChatDOC can perform tasks such as provide a short summary, search for specific terms, explain the overall theme if it’s a work of literature, or unpack the science in a research paper.

Other similar tools are out there, but ChatDOC is definitely one of the better PDF readers I’ve used. Its free version is quick and easy-to-use, and delivers on its promise of providing an AI that can discuss a given document with users and even quiz them on it.

What My Experience With ChatDOC Was Like?

I found the web version of the tool user-friendly and appreciated ChatDOC’s laser focus on performing one task: reading texts. This is not an AI for everything you might use AI for, but it’s good at what it does in terms of helping with research.

I uploaded a fairly dense essay, “On the Failure of Oracles" by Plutarch, from which I was looking for specific information. ChatDOC summarized some of the key arguments and searched for the sections for which I was looking.

Additionally, I uploaded a famous paper on quantum mechanics by Albert Einstein and others, in which he pointed out unexplained aspects of quantum mechanics. ChatDOC summarized the key arguments and answered questions I had about physics terminology.

On the whole, ChatDOC was helpful despite some limitations. It wasn’t ever inaccurate in its explanations, but it also wasn’t always entirely correct or helpful in its summaries, which tended toward repeated abstract-like statements.

On the plus side, ChatDOC always provided citations for its statements, and when you click on these citations, it takes you to the relevant text and even highlights it. I love this feature, as it's an example of what I think AI is most useful for: helping humans find relevant bits of information.

ChatDOC could also quickly generate good short answer, multiple-choice, or essay questions based upon documents you upload to it. I could see this helping teachers get started with assessment work based upon a reading, and it could also be a way for students to test their knowledge and understanding of a work, though paradoxically, any student putting in that level of work on their own to understand a reading probably doesn't really need a tool such as this.

What Does ChatDOC cost?

The free version lets you upload 5 files per day and a total of 10 per month, as well as ask 20 questions per day and no more than 100 per month. It has a 300-page limit to the documents you can upload as well as other limitations.

The Pro account costs $8.99 per month and is free of most of these limitations.

The free version is pretty restrictive, and I found that I quickly met my daily and monthly limits. It’s unfortunate that there is no intermediate tier between the free version and Pro version, which has a fairly steep monthly fee.

Would I Use ChatDOC?

Yes. It’s a useful tool and I would encourage educators to take a look at it, specifically if they are personally conducting research often.

I have mixed feelings when it comes to using it with students. As mentioned, in a small study researchers found that students would often read this type of summary rather than the text itself if given the option. This is obviously less than ideal, but if this feature could somehow be avoided, I could still imagine certain students would find it comforting to have this specific text-type tutor with challenging texts.

So, as with many AI tools, if used the right way, it could potentially be helpful, though there is a real potential for abuse. That said, many students are regularly using other AI tools that are less effective overall than ChatDOC. At least this tool is constantly citing and referring to what is hopefully a high-quality text and potentially getting students to engage with it in new ways.

Google’s VEO video-generating AI tool is now available through Gemini for Pro subscribers. I recently began experimenting with the tool and exploring potential teaching opportunities.

Overall, it’s a wild new science fiction-level technology with some fun, if gimmicky, teaching potential I could see using in the classroom.

More importantly, however, I think being aware of what this type of video-generating technology can do and discussing this with students is critical. This new generation of AI has massive potential for misinformation and will likely have implications for the entertainment industry and the future job market, and it's our job to help prepare students for all this as best we can.

How To Use VEO Through Gemini

For users with Gemini Pro, which costs $19.99 per month, accessing VEO is simple. You can just ask Gemini to “make a video” or click on the “create video” tool, which puts you in video mode and gives you more control.

Just remember, the Gemini Pro tier only permits you three video prompts per day.

You can also upload photos that VEO can use to add action etc., which is both very cool and very scary. I made a short video of myself shoveling snow using a headshot, and it looks pretty legit if a little artificial.

Prompting the creation of a video isn't as forgiving as a text-based chat with a chatbot. My first few prompts were nonspecific and resulted in poor-quality videos.

That said, I quickly learned where I went wrong and didn’t have a long learning curve. The secret, I found, is to provide as many specifics as possible. This was much more enjoyable than my experience with OpenAI’s Sora early last year. Using Sora was a consistently frustrating experience, at least for someone who was new to the world of AI video prompt writing.

VEO, on the other hand, doesn’t always do what you want but generally creates something interesting that is at least roughly based upon what you prompted.

Teaching With VEO

I enjoyed experimenting with having VEO recreate moments from history. I asked it to create a scene about the New York City Fire of 1776. The first attempt went very poorly. For some reason, Gemini/VEO had a dragon start the fire, but this tool isn’t exactly claiming to be an accurate purveyor of history.

Once I got more specific and asked it get rid of the dragon and show a person saving a child, it created a better video (see below).

This isn’t a fantastic work of visual art by any means, but it was interesting to watch and more fun then it should have been to "create." It has some subtle inaccuracies, and I could envision an exercise asking students to spot these. For instance, there are no wooden buildings even though wooden structures were common in New York City at that time.

I also tried to get it to create a realistic video of an electron traveling through a cloud chamber in the style of a vintage early 1900s science show. Separately I asked it to create this in as realistic a way as possible. Both of VEO’s attempts left something to be desired in terms of accuracy, but that can also be a useful teaching tool.

Here is one of those videos:

Using Gemini and VEO can also provide good examples of the importance of word choice.

Teaching with this AI could also be an interesting classroom activity for a creative writing class. I could see a situation in which students write short stories and then select three from which to generate videos.

Broader Implications of This Technology

Beyond these admittedly somewhat frivolous direct teaching applications, this is a serious and potentially game-changing technology that I’m taking note of and reminding my students to keep an eye on.

I truly don’t think Hollywood or society will turn to AI-generated movies any time soon, but there will be fewer people working in special effects, and those jobs will look very different and will probably involve a lot of AI prompting and fine-tuning. This raises questions about equity, copyright, and all-around fairness, all of which are important to discuss as a class.

Even more pressingly, creating these videos myself has already given me a better ability to discern the tell-tale signs of AI generation. This can help students learn digital literacy and how to spot misinformation and deep fakes -- skills that are becoming increasingly important.

Bottom Line: Would I Teach With VEO?

Yes, I would teach with VEO, but with some caveats.

I wouldn’t give students direct access or encourage them to use it, but I would share videos I generated with my class as a way to illustrate the technology’s strengths and weaknesses, and potentially as a subject matter tie-in, if and when appropriate.

In August 2025, Ohio became the first state in the U.S. to require traditional public school districts, community schools, and STEM schools to adopt an official policy on the use of artificial intelligence. The policy has to be in place by July 1.

The Ohio Department of Education and Workforce has shared a model AI policy, which is a good starting template for districts, schools, and institutions in other states as it features a purpose statement and AI definitions in addition to guidelines around literacy, ethical use, data privacy, and other concerns.

“The Ohio Department of Education and Workforce has been very forward in AI for the last couple of years, including providing some good resources,” says Christopher Lockhart, Chief Information Officer for Columbus City Schools, who is leading the effort to comply in the state’s largest district (46,000 students). “There's just never really been that push to say, ‘Okay, now schools, we need you to do something. Either say you're going to do it or say you're not going to do it, but you have to do something.’”

With AI continuing its exponential growth, it is most likely only a matter of time before every state legislates such a requirement, Lockhart says.

“Any school district that does not yet have a policy should take a hard look at developing one,” suggests Lockhart. “And I would take that work on now just so it's not a strain to get it done if a mandate comes down. It's going to benefit your staff and your students so they have that clarity of how to operate within the AI framework at the district.”

Gathering All The Voices In The Room

Lockhart’s district already had an AI working group in place when the impending mandate was announced last year. It is one of the first steps recommended by the state, which Lockhart jokes doesn’t seem like a coincidence. “I was like, ‘Well hey, we already have one,’ and then I realized I had talked at several functions around the state about the Columbus model and the fact that we had an AI work group,” he says. “So now I'm thinking, wait a minute, maybe they saw what we were doing and thought, ‘Oh, that's probably a good idea. Let's put that in there also!’"

Lockhart’s AI implementation working group provides a good model for other districts, with representatives from across grade levels and departments, including IT staff, teachers, administrators, and other support personnel. He also includes AI experts who understand the legal and practical ramifications, such as data security and privacy.

“I wanted to have a lot of voices at the table to make sure that everybody understands what this is,” he says. “We already know we're going to have some resistance, but at least the loudest voices were also in the room while we were drafting the policy, being heard and saying, ‘I don't like it, but can we at least do this and add this to it?’”

Lockhart’s group is also meeting with students to make sure their input is considered in any policy, including recommendations around teaching AI literacy and ethical use.

“The reality of it is what I've argued for as a proponent of AI in schools is that if we're not teaching it to the students in school, they're going to use it outside of school,” he says. “And if we're not teaching them the proper ethical safe way to use it, they're going to just be out there on their own.”

Lockhart also says any policy should consider equity as some students may not have access to high-speed internet or computing devices after school hours.

“We should find that balance for what we can give them as a safe tool here in the district to learn to get comfortable with AI in all its different iterations beyond just the chatbots and the generative AIs,” he says.

3 Considerations When Developing A Formal AI Policy

Other practical considerations for any district drafting formal AI policies:

1. Secure district-wide leadership support beyond the IT department, especially getting buy-in from the superintendent.

“If it's seen as an IT initiative, then it doesn't go very far,” Lockhart says. “But when it's seen as a district leadership initiative, you get a lot more buy-in.”

2. Do not make the policy overly restrictive. "You really don't want to put that type of restrictive language in a policy for something that's changing so fast," Lockhart says, "You certainly don't want to put in an application name. You don't want to specifically have ChatGPT or Claude or Gemini in your policy, because this all is going to keep changing.”

Lockhart says keeping the policy general also eliminates having to go in front of the school board every time a change is needed. “You can get more specific in what we use in schools with the administrative guidelines,” he says. “Once the school board sets policy, they then direct the superintendent to then build out the details of what that looks like in the administrative guidelines.”

3. Ensure the work continues beyond the initial drafting to include ongoing professional development and curriculum development

“As AI keeps changing, its prominence also keeps changing. The policy is going to have to change,” he says. “The administrative guidelines, or practices as some districts call it, will also have to change to keep up with the technology.”

AI Is Not 'Fly-By-Night'

Although AI seems like a new technology for many, Lockhart notes that’s not the case. For example, when he does presentations on the history of the technology, he starts in 1956 with the first work with AI at Dartmouth, then goes through the various iterations over the subsequent decades, from Big Blue playing chess to ChatGPT now. And he suggests that it is not going away any time soon.

“So I tell people that AI is not new; it is a mature technology,” Lockhart says. “It is not something that just popped up overnight–it's just new to us having access as the public. And I think it actually makes them a little more comfortable with saying, ‘Oh, okay, you know, we get it. We don't understand it, but we have a little more insight now that this is not something that's going to be fly-by-night.’”

Ultimately, forging that basic understanding around AI can be helpful for everyone involved in implementation and policy.

“Overall, this policy has been a good thing,” Lockhart says. “I think for a lot of school districts, they've struggled with the idea of whether or not they even wanted to have an AI policy, or to just bury their heads in the sand and pretend like, ‘If we don't talk about it, we don't have to deal with it’. So, I think this gets everybody a good footing of how we're going to deal with it.”

Researchers from various universities recently set out to understand how students are utilizing AI to help them understand assigned readings. The study, Self-Regulated Reading with AI Support: An Eight-Week Study with Students, looked at how 15 undergraduate students used AI to help them understand various texts over an eight-week period.

Though a small study that is currently in preprint form (meaning it has not been peer reviewed), it sheds compelling light on the ways in which AI use can support and hinder student learning.

Chris Yue Fu, the study’s lead author and a doctoral candidate at the University of Washington, recently spoke with me via Zoom and a follow-up email to explain the key takeaways from this research.

The Study’s Key Findings

Two things in particular stood out about these results to Fu, the first of which is the attention behavior gap.

“Students could clearly articulate what good AI engagement looked like as they told us things like, ‘the better your questions are, the more helpful AI can be,’” Fu says.

But even when they were taught good prompting strategies, only 4.3% of their prompts actually employed these strategies. Fu says. “They knew better but didn't do better, and that gap didn't close over eight weeks, even though the course was literally about AI.”

The second major takeaway was what the researchers termed “reading through AI” rather than “with it.”

“Students weren't just using AI to help them understand a text. They were using AI-generated summaries as the primary thing they read, and then selectively dipping back into the original text,” Fu says. “The AI output became the text, and the actual reading became a background resource. That was genuinely unexpected.”

Hints At Ways AI Could Be Used More Effectively

Despite those negative-sounding outcomes, the research also identifies ways in which work with AI tools can be helpful.

“For lower-level cognitive tasks, AI is already quite effective. When a student encounters an unfamiliar term or needs a section summarized, AI handles that well,” Fu says.

More effective AI could nudge students toward higher-order thinking, particularly the kind that is often necessary to understand and engage with college-level texts.

For example, after providing a summary of a given text, Fu would like to see AI follow up with something such as, “‘Now that you have the main argument, what assumptions is the author making? Do you agree with them?’ Or after a student asks about methodology, the system might prompt: ‘How would the findings change if they had used a different sample?’”

Fu says that after this study was concluded, there were more AI models designed to attempt this kind of productive questioning.

“The technology is starting to move in the direction our research suggests it should, toward sustaining the conversation rather than ending it after a single answer,” Fu says. “We actually saw evidence that students naturally progress toward this kind of thinking.”

Teacher In The Loop Design

In addition to continuing the conversation and going deeper with questions, Fu and his fellow researchers also called for more teacher control in the ways in which AI chatbots and tutors work in the classroom.

“We envision a system in which the instructor can set learning goals and requirements for specific readings in advance,” Fu says. “For example, an instructor assigning a dense methodology paper might tell the system: ‘Make sure students engage with the limitations section,’ or ‘Prompt students to compare this framework to last week's reading.’”

Right now general-purpose chatbots are “designed to answer questions,” Fu says. “They optimize for task completion. When a student asks for a summary and gets one, the system has done its job, but the student hasn't done theirs.”

Fu adds that there is an opportunity for designing education-specific AI tools “that gently push students toward the deeper engagement we know they're capable of.”

The research Fu led suggests this could be significant in its impact with students. “We saw that capability in the data: students naturally moved toward reasoning when they kept going," he says. "The challenge is designing systems that support that progression instead of letting it get cut short.”

Back in 2007 Jon Bergmann and Aaron Sams pioneered what is now known as flipped learning. Today, Bergmann, who remains a high school science teacher in addition to being a recognized education expert, says a new teaching paradigm is underway because of the emergence of AI.

To that, Bergmann has developed a new teaching framework that combines flipped learning and mastery learning models with the modern realities of AI. He calls it The Mastery Flip, and its three pillars are designed to maximize the beneficial impact of AI while minimizing its harms and preparing students to harness this technology for the future.

Pillar 1: AI Engines (Turbocharging the Independent Space)

AI can help students when they’re working at home in what flipped educators often refer to as the independent space. Ultimately, Bergmann would like to see teacher-controlled AI tutors that are trained to ask students questions rather than provide answers, and can be programmed to reflect a teacher’s voice, style, and preferences.

“In the world of chemistry, there are two camps in how you do the math part of chemistry,” Bergmann says, advocating for the option of teachers choosing which method for the AI to emphasize. “I want it to be tuned to the teacher.”

Many AI tools aren’t quite there yet, but are getting close. Bergmann is optimistic that tools that provide more teacher control, along with rigorous data and privacy control for students, are on the way.

One tool he’s impressed with is Skylow.AI, which allows users to upload video and audio of themselves to create an AI clone. So now when Bergmann’s students watch one of his flipped learning videos, they can raise their hand and ask questions about the material, and an AI version of Bergmann will answer.

He reports that the tool has so far split the class, with half liking it but the other half saying, "It's too creepy that there's an AI Mr. Bergmann.”

Fear of robot clones aside, the point remains that the right type of AI technology could be really beneficial to students who are working outside of the classroom.

Pillar 2: Analog Roots (The Anchor of Learning)

The availability of AI and the way it can be used to generate student work makes the teacher’s work with students during class time even more important than ever before. That’s why Bergmann’s current approach is to encourage more tech outside of class to support learning but less during class so students can focus on cognitive work and learning from the teacher.

“My class is becoming less and less digital,” he says. Though he adds, he can imagine a role for well-designed AI tutors even in class, provided the right oversight is in place and the AI supports a student’s productive struggle rather than giving them all the answers.

“The LLMs were designed for businesses, and for efficiency, and not for education, and productive struggle is not built into them, so we need to have something that's built for kids and their brains,” Bergmann says.

With or without the help of AI, the idea is to use class time to ensure students themselves are working, struggling, thinking, and ultimately making progress in their learning.

Pillar 3: Human Checks (Validating the Cognitive Journey)

Many teachers seem unaware of the way in which AI has fundamentally changed what effective assessment should look like, but Bergmann does not have any such illusions. Traditional research papers, for instance, no longer work. “You can’t do that anymore,” he says. “The game's over. In two seconds, I can prompt ChatGPT to write the best research paper.”

Because of this, teachers need to create assessments that ensure students, and not some AI chatbot, have really worked through the material and thought about it.

For example, in one assignment for Bergmann’s physics class, students have to build a trebuchet, a complex medieval catapult-type weapon. The final for the assignment involves students using these beefed-up catapults on the football field to see how far projectiles can be launched. “Then they have to explain the physics behind it, but I'm smart enough to know that they're probably going to have AI write a lot of that, so I'm going to just accept that,” he says. But it’s only going to count for a portion of their grade; larger portions will be based upon the performance of the catapult and the student's ability to explain the physics behind it to Bergmann. “We're going to have a marker board or a whiteboard, and I’ll say, 'Explain how this worked. Why did this happen?’”

These types of human checks can ensure that students are not outsourcing the knowledge and critical thinking portion of assignments to AI, which Bergmann worries may already be happening too much.

“My terrible prediction is I think it's going to be very bifurcated. Those students who use AI as a cheat code are going to really struggle in life,” he says. “This is an electricity-level event right now that’s happening in our lives. The vast majority of candlemakers went out of business when electricity came along.”

Bergmann hopes his new model can help educators guide students to avoid the fate of candlemakers and use AI to supercharge learning.

If you have been following the growing coverage of AI and sustainability, you have probably noticed a familiar pattern. The conversation often lands on guilt and abstinence. People hear about energy-hungry data centers, water use, and emissions. The conversation about AI and sustainability often ends with a simple conclusion: maybe we should just stop using it.

That conclusion feels responsible, but it isn’t strategic.

AI has become foundational to how work gets done, how science advances, and how communities solve problems. Opting out in schools does not reduce society’s demand for AI. It simply leaves students less prepared to participate in a world in which AI is shaping civic life, careers, and problem-solving.

The real issue is not whether we use AI. The issue is whether the AI industry is required to build and power it responsibly.

We have been here before. We did not stop driving cars because of the pollution; we required emissions standards and redesigned engines. When refrigerants harmed the ozone layer, we did not abandon refrigerators, we phased out Chlorofluorocarbons (CFCs) and improved the technology. We did not stop eating packaged food because we lacked information--we required nutrition labels.

Nutrition labels do not require abstinence. These require transparency. Environmental labels can do the same for AI. Rather than opting out, we should demand accountability and redesign.

What Better Can Look Like

Some major companies have set clear sustainability targets. These examples give educators and students something concrete to point to.

• Google’s 24/7 carbon-free energy goal (by 2030)
• Google’s data center sustainability and efficiency work
• Microsoft’s sustainability commitments, including water positive and zero waste goals
• Microsoft’s carbon negative commitment (by 2030)

Examples of AI being used to reduce waste and improve efficiency within infrastructure itself are also available:

• Google DeepMind’s data center cooling work
• GraphCast, an AI model for faster and more accurate global weather forecasting

None of this erases the environmental impact of today’s AI, especially as demand accelerates. What it does show is that companies can be pushed to pair AI innovation with clean energy, water stewardship, and clear reporting.

Students and Staff Have Already Proven They Can Move Systems

Where I work, in New York City, student and staff driven environmental work is not theoretical. It has happened repeatedly with initiatives such as:

• Green Bronx Machine
• Cafeteria Culture’s Foam Trays Removal
• Billion Oyster Project

That same energy can be applied to sustainable AI.

A Strategy for Teaching AI and Sustainability

AI’s environmental impact is real, but product-level data is not surfaced in a simple, student-friendly way. That is what needs to change. We can start by building students’ understanding and then channel that learning into action: defining what transparency should look like.

These ready-made resources can ground students in the tradeoffs of AI and sustainability:

• SubjectToClimate: ELA lesson on generative AI’s energy use
• Share My Lesson: Mapping U.S. data centers and their environmental impact
• Scholastic Upfront: “Power Hungry” lesson plan
• National Science Teaching Association (NSTA): The carbon cost of our clicks

Once students understand the landscape, what could come next is to define what responsible disclosure should look like, and push the market in that direction.

Design an AI Environmental Label

Students already recognize standardized disclosures, such as nutrition labels. Privacy labels and indexes such as those from Common Sense Media, ISTE/ASCD, and Apple show how complex issues like student data practices can be made more transparent.

Pose a Challenge

If food and apps can be labeled, why shouldn’t AI tools have a label disclosing environmental impact?

Students can design a prototype AI Environmental Label that includes:

• Where the tool runs (cloud provider or infrastructure)
• Whether the company publishes sustainability reporting
• Whether there is a renewable or carbon-free energy target
• Whether water use is disclosed
• Date of last environmental update
• Third-party verification (yes or no)

Students can encourage rating platforms to include the environmental standard schools and communities should expect.

The Point

AI has real environmental costs today. Educators should not deny that, but opting out is not a strategy. Leverage is.

Schools and students can vote with their choices by favoring AI providers that publish credible sustainability data and commit to clean energy goals.

The next move should be as normal as nutrition labels: environmental ratings for technology tools. Until that exists, students can help create demand by proposing an AI Environmental Label and using it to push vendors toward cleaner infrastructure and clearer reporting.

This approach addresses AI’s growth responsibly while building students’ literacy, agency, and civic influence.

You can watch this week's episode above or listen/download below.

While many school districts remain stuck debating AI policies and compliance issues, Franklin Township Schools in Indiana has built something different: a culture in which artificial intelligence serves as a learning companion rather than another initiative to fear. I had a great spontaneous chat with Nadine complete with a cameo from the superintendent from his car!

Nadine, who has been the district's Director of Technology Integration for 11 years, started her AI journey not with bans or restrictions, but with a year of foundational professional development. Her approach was simple yet strategic: help teachers understand how AI actually works before expecting them to use it.

"I had to explain to them that a tech tool is like a car," Nadine says. "All of these different companies are cars, but underneath the hood, each one of them are connected to a large language model." That basic analogy helped teachers move past the overwhelming flood of AI product announcements to understand the underlying technology.

The turning point came from a personal experience. Nadine's dyslexic son came home from high school frustrated that his resource teachers, despite their best efforts, couldn't possibly know all the content across his multiple subjects. "That was like this giant aha moment," she says. She saw how AI tutoring could fill that gap.

Rather than rushing to a district-wide rollout, Franklin Township applied for Indiana's digital learning grant and piloted SchoolAI with special education and English language learner populations. The results spoke for themselves—from helping students work through thesis statements to instantly translating for a third-grader who spoke Punjabi.

The district's approach reflects what Nadine calls, "AI safety as a design challenge rather than a compliance issue." They chose SchoolAI specifically because teachers and admins can see student chat interactions.

"We don't view AI as an initiative, we view it as a companion," says Superintendent Dr. Chase Huotari. His advice to hesitant colleagues? "Starve the issues and feed the possibilities. That worst case scenario playing out in your head is not what's going to happen."

• Tech & Learning Conversations with Kevin Hogan

Although artificial intelligence has created many opportunities in education, such as supporting individualized learning, providing tutoring, and increasing teacher efficiency, it also poses risks for school districts. From AI slop and pink slime to harmful deepfakes and scams, district leaders and technology professionals have their hands full trying to provide students and teachers with safe AI.

Adding to those challenges now is the threat of shadow AI, which certainly sounds, well, shadowy. In reality, it’s a bit less nefarious, although it still can cause havoc for IT departments as well as school staff and students.

What Is Shadow AI?

Quite simply, shadow AI is the unmonitored use of any unauthorized AI tool or platform by teachers, staff, or students, such as generative AI chatbots or data visualization tools. Having such unvetted AI programs can open schools to the potential for data breaches and other cybersecurity vulnerabilities.

Typically, the unsanctioned use of these AI tools is unintentional, says Maurie Beasley, network/system administrator for Llano Independent School District in Texas and author of Teachers Have Bigger Fish to Fry: Tales from K12 Education/ How AI Might Help...or Maybe Not.

“It's really often ignorance,” she says, noting that when she informally asks educators in her district about their AI usage, many don’t even realize they’re accessing AI as it’s been baked into so many tools now. “They're not hiding in the closet and trying to do things they shouldn't be doing. It's just the utilization of something that they have easy access to that makes their world or their life easier that they're using without permission and not thinking about the consequences.”

“An IT person literally calls it shadow because ‘If I can't shine a light on it, it's in the shadows,’” says Jim Beasley, Llano ISD’s Director of Technology (and Maurie’s husband). “And if you're using it without my permission, that's an IT perspective.”

Jim can envision a worst-case scenario of shadow AI, such as using an open-source AI agent tool like OpenClaw that, when installed on a local machine, has unrestricted administrative-level access, allowing it to manipulate files and send emails. Such a tool on a network, especially if it exposes API keys, would present "a really big problem."

The Risk For Schools

One of the biggest risks with shadow AI for schools is the potential for data breaches, especially when the evolving aspects of AI can make for new threats and concerns.

“Right now, if there's a data leak, you stop the leak, maybe you can pull the data back in, maybe you can't, but that data, you can draw a bubble around it and say ‘This is what got out,’” says Jim. “Once a data leak gets into AI, you can't remove it. I mean, OpenAI doesn't have a way to pull that data back out. So if you leak a social security number and it gets pulled into some training data for a large language model, it's there forever potentially.”

Of course, any unmonitored access to a school or district network can create opportunities for hackers and other bad actors as well as more sophisticated cybersecurity attacks.

“Being compromised before meant somebody gets access to someone’s email, for example, and starts sending emails out on their behalf,” says Jim. “If some kind of AI gets onto my network, or somebody plugs something in it's got AI access, what does that even mean? How is that going to interact on my network? What does ransomware look like that has intelligence attached to it? My biggest goal is to retire before I have to figure that out because I don't know if I'm smart enough to figure that out.”

What Schools Can Do About Shadow AI

Obviously, as with any potential threat, the first step to protecting against shadow AI is awareness and education.

“If you don't start with education, the technology things are useless,” says Jim. “At the end of the day, it's 90% education and 10% technology, or maybe 10% technology with 5% of that being the processes that you put in place as part of that technology. And if people aren't educated, they are going to do things you cannot anticipate in ways you did not anticipate.”

Maurie encourages skepticism, even joking that she pushes her colleagues to the point of paranoia. “It's very enticing to let AI take the cognitive load,” she says. “Yeah, let it do the easy stuff and let it help you, but when it comes to the cognitive part, make sure that's still the human that's making those decisions.”

If they haven’t already, IT departments should also consider implementing guardrails around AI use, monitor usage, and once again, make users aware of the risks. Jim suggests sticking to IT 101 principles, such as ensuring employees have the least necessary permissions and tightening previously lax policies, especially concerning Chrome extensions, which are now being enhanced with AI. He also advocates spending more time checking for erroneous logins and proactively securing networks.

With how rapidly edtech is evolving and AI being added to so many platforms, school technology leaders need to be extra vigilant, and even consider re-vetting software and tools that were previously approved.

‘It’s Another IT Project’

Ultimately, dealing with shadow AI is the same as dealing with any cybersecurity threat.

“If you're in an IT department, focus on what you know how to do, have the same disaster recovery plans you've always put in place, treat AI that way, but don't ignore it, either,” says Jim. “If you ignore it and you just decide somehow it's going to take care of itself because Google's going to provide it just like they've done everything else, you're making a mistake in multi-dimensions because, A, Google can't plan for everything. And B, Google's AI doesn't do everything, so people are going to go off and use a different AI.”

“Artificial intelligence, when you look at it from an IT perspective, is the same process you've gone through with every new technology that's ever happened,” says Maurie. “You need to have a security plan in place. You need to make sure you have backups ready in case you need to restore something. It's literally just another IT project.”

OpenClaw, formerly known as Moltbot and Clawdbot, is an agentic AI assistant that has surged in popularity with the tech-savvy first adopter crowd in recent months. According to some of these early users, OpenClaw is the first tool to live up to the promise of agent AIs.

I’ve recently tried some AI agent tools and haven’t been particularly impressed, but was intrigued enough by OpenClaw, which is open source and free, to give it a whirl and see how it stacked up. Ultimately, I found the process of using it to be a bit more complex than the AI agent tools I’ve tried.

One of the selling points of OpenClaw is that it operates on your computer, which is supposed to alleviate some privacy concerns and give you more control. But that also comes with its own inherent security risks, and makes setting up the tool more complicated and above my technical know-how. As a result, I used a browser-based version of OpenClaw.

True techies will be dismayed by this as it defeats the purpose of the tool in the first place, but even in this more limited version, I found it outperformed other AI agents I’ve used, including Google’s new Auto Browse, and could indeed help with my research and class prep.

What is OpenClaw?

OpenClaw is the one true AI agent that will rule all the other pretenders, or at least that’s the impression some of its earliest adopters give. The initial version is free and open source, and designed to integrate with your digital life, acting as a true digital assistant.

Launched in November 2025 by Peter Steinberger, an Austrian coder, he named it Clawd as a playful nod to the popular AI chatbot Claude. However, Anthropic, the company behind Claude, asked Steinberger to change the name. He briefly changed it to Moltbot, inspired by the “molting” process in which lobsters shed and regrow their shells, before settling on OpenClaw.

This AI agent gives users the ability to do tasks such as order groceries or monitor and summarize emails. For a teacher, it might help with lesson planning and class management, and if it could be made fully secure, could even be used for activities such as monitoring class participation and trends, though that would require many more safeguards around student data.

OpenClaw is designed to run on a computer that you own. If you leave on your device, you can then communicate with your AI assistant via various messenger apps and have it perform tasks for you regardless of where you are.

All these features do come with some inherent risks. Giving a tool that can behave in unexpected ways complete access to your computer can be dangerous, and there's something equally insecure right now about having an AI communicate with others or use your credit card to purchase things. In fact, a number of tech companies recently put restrictions on OpenClaw use due to security concerns.

What Does It Cost?

The standard version of OpenClaw is free and open source. It does require some degree of technical know-how to get set up, and takes a little time even for those with this know how.

Tools are emerging that allow you to access the tool on a virtual browser. This makes using it easier, but arguably defeats some of the purpose. Additionally, in my experience, some of these sites seemed suspect.

I won’t link to the OpenClaw site, which I ended up using, because I felt that its design intentionally mimicked OpenClaw’s web design so you would think the two companies were linked—they are not. This makes me worried about paying for their services personally, and even more worried about directing readers there.

How Good Is OpenClaw At Its Job?

By not operating OpenClaw on my device, I couldn’t access some of its best features, but still was fairly impressed overall. OpenClaw was able to do things such as look up Airbnb options for an academic conference I’m going to, and actually did a great job of summarizing the strengths of each listing, unlike some other AI agents that had failed in this task.

It was also able to find and summarize various lesson plans on teaching writing, and was as skilled at research as any AI tool I’ve found. It could quickly find, summarize, and categorize research on AI tutors, quantum mechanics, and more.

I was left with the strong impression that I could get more with this tool, with more thought and perhaps a version more customized to my use and to teaching.

Bottom Line On OpenClaw

OpenClaw is not essential, but it’s a strong-performing AI agent and one that tech-savvy teachers will want to explore with caution from their personal, non-school issued devices. Nothing I was able to do with it was any kind of game-changer, but if you’re looking for an AI agent to experiment with and see how it might help you and your teaching practice, this one is worth adding to your list of tools to check out.

For those of us who are less tech-savvy, it’s a good idea to keep an eye on these types of tools as they’re likely to continue to improve and get more user-friendly and integrated into teaching as they do.

Google recently unveiled its new Auto Browse AI feature that integrates with its Google Chrome browser. Think of a self-driving car, but for your internet browser. In Auto Browse, an AI agent takes the driver's seat using a tab in Chrome to explore the internet at your direction.

Other AI agents exist that browse the internet, but Auto Browse marks Google's biggest public-facing entry into this arena. Auto Browse fully integrates with your browser and across Google’s other services, undertaking tasks such as searching your calendar and email and using information contained there to complete other tasks.

For educators, the tool is, at least in theory, well-placed to do things such as complete spreadsheets, help with travel plans, lesson planning, and finding teaching resources and materials.

In practice, after experimenting with Auto Browse, I didn’t find it to be a revolutionary step over existing AI services, though its integration with Gmail and Google Calendar was pretty seamless and impressive.

Here’s a closer look at Auto Browse.

What is Auto Browse?

Auto Browse is the latest and greatest AI agent tool from Google that works with Gemini. The idea is that you can use it to open Google Chrome tabs for you and complete tedious web tests in autopilot.

That being said, there are some caveats. As soon as you start using Auto Browse, a warning appears saying, “You are responsible for Gemini's actions during tasks.” I found this kind of unsettling. What if my AI agent starts ordering pizzas, convinced (probably correctly) that that would be the best way to keep me happy?

Because you are ultimately responsible, you have the option to take control of the browser. But sitting there watching what Auto Browse is doing really defeats the purpose of using it in the first place.

Additionally, cybersecurity experts say Auto Browse and other AI agents are at risk for “prompt injection attacks,” malicious instructions hidden on websites that can trick AI agents into putting your computer or accounts at risk.

That said, Auto Browse does have some cool features that some teachers, particularly AI enthusiasts, will love.

How Much Does Auto Browse Cost?

Auto Browse is available for subscribers to Google AI Pro ($20 per month) and Ultra ($250 per month).

I subscribed to Google AI Pro to gain access. The interface is fairly user-friendly. You can activate it by clicking on the Gemini option in the right-hand corner of Chrome, which opens the Gemini AI sidebar. You can then ask Gemini to open Auto Browse within that, or it will open automatically depending on your query.

How Helpful Is Auto Browse?

For this story, I asked Auto Browse to undertake a series of education-related tasks, including looking for an Airbnb for me in Boston when I attend an academic conference, looking for open academic positions, and generating some teaching materials based on old talks I gave.

On the whole, the performance was mixed. I was impressed by how the AI agent was able to look up the date I was going to be in Boston from my Google Calendar, and could search my email for contacts I’d communicated with on certain topics. But in some other areas, it fell short. It did not help me find an Airbnb rental, for instance. Though it provided a list of potential rentals that fit the criteria I had given it, the links to these rentals provided by the AI did not work. I was able to find the properties it had discussed by searching for them myself, but that defeated the purpose.

More impressive was the response to my query asking it to find specific types of writing professor jobs available in New England. It created a nice list of all the jobs and what was required to apply for each. This was pretty cool, but other than this, I had trouble finding tasks I wanted done that Auto Browse was uniquely qualified to help with.

That aside, I was impressed with the AI integration of Gemini with other Google products, including Gmail. I use a personal Gmail account, but could see using Gemini to do quick searches of things “every student who had a question for me over the past week . . . .” Or every student who asked about a specific assignment. Gemini was really strong at completing these types of queries and it didn't even need to go into Auto Browse mode for many of these.

Bottom Line: Is Auto Browse Worth Exploring As A Teacher?

Auto Browse has a lot of potential and is fun to experiment with, but it’s not superbly useful. It does indeed act as a personal assistant as it's supposed to, just a personal assistant that’s not particularly good at its job.

Nonetheless, I have hope that it could get better as time goes on, and I might get more ideas of how best to use it. Ultimately, educators who are obsessed with the latest and greatest in AI should take a look, but others may want to wait until the technology matures a bit.

Most Likely Machine is the teaching solution to a very real and modern gap in learning, around how algorithms work and the ethical ramifications.

As algorithms become more prevalent in more areas of our lives, it becomes ever more important to understand these key decision-making automations.

Critical to the learning here is how these systems can lead to bias and the importance of ethical design when writing algorithms. This is especially so in the age of AI when the complexities coming from a single coding decision can reach very far indeed.

Artefact, the design firm behind Most Likely Machine, has built this free multi award-winning learning service as a fun and gamified way to help 10-14-year-olds understand just how algorithms work. This should help them see more clearly in an age of automated feeds and decision-making search results.

This guide aims to explain all you need to know about Most Likely Machine in your class.

What is Most Likely Machine?

Most Likely Machine is a free teaching tool designed to help educate 10-14 year-olds about algorithms. This is not only about how these are made and used, but also the effects that these can have in our daily lives. That should help empower young people to make more balanced decisions in the future.

Using a game-style environment, students are able to build their own algorithms to observe consequences and reflect on the social impact of automated decision-making.

So while some tools focus on algorithm-writing specifically, this is more about conceptual understanding -- how data is weighted, how choices shape outcomes, and why no algorithm is ever truly neutral.

The Fast Company’s Innovation by Design Award and the Core77 Interaction Award have both been given to this tool. The project has been piloted in real classrooms with strong engagement from both teachers and students.

How does Most Likely Machine work?

Most Likely Machine is browser-based, so it's super easy to access from nearly any device -- allowing you to work on this as a class or individually.

Initially, you're introduced to the idea of a class designing awards using algorithms, what these are, and why these exist. Then it's down to the students to work on creating algorithm-based choices and seeing how outcomes are affected.

Students meet a host of characters based on famous historical figures, including Albert Einstein and Rosa Parks. These are presented as classmates competing for awards such as “Most Likely to Go to a Top University,” “Most Likely to Go Viral,” and “Biggest Troublemaker.” By choosing which traits matter most and weighting those characteristics, students construct algorithms that automatically select winners.

Students select traits such as intelligence, popularity, creativity, or leadership, and assign weightings that determine how much influence each factor holds.

As a result, students can see directly how personal assumptions, social stereotypes, and data selection choices influence outcomes.

The setup aligns well with computing, digital citizenship, and PSHE curricula, while also linking naturally to discussions around ethics, media literacy, and social responsibility.

What are the best Most Likely Machine features?

Most Likely Machine is very fun and friendly in its story-led design. That makes it engaging for students while still teaching how algorithms are built, tested, and refined. Only in the context of a reality, which helps them understand outcomes.

Otherwise abstract computational ideas suddenly become a tangible and fun experience.

The bias reflection section is particularly helpful as this focuses on social assumptions influencing technical decisions.

Real-world case studies are also used as a way to offer examples that students can critique, covering areas they may feel passionate about, including unfair grading systems, harmful social media, and racial bias in law enforcement tech.

How much does Most Likely Machine cost?

Most Likely Machine is totally free to use and is widely accessible via a browser. As such, this can be used across schools, subjects, and by teachers of varying technical abilities, with ease.

No subscriptions, licensing fees, or premium upgrades are required for classroom use.

Most Likely Machine best tips and tricks

Use it as a discussion starter
Run the activity first, then allocate dedicated time for group discussion. The richest learning often emerges from debating why certain results appeared and what that reveals about bias.

Connect it to real-world technologies
Link student reflections to everyday systems they encounter, TikTok recommendations, YouTube feeds, Spotify playlists, or school assessment software, to deepen relevance.

Pair it with ethics-based writing tasks
Ask students to write short reflections or debates on whether algorithms should be trusted in sensitive decisions like policing, hiring, or university admissions.

Some educators are responding to artificial intelligence (AI) by retreating to “safe” strategies, such as requiring all writing to be done by hand on paper. That move may feel like a responsible guardrail, but it is neither responsible nor future-facing. It also has real equity consequences. It disadvantages students with disabilities and learning differences who rely on assistive technology, as well as multilingual learners who may need translation, vocabulary support, or language scaffolds to fully express what they know. If the goal is authentic student thinking, removing tools that increase access does not protect learning. It restricts it.

Responsible use of AI in schools does not begin with tools. It begins with clarity.

Before students ever open an AI-powered app, innovative educators ask a foundational question: What is the learning goal? From there, decisions about whether, when, and how students use AI become far more intentional and far less controversial.

This goal-driven approach is something strong teachers have been modeling long before AI entered classrooms. The difference now is that AI gives educators new opportunities to personalize feedback, extend creativity, and support learning.

Writing First. AI Second.

Imagine a teacher who is explicit with students about the purpose of their learning and how technology fits into it.

If the goal is for students to learn and gain experience in how to write an “About Me” story, including understanding structure, voice, and clarity, then AI is not part of the initial drafting process. Students begin by writing on their own because the skill they are developing is writing. They also end up with a class set of stories that helps classmates get to know each other better, which is part of the purpose, too.

Once that learning goal is met, AI becomes a support rather than a shortcut.

Students can paste their completed drafts into an AI tool and ask targeted questions such as:

• Are there places in which the story is unclear?
• What questions does a reader still have?
• Is anything missing that would help a reader understand me better?
• Are there grammar or structural issues I should fix?

At this stage, AI functions like a personalized writing tutor, offering feedback that helps students revise with intention. From there, students might push their work further by asking AI to help them transform the story into a poem, a song, or another format. This is not about replacing student thinking. It is about extending student work.

This mirrors what I outlined in The New Writing Process in the Age of AI, in which writing is no longer a single linear act but a cycle of drafting, feedback, revision, and reimagining. AI does not replace writing. It expands what happens with the students and their work.

The key is transparency. Students understand why AI is being used and why it was intentionally excluded earlier in the process.

Math, Struggle, and Support

The same principle applies outside of writing.

In math, the learning goal is often reasoning, not simply arriving at an answer. Students need space to wrestle with problems, test strategies, and make mistakes. AI should not short-circuit that productive struggle.

This is where tools such as Khanmigo can play a valuable role when used correctly. Students attempt a problem first. Only after that initial effort do they turn to AI for guidance.

Instead of giving answers, AI can ask probing questions, explain alternative approaches, or help students identify where their thinking went off track. Used this way, AI functions as a tutor, not a solution engine.

Again, the distinction matters. The learning goal drives the use of the tool, not the other way around.

The Conversation Matters More Than the Tool

Across subjects, what separates responsible AI use from misuse is not the sophistication of the technology, it is the conversation educators have with students.

Students become far more thoughtful users of the technology when they understand:

• What they are learning
• Why they are learning it
• How AI can support that learning without replacing it

This approach pushes back on the simplistic narrative that AI is something schools must either ban or blindly adopt. When educators design learning intentionally, AI becomes another tool students learn to use with judgment, purpose, and creativity.

The question is not whether students will use AI--they already are.

The real question is whether schools will help students understand when it strengthens learning and when it gets in the way.

NotebookLM was developed at Google by bestselling science author Steven Johnson and members of Google’s AI team. I’ve long admired Johnson’s work and compelling ways of thinking on a variety of topics, so it’s no surprise that NotebookLM has become one of my favorite AI tools.

It’s also unsurprising that it’s also a favorite of many educators with whom I speak. That’s because NotebookLM does all the things we normally associate with good education-focused AI use. It puts teachers in the driver seat, allowing them to decide which sources the tool draws upon for all the materials it creates.

Even more significantly, NotebookLM is, at its heart, a research assistant, and is fundamentally designed to help users organize and learn from various sources they gather rather than do their thinking for them. In other words, it truly works as a notebook for the AI age.

Here are some ways in which teachers can use NotebookLM.

1. Teaching With NotebookLM: Use It As A Research Tool

These days, NotebookLM is a good starting point when you begin researching any topic. It gives you the option of choosing “fast research” or “deep research.” Deep research takes more time but goes more in-depth. Regardless of which option you select, you are then given a series of sources that you can choose to keep or discard.

Below, I talk about some of the cool things NotebookLM helps you do once you’ve gathered this research, but before getting to that, I want to say how helpful this tool is in its own right. Sure, other AI tools can search the internet for sources, but here you not only get the sources, it all is automatically saved and organized within an easy-to-use interface. I find this helpful when doing research for books and articles as well as for research for class prep, but of course, this organization is only the beginning.

Both deep and quick research options work very well, depending on your needs. I've found that starting with a quick research option, exploring those sources, and then having NotebookLM do deeper research to unearth more resources, can be very effective.

2. Interact With Research and Educational Materials

Once you get research results or add your own documents and materials, which is also a great option within NotebookLM, you get a whole bunch of AI-enhanced ways to organize and learn from this research.

First, you can interact with a chatbot that uses these sources, and only these sources, as the basis for its responses to you. This can be a fun way to delve into a topic and, literally or figuratively, interrogate a text.

For example, I was reading a paper about a complex physics experiment and didn’t fully understand certain implications. I asked a few questions, and the NotebookLM chatbot read through this paper and other similar ones I had selected and clarified various points.

Of course, even with the guardrails that NotebookLM inherently places on these interactions, it might not be perfect, but it’s still a helpful learning tool as you conduct and think through research. It could also theoretically be a good and fun way for students to explore class materials you assign, though I don't personally use NotebookLM in that way.

3. Create Class Materials From Specific Documents

You can also then use these documents to do many of the tasks AI is already helping teachers with, such as generating quizzes, flashcards, mind clouds, and even slides. This can be either a tool for organizing your own thoughts and research, or the first step to creating some class materials.

None of the materials I've used NotebookLM to create are perfect, but it is generally better than other programs I’ve tried. Since it is based on specific materials I had approved, I also feel much better about using this as a starting point than I would working with a more general AI tool.

4. Create Podcasts and Videos

Creating on-demand podcasts and videos that summarize documents and readings you select is one of the features for which NotebookLM is known. The podcast feature, in particular, drew the lion’s share of press after the tool launched, and from a purely technical standpoint, it's amazing.

You put in a few documents and, soon after, you can listen to a casual podcast with multiple hosts, breaking down the topic for you in the manner of a modern laidback podcast. The video is somewhat similar but offers visuals and graphics along with narration.

As mind-blowing as both features are, neither is my favorite, as I still don’t trust this kind of summary from AI. That said, this feature is one that a lot of teachers use. So even though it’s not my personal favorite feature, I’m intrigued by it and agree there’s a lot of educational potential for this kind of tool.

I also recommend everyone try creating their own podcast using NotebookLM because, from a pure wow factor perspective, it's still hard to beat.