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

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

Here’s a closer look at Dabble.

What is Dabble?

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

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

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

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

What Are Dabble's Best Features?

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

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

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

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

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

What Are Some Areas That Dabble Could Improve?

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

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

How Much Does Dabble Cost?

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

What Are The Best Dabble Tips and Tricks?

The Scene Feature

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

Tracking Dally Progress

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

Exporting Material

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

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

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

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

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

1. Do Not Use AI For: Assessment

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

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

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

2. For Brainstorming

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

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

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

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

3. For Lesson Generation

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

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

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

4. For Emails

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

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

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

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

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

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

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

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

Neo vs. Chromebook: Overview

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

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

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

The Hardware Story

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

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

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

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

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

A Student’s Perspective

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

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

This next section is written by her:

Five Things I Noticed in the First 10 Minutes

By a 9th Grade Student

1. The Apple screen made everything look way better.

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

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

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

3. The Apple trackpad was awesome.

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

4. The bigger screen actually mattered.

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

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

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

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

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

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

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

“The 50-tab Challenge”

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

Outcome: Tie

“The Video Project”

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

Edge: Neo

“The Offline Day”

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

Edge: Neo

Things The Spec Sheet Won’t Tell You

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

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

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

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

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

Questions Districts Should Ask Before Buying Either

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

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

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

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

Conclusion

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

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

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

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

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

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

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

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

The Personification Two-Step

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

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

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

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

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

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

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

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

Crafting A Partnership

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

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

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

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

The Bottom Line

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

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

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

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

From the Data: Understanding the AI Digital Threat

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

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

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

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

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

Actionable Step 1: Empower Teachers Through Specific Safety Training

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

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

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

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

Actionable Step 2: Build Proactive Parent Awareness

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

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

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

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

Actionable Step 3: Provide Meaningful Layered Student Support

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

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

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

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

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

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

Stepping Into the Future Together

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

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

Resources

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

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

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

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

Here’s a closer look at TeacherServer.

What is TeacherServer?

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

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

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

What Are TeacherServer's Best Features?

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

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

What Are Some Areas Where TeacherServer Could Improve?

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

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

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

How Much Does TeacherServer Cost?

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

What Are The Best TeacherServer Tips and Tricks?

Search TeacherServer By Subject

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

Search Most Popular Tools

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

Be Critical

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

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

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

What is Copilot Study and Learn

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

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

Where Copilot Study and Learn Earns High Marks

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

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

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

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

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

Protons are made of quarks

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

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

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

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

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

Where Copilot Study and Learn Could Use Additional Supports

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

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

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

Bottom Line: Copilot Study and Learn

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

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

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

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

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

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

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

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

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

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

Understanding Social Penetration

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

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

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

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

Managing Student-AI Relationships

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

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

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

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

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

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

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

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

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

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

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

Boosting AI Literacy

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

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

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

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

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

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

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

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

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

Watch above or listen/download below.

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

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

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

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

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

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

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

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

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

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

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

1. Differentiated AI Use

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

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

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

2. AI Literacy That Goes Beyond Prompt Engineering

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

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

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

3. An Emerging Trust Issue

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

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

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

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

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

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

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

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

5. AI Creating New Opportunities

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

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

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

It featured two sessions:

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

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

Watch on demand here

The Cutting Edge: New Features from Top AI Solution Providers

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

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

Brisk Teaching

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

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

The platform's "Brisk Brain" is built on two foundations:

• District Guidance: Content-agnostic priorities such as instructional focuses and a Portrait of a Graduate.
• Curriculum Libraries: Resources mapped directly to a district’s high-quality instructional materials.

“Brisk works where you do,” said Bostwick. “You can always leverage the extension to open up over top of anything that you are creating. You can see how easy it is to open the extension right over top of any YouTube videos, docs, web pages, slides, and more.”

The Curriculum Intelligence framework ensures generated resources, such as lesson plans and assessments, are aligned to state standards and district-level goals. A forthcoming feature, Planning Mode, will facilitate dynamic conversations between teachers and Brisk, suggesting district instructional strategies based on student needs.

For differentiation, Brisk offers student-facing tools such as Boost activities and a Tutor that apply customizable guardrails and scaffolds. This helps maintain "productive struggle" while meeting diverse learner needs through multi-language support, speech-to-text, and read-aloud functionality.

For curriculum directors, Brisk provides the benefit of one secure AI layer across all adopted curricula, saving time and capacity by customizing static core materials with district priorities. Brisk is safe and secure, adhering to COPPA, FERPA, and GDPR compliance.

Copyleaks

Senior Solutions Engineer John Michael Perla demonstrated Copyleaks, an academic integrity platform designed to guide students in the responsible and ethical use of generative AI. It integrates directly into the LMS environment, such as Canvas.AI Detection and Customization.

Copyleaks’ AI detection settings are customizable by the school—a top-down approach—with three levels available:

• Level One: Detects content copied directly from LLMs with no modifications.
• Level Two (Default): Identifies submissions in which minor edits were made to the LLM-generated text.
• Level Three (Strictest): Designed for high usage of LLM-modified content, including content generated with sophisticated prompts or evasion tools.

“Within Copyleaks, you can also customize the plagiarism settings,” said Perla. “So if you want to pull in data, we can identify content from the entire Internet, which is like seven billion web pages. We could also create a private cloud hub for your school, which will be all your school's data, in one cloud repository.”

A key differentiator for Copyleaks is its use of logic to explain the AI detection score, preventing instructors from relying solely on a percentage. This logic is provided through two patent-pending tools:

• AI Source Match, which identifies specific web pages or articles that the LLM used as a reference to generate the student's submission.
• AI Phrases, which detects phrases used with high frequency by AI tools.

Copyleaks is a top-tier partner with Canvas and D2L, and also integrates with Moodle, Blackboard, Brightspace, Schoology, and Sakai.

Inkwire

Dr. Julianne Ross-Kleinmann of Ulster BOCES demonstrated Inkwire, an early-stage, project-based learning (PBL) AI tool that supports deeper learning and accelerates authentic instructional design.

Inkwire consists of two main components:

• The PBL AI Copilot, which helps educators design PBL experiences aligned to state standards
• The Portfolio Platform, through which students can capture and showcase evidence of their learning.

“What stood out for us is that Inkwire is focused specifically on teaching and learning and not just a generic AI tool letting educators figure it out on their own,” said Ross-Kleinman. “Instead, it helps teachers think intentionally about authentic learning experiences.”

The design process utilizes the High-tech High Graduate School of Education kaleidoscope framework, which is customizable to a district's own instructional frameworks. Teachers can input a design idea, upload files, and then ask the AI to generate core project elements such as essential questions, launch ideas, and assessment types.

The output is a "one-pager report" that includes learning goals, including state-specific standards, competencies (which can be aligned to standards such as Portrait of a Graduate), and a day-by-day or calendar-view plan.

Inkwire is designed for teacher collaboration, allowing educators to co-design interdisciplinary projects. The portfolio section is crucial for assessment, as it can house student work and artifacts, supporting statewide initiatives by following students as they move between schools and tracking seals of alignment.

The Integrity Infrastructure: How to Ensure AI Solutions are Safe

Tech & Learning’s second "EdTech to Watch" webinar, hosted by brand manager Christine Weiser, explored responsible AI integration in K-12 education. The event showcased a pair of product solutions–TrekAI and Lightspeed Systems Insight–and emphasized the importance of safety, governance, and personalized learning. Attendees got to see demos of both tools and ask questions, and then were treated to a discussion of vetting AI tools from AI consultant Greg Bagby.

TrekAI

TrekAI functions as an on-demand, Socratic tutor for students. Designed to replace chaotic or unvetted AI usage, it serves as a "walled garden" that provides students with a supervised "learner's permit" experience.

“We're just handing the keys over to our students, and we're just kind of like, ‘Okay, just go figure this whole AI thing out,’” said Erin Burchik, Chief Growth Officer for TrekAI. “ChatGPT and other platforms kinda feel like this chaotic, overwhelming, unsafe interstate. And I don't know about you, but when I taught my kids how to drive, we started on back roads and parking lots. We didn't go straight to the interstate. And so we want to advocate for the time that students are in your care, you give them an environment to learn how to navigate this new world of AI safely.”

TrekAI aligns with district standards and curriculum, resisting plagiarism by guiding students through problems rather than providing instant answers. It is FERPA and COPPA compliant to protect student data privacy. It also includes social-emotional alerts that notify counselors and administrators if student interactions indicate risks such as self-harm or violence.

Another offering, Trek Adventure, is a hyper-personalized literacy tool that transforms standard curricula into choose-your-own-adventure books. By integrating specific educational standards (such as math concepts involving fractions) into the narrative, the tool reinforces classroom learning while keeping students engaged. It offers accessibility features, such as dyslexic-friendly fonts, and provides teachers with insights into student reading progress, making it a practical tool for guided reading rotations and literacy blocks.

“Almost all of our team are former educators, so when we're building out new products like Trek Adventure, I think about my sixth grade science class when I was a teacher for fifteen years,” said Joe Edlhuber, account executive, “I would have used Trek Adventure every week during my guided science rotation. It's really practical, not just fluff tech. It is really something that teachers should be using and students wanna be using every week.”

Lightspeed Systems

Insight from Lightspeed Systems provides districts with total visibility into their digital ecosystem, allowing administrators to identify exactly which AI tools are being used, by whom, and for how long. Given that schools often struggle to manage the hundreds of apps present in their environment, Insight’s analytics hub helps identify usage trends and distinguish between productive AI engagement and potential misuse.

“What people find is whether you've built an approval system or not, AI is already in your environment,” said Sergio Villegas, former educator and current regional sales manager for Lightspeed Systems. “What we need to be able to do is answer questions better. So our dashboard is gonna show you everything.”

Insight also offers automated monitoring of vendor privacy policies, alerting districts of changes in terms to ensure ongoing compliance.

Lightspeed Alert automates safety monitoring by analyzing student interactions in real-time. It focuses on identifying concerning behavior, such as indicators of self-harm or violence, across AI platforms and web activity. When an incident occurs, the system shows evidence—including screenshots and transcripts—to administrators, allowing for rapid, human-led intervention.

“This is kind of the wrap-around story of why having a consistent ecosystem matters because these systems need to talk to each other,” said Villegas. “It's hard for schools because we're all being tasked with too much on our plate and not enough people and resources to do it. So having a single ecosystem that can surface information, that can let you search deeper, that can automatically notify you, is important.”

By connecting directly to student information systems, the tool ensures that school staff can reach out to families or take appropriate action quickly, moving schools away from reactive, "he-said-she-said" scenarios.

Strategic Context: The AI Vetting Framework

To complement these tools, AI specialist Greg Bagby outlined a strategic roadmap for vetting AI technologies.

“Before adopting any tools, districts should ask this question: What problem is it solving?” Bagby said. “Is this a tool with clear goals for tech that we need to support? Is it gonna give personalized feedback? Is it for lesson design, assignments, tutoring? And who's gonna benefit from this tool? Is this for the teachers, the students, the school leaders ? We need to have all this in mind before we start jumping into tools because we have access to many, many tools, and why not understand the why before we jump in.”

Bagby said that districts should avoid working in silos and instead form inclusive task forces that include stakeholders from IT, curriculum, and, crucially, students.

Bagby’s framework underscores that AI must prioritize human-in-the-loop oversight, cultural bias testing, and rigorous data privacy agreements. He concluded that because AI technology evolves daily, district policies should be viewed as living documents, requiring constant dialogue to ensure they remain safe and reflective of the current learning environment.

“Just make sure that you avoid vendors who won't disclose how student data is being used and how AI is being trained,” Bagby added. “If it's using the student data to be trained on, that's one of those things that we wanna make sure that we always step away from and be cognizant of.”

Watch above or listen/download below.

Cybersecurity has topped CoSN’s annual State of EdTech report for at least eight consecutive years. For Jeff Carlson, Head of Education Strategy and Advocacy at Clever, that streak is not a failure of imagination — it’s an honest reflection of how hard the problem actually is.

“Even if you’ve solved one thing, there’s another thing,” Carlson said during this week’s Tech & Learning podcast conversation. “K-12 has become one of the most targeted industries for cyberattacks, and the threats keep evolving.”

Clever, which provides single sign-on and rostering services to between 75 and 80 percent of U.S. schools — both public and private — has watched that threat landscape shift in real time. What once centered on credential theft and unauthorized access has expanded to include third-party vendor vulnerabilities, with Carlson citing an eightfold increase in two years of districts worrying about attacks on vendors that then cascade into school systems. The data at risk is not just administrative: it includes student health records, special education documentation, and mental health communications.

“The biggest concern we hear from tech teams is that student data is going to be released,” said Carlson, who spent time as a special education teacher before moving into ed-tech strategy. “You’ve got health data, comments going back and forth about mental health challenges. That’s not just a technical problem.”

The arrival of AI has added a new layer of complexity. Carlson described a moment that has become familiar to many district leaders: waking up to find that an app updated overnight and now includes student-facing AI features no one vetted or approved. His advice is pointed — focus on enabling AI for educators before enabling it for students, prioritize tools built specifically for K-12 over consumer products retrofitted for classrooms, and establish clear data minimization standards that apply whether or not a vendor calls itself an AI company.

“We’re pretty quickly moving from having AI companies to just companies that are using AI,” Carlson said. “We used to have internet companies. Everybody has a website now. We can’t assume that just because somebody hasn’t told you they’re using AI that they’re not.”

For district leaders feeling overwhelmed, Carlson’s closing message was direct: take a breath, communicate openly with families and staff before a crisis forces the conversation, and treat AI governance as an ongoing dialogue rather than a policy set once and forgotten.

“The best thing you can do with fear is try to understand where it’s coming from,” he said. “And see how you can get over it one step at a time.”

Recent data from the 2024–25 school year continue to illustrate the persistence of academic achievement gaps for K-12 students. In response, headlines have blamed "screen time" and the purported detrimental effects of technology for much of this decline. From EdWeek to the New York Times, articles have presented a gross oversimplification; the story is more complicated.

Digital tools and programs present schools and systems with more durable, sustainable strategies to accelerate learning. During the 2024–25 school year, FullScale (formerly The Learning Accelerator) funded and evaluated 10 innovations through the Accelerating Adoption Network — six edtech tools, three programs, and one microschool model — each designed to scale promising, technology-enabled, evidence-based solutions.

Through this initiative, we learned about the complexities that emerge when evidence-based tools meet reality at scale.

3 Critical Lessons About Scale

The Accelerating Adoption Network sought to evaluate how these innovations scaled in terms of breadth, depth, and durability, as well as the impact on student learning, using a combination of teacher surveys, leader focus groups, and student outcome data (e.g., standardized assessments or course grades).

While the networks successfully scaled their breadth — expanding across 33 states, 130 sites, and impacting more than 3 million students — the depth and consistency of implementation varied significantly both across and within schools. Consequently, though student outcomes showed improvement over the course of the school year, the gains were not statistically significant. Regardless, most educators reported durable changes to instruction, with 62% reporting that the innovation positively influenced their instructional practices and 82% indicating that they wanted the work to continue.

The networks scaled, but the more interesting question is how. Three core learnings from the evaluation point to barriers that successful scaling need to overcome:

1. Implementation incoherence: The timing of the rollout, competing interventions, infrastructure challenges, or lack of consistency between the innovation and school or district priorities led to variation in implementation.
2. System conditions: Teachers largely found the innovations beneficial and easy to use, but system conditions such as from insufficient devices and bandwidth to competing tools and unclear expectations influenced implementation.
3. Research and evaluation infrastructure: Data-sharing agreements and institutional review processes, combined with rigid study designs, prevented agile and responsive decision-making, and limited our ability to gather interim data or revise research questions based on reality.

Despite current narratives about “screen time” and edtech efficacy, the Accelerating Adoption Network produced evidence of successful scaling. The included innovations reached more students, demonstrated growth on academic outcomes, and led to teachers making durable instructional changes, but this evidence is incomplete.

Evidence-Based Is Necessary, But Not Sufficient

Successful scaling depends on more than just having a high-quality, evidence-based tool. It requires a set of system conditions to be in place.

In our work with the Accelerating Adoption Network, we identified five interrelated elements that enable scaling. These cut across contexts and roles, offering actionable guidance for funders, policy-makers, and system-level decision makers.

• Strategic Alignment Before Adoption: Decision-making and conversations about edtech should begin with instructional strategy, not procurement. Clearly communicate how an innovation advances priority goals, what it replaces, and what it deprioritizes.
• Protected Time and Instructional Coherence: Make explicit decisions about when, where, and for whom an innovation should be implemented, not just used, and how it connects to curriculum, pacing, and instructional goals.
• Implementation Capacity: Build implementation capacity deliberately at multiple levels, ensuring educators have access to timely coaching, technical assistance, and leadership support within sites, while establishing routines that surface implementation patterns across sites.
• Enabling Infrastructure and Operational Readiness: Before scaling, ensure schools have reliable access to devices and connectivity as well as technical support.
• Continuous Improvement and Learning: Use formative data such as usage patterns, educator feedback, and interim student indicators to guide adjustments throughout implementation rather than waiting for end-of-year results.

Scale requires conditions that go beyond individual tools to include broader system readiness. As funders, policymakers, and leaders take an ever more critical look at the efficacy of edtech, the focus should include both selecting strong innovations and creating the conditions that allow educators and systems to learn, adapt, and improve over time so that the field can move beyond isolated successes and toward durable, equitable learning for more students. To do so, before adopting or scaling a new tool, it is critical to first ask these three questions:

• How does implementation of this tool fit coherently into our context?
• Do the necessary system conditions exist to ensure successful implementation?
• What research and evaluation needs to occur to support continuous improvement and to determine whether the tool achieves its intended outcomes?

When Kadion Phillips first joined Shrewsbury Public Schools in Massachusetts as Director of Information Technology a few years ago, one of the first tasks on his agenda, like many other district leaders, was tackling how to implement artificial intelligence.

To that end, Phillips and district superintendent Joseph Sawyer established a special committee of approximately 20 staff and faculty members to address the rapid development and implementation of AI.

Ultimately, the Shrewsbury AI committee came up with five pillars to guide the district’s use of AI:

1. Preparing students for an AI future - Focusing on readying students for a future in which AI tools will be infused throughout society, higher education, and the workplace.
2. Effectively utilizing AI learning tools for students - Using AI tools to appropriately support student learning across all classrooms.
3. Implementing AI working tools for staff - Finding the right AI platforms to help faculty and staff to work more efficiently and effectively.
4. Establishing guardrails against inappropriate AI use - Putting effective rules and best practices in place to protect data and discourage inappropriate use of AI.
5. Ensuring AI practices promote academic integrity - Creating an effective framework for acceptable use of AI by students in the context of academic integrity.

Rather than selecting specific tools, the committee has worked to align these pillars with the district's "Portrait of a Graduate" skills, such as critical thinking and innovation.

“We also started creating a guiding document, more of a framework, that we're hoping to get out to staff in terms of, ‘Here's where we are right now, and here's what we're trying to get to,” Phillips says. “We're also developing a one-pager of AI dos and don'ts to release this year.”

For this and his other work in his district, Phillips was recently recognized with a Tech & Learning Innovative Leader Award. He shares his continued efforts around AI and for improving the district’s cybersecurity.

A Work In Progress

The son of a math teacher from Jamaica who eventually went on to be a school principal, Phillips was exposed early to the possibilities of a career in education and technology. He was working in tech support for a startup company near Times Square in New York City with plans on going to grad school for physics when he heard that there was a local teacher shortage.

“I decided the best way to study for grad school is to teach physics,” Phillips says. He became a teaching fellow since he didn’t have a teaching degree, which was the shortest path to certification. “I was basically kind of thrown into the classroom in New York City after doing a summer intensive, and it was definitely rewarding but also challenging.”

While teaching, his tech expertise helped him become the de facto tech guy at his school, helping to resolve any issue, from fixing the copier to helping in the computer lab to running data analysis. Although he eventually became an assistant principal, following in his mother’s footsteps, a colleague suggested another path.

“I met the tech director in Rochester and he was telling me, ‘Hey, this is what you should do, you should be a tech director,” Phillips recalls. “And I'm like, ‘a tech director, what’s that?’ I'd never heard of that role. I know you had one person help send emails, but I never really thought somebody was in the back end coordinating it all.”

Phillips and his wife, also an assistant principal in NYC, were looking for a better work-life balance at this point, so they moved to suburban Massachusetts where she had been raised. After diving into tech leadership positions in a few districts, Phillips eventually ended up in Shrewsbury about two years ago, just in time for AI’s arrival in education.

As mentioned, Phillips has been proactive in implementing AI, although there are always challenges, such as staff who still are resistant to allowing it in their classrooms because they can’t get past the potential for cheating, and navigating the student data privacy aspects. Like for many districts, it continues to be a work in progress.

“We aren't necessarily picking a tool and saying this is what you need to use but it's like thinking about, ‘Hey, what are the opportunities where AI could be useful?’” Phillips says. “We figured out the skills that we want kids to get with AI is going to be similar to what we did with our portrait of a graduate, such as critical thinking, content mastery, innovation, resilience, collaboration, and even the global citizenship piece.”

‘Locking Down Without Locking Out’

In addition to a focus on AI, Phillips has prioritized cybersecurity as a foundational element of the district’s technology plan, which has earned his team a Massachusetts Governor’s Citation for Municipal Cybersecurity.

“We try to lock things down as much as possible,” says Phillips, adding that they have multifactor authentication on all systems now. “We also have tiered access, like limiting who needs to get access to what. So like our main account doesn't have access to our domain controller, for example, so even if our regular account were to get compromised, it wouldn't have any access to our other internal systems.”

Or, as he puts it, “locking down without locking out” users.

Despite his best efforts, however, Phillips knows cybersecurity is sometimes out of his hands.

“It's just one of those things where we're trying to do best practices but the vendors for our most critical systems, we also have to trust that they're also doing the same,” he says, noting some recent high-profile data breaches. “I think that's what keeps us IT workers up at night, you know, showing up in the morning and seeing that your system potentially could be compromised.”

The district utilizes a state-funded grant to provide training and conduct phishing campaigns, which has improved staff awareness and the reporting of suspicious activities.

Even so, cybersecurity needs to be a constant commitment.

“You can create all this security, do all the training, and build this wall around it,” Phillips says. “But then somebody just leaves the door right open.”

Tools They Use

• Google for Education
• Schoology
• Seesaw
• ParentSquare
• Zoom
• Apple MacBooks and iPads
• Epson
• Fresh Service

Back in 2010, my school district embarked on the first-of-its-kind (in Texas at least) one-to-one device roll-out. The device du jour at the time was the Apple iPad. While billed as a consumer device, its price point (back then 16GB went for $499) and ease of use made it an intriguing option for education. Sure, there were no real ways to manage it back then (I don’t think the phrase MDM had even been invented yet), but the increased access to our students was a major bonus as we stepped deeper into the 21st century.

The next year, Google decided to enter the market and introduced its own education-inspired device, the Chromebook. These devices were a little more robust than the previously attempted cheap laptop market (anyone remember those little “netbooks”?). While those initial models didn’t offer the touch-screen benefit of an iPad, they did have a better price point (around $299) and could be managed through the Google Admin Suite.

Where Apple tried to distance itself from comparisons, spouting its design and creativity-first belief, it was inevitable there would be a standoff. “Creativity is nice and all, but what about state testing?!” the people would shout. “What about a keyboard!?” the masses would cry. Add to that the nearly $200 in savings and the ability to manage, and it's no wonder why the iPad would lose out to its keyboard cousin. According to Ed Week’s Market Brief report, the Chromebook overtook the iPad in terms of preferred educational student device sometime in 2014.

While myself and many other tech leaders waited for Apple’s response, the ChromeOS market continued to dominate, fueled even more by a global pandemic and quick access to funds (ESSER) for devices.

“The One” Arrives

As of 2025, ChromeOS currently dominates the global education market with a 60% adoption rate. Apple’s response? It lowered the price of the iPad, but schools still needed third-party MDM systems to manage all the intricacies of these devices.

Flash forward to this year. Now fully 17 years since the debut of the iPad, Apple has re-entered the education market with its 13” Macbook named . . . Neo. Apparently this isn’t a nod to the mercurial Keanu Reeves character from The Matrix, but rather to convey a sense of “fun, friendly, and fresh.” According to Meriam-Webster, “neo” is a prefix defined as new, recent, or different.

Whatever the reason, the announcement on March 4, 2026, sent shockwaves through the education market. Its price for education buyers? $499 – the same as those first iPad2s I bought way back when. So after all these years, Apple is back.

The question isn’t whether the Neo is a good device, but if the conditions that doomed the iPad’s educational takeover have changed enough for Apple to finally win.

Unlike 2010, Apple enters back into a market that is highly volatile on multiple fronts. Education funding is in disarray and we are in the midst of “the Great Edtech Backlash” in which families are insisting that schools crack down on screen time. Some, such as LAUSD, are complying with their own guidelines, but the truth is, the device market in education is ever-fragile at the moment. Heck, even the new villain in Toy Story 5 is an educational tablet device called a Lilypad.

It’s at this moment when Apple decided to reengage with the education market. While timing isn’t great, some schools are already starting to put in orders for Neos for their students. Kansas City Public Schools recently purchased more than 4,500 of the budget-friendly Macbooks and now proudly claim to be an “all-Apple district.”

While KCPS have placed their flag firmly in the ground with the Neo, other schools are looking at how to stretch their limited funds. Some have said that replacements are starting to run low, a combination of less funds and greater supply chain issues due to global tariffs and trade disputes. Unlike previous Chromebook refresh cycles, the tariff-related pricing volatility and procurement uncertainty are forcing technology leaders to place orders earlier or get more creative around their deployment schedules and scope.

Helping To Stretch Dollars?

We can sometimes be a victim of the latest new, shiny object in education, but given the current landscape I mentioned above and our previous forays into student device deployments, it's wise for technology leaders to take a more cautious approach this time around. Procurement of the Neo doesn’t seem to be an issue yet, even though early demand has surpassed expected supply.

Apple also seems to be addressing the MDM issue. While they do have their own Apple School Manager platform, they also have partnered with Jamf to develop a more seamless approach to deploy, track, and maintain devices. They are also touting the longer shelf-life of their devices, which I have experienced first hand.

Typical Macbooks can last as long as 8 years, which dwarfs the 3-5 years many Chromebooks typically last (dependent on the model type). While modern Chromebooks can now receive up to 10 years of ChromeOS updates, most school districts still budget for replacement after approximately 4-5 years because of physical wear and tear, battery degradation, and student use patterns. Will the Neo have the same issues? Not if it keeps with Apple’s previous track record.

In an era when educational funding is short, anything schools can do to stretch dollars may be enough to put Neo on top. One of the hidden benefits of our iPad one-to-one program was that after we had used them for 4-5 years, we could still sell them at a decent rate to recoup some of our costs. Based on the historical performance of MacBooks and other Apple hardware in secondary markets, there’s no reason to believe the Neo will be much different.

The true cost of a device isn’t necessarily what you pay on day one; it’s what the device costs after accounting for its value at the end of its lifecycle. If your $350 Chromebook is worth almost nothing in four years but your $499 Neo can be sold for $200, which device actually costs the district less?

Gearing Up For The Head-To-Head Battle

The aforementioned “Total Cost of Ownership” is a conversation schools should be having anyway, but one that Apple feels it will win out on in the long run. That and the same argument it made during the launch of the iPad.

For the everyday student tasks, both devices do pretty well. Whether it’s accessing the Learning Management System (LMS) or writing and researching online, both devices handle that without much difference. Chromebooks excel at using Google’s suite of tools (not a shock there), but struggle when you start getting more involved in creative pursuits. Activities such as video editing, podcast production, music creation, graphic design, and software development are areas in which the Neo leaves the Chromebook in the dust. And . . . it can handle state testing! (Sarcastic cheers of rejoice.)

Of course, none of this matters if districts can’t absorb the additional upfront cost. A superintendent facing budget cuts may have a hard time convincing a school board to spend $499 today in order to save money four or five years from now. Even with the Total Cost of Ownership argument.

On paper, both devices make compelling arguments. The Chromebook wins on familiarity, price, and ease of management. The Neo counters with longevity, creativity, resale value, and Apple’s growing AI ambitions.

But spec sheets don’t tell the whole story.

Over the next few weeks, I’m going to put both devices through the kinds of tasks students actually perform every day. Writing, research, video creation, coding, offline work, state testing, battery life, and yes–the inevitable moment when a student manages to have 47 browser tabs open at once. Since I already mentioned The Matrix, I would be remiss if I didn’t put both devices through the paces with AI-empowered functionality, too.

In some ways, the bigger question isn’t which device is better, but which one makes more sense for schools to infinity and beyond (yes, another Toy Story reference). And after spending the last fifteen years watching Apple and Google trade punches in the education market, I’m not entirely sure I know the answer yet.

We’ll find out together.

Watch above or listen/download below.

About two-thirds of U.S. districts and states have some form of AI policy in place. The other third is, as Sasha Luks-Morgan puts it, the wild west. And even many of the policies that do exist, she argues, aren't doing what they're supposed to do.

Luks-Morgan is a policy analyst at SchoolAI, a K-12 AI platform they say is built around teacher visibility and student engagement, and she spends her days meeting with districts across the country helping them figure all this out.

In this conversation with Tech & Learning, she offers a clear-eyed framework for where AI policy in schools stands — and what it's going to take to move it forward.

Her three-pillar framework for any district starting from scratch: understand how your students and teachers are actually using AI right now; lock down student data privacy and personally identifiable information; and address academic integrity in a way that keeps students in the driver's seat of their own learning rather than offloading cognition to a machine.

"Systems only move as fast as the humans in them," she says. "That's the starting point."

She also draws a sharp distinction between two policy philosophies that often get conflated. Acceptable use policies, she says, are essentially lists of prohibitions. Responsible use policies are something different — these model good behavior, explain the reasoning behind expectations, and treat students as participants in the process rather than subjects of it.

On the vendor side, she is equally direct: the edtech industry has not taken enough responsibility for making tools work in ways that support good policy. SchoolAI's response is a free policy-writing service — available to any district, partner or not — in which a member of the team will sit with administrators, hear their concerns, and draft or revise a policy document they can take to legal counsel and implement.

"Bad policy is worse than no policy," she says. "We really want to see districts succeeding."

She also addresses district scale: the fundamentals — student privacy, ethical use, data governance — apply universally, from a 108-student K-8 district in the Mid-Atlantic to New York City Public Schools. What changes is implementation, committee structure, and who carries the weight. In small districts, that often means one superintendent or tech director holding the entire AI portfolio alone.

Her advice for the path forward: review your policy annually, build teacher expertise so your bench runs deep, and accept that the technology will always outpace the paperwork. The goal isn't to catch up but to stay close enough to matter.

Since AI came onto the scene, a lot has changed with it while some recurring themes have sustained. AI continues to act as a broad tool that provides remarkably imperfect productivity.

While significant cautions abound assuming its reliability, understanding AI’s usefulness while recognizing its limits makes my day as a principal more efficient AND effective. This brought me to consider what a typical day looks like for me using AI tools to offload administrivia.

With so many options, I wanted to break it down for your use. I don’t expect to use all these tools everyday, but try a few and walk away liberated, with more time and availability for students, staff, and parents. That’s a win for everyone.

I start my day thinking about rising concerns by my faculty over religious opt outs, based on a Supreme Court decision this fall. They were receiving template emails people find online (hilarious when they forget to replace ‘student name’ with their child’s name). My nervous teachers want guidance, so I use AI to clearly and comfortably provide them with direction:

DOE Memo Converted To Slides (Time spent: 8 minutes)

I google my state's DOE guidance on handling parental opt outs and find a state memo that is a great antidote for insomnia. It is the standard boring, lengthy legalease. I add the link to Gemini and select canvas mode.

What I like is it follows my prompt to be pleasant and supportive. The slide deck gets me “mostly there,” my common phrase for AI giving me a terrific start that I edit, ensuring my voice is present. I then export the slides and edit as needed.

This process takes me under 10 minutes, during which I transform a dry, boring memo into a meaningful presentation for my faculty. This would have taken about an hour analog-style, so I get back 50+ minutes to be present for my school community!

Text-To-Email Blast To Parents (Time spent: 5 minutes)

Principals are mediums of communication, and often, are expected to funnel information into a digestible explanation, even when that information is shared in fragmented bursts, as it typically is.

Next on my list is a field day announcement. I need to communicate to ensure everyone is prepared for our big day of events. My physical education teacher sends me this text (at right).

It is timely, but to stop and rewrite it in a cohesive format will take time I often don’t have during a busy day. (Time that is better spent outside my office as improvement doesn’t happen there but in classrooms.)

I copy and paste the phys ed teacher’s text with a simple prompt: Convert this into a message for parents of our school, Central elementary for our field day.

The output is thorough, better than I expect, done in about a quarter the time it would normally take. Better, to get all this into a 140-character text message, I prompt from the output, “140 characters” and voila! This way, parents get an email and a text, increasing the likelihood they see it. All from a single text message!

Letter Of Recommendation (Time spent: 7 minutes)

I can write fast but when I get asked to write letters of recommendation, these all happen within weeks of each other. Drafting a good letter with fidelity takes time. This is the next task on my list.

For this, I have the requester send me their resume, copy it into my chat, and prompt the AI to write a letter of recommendation based on the information–2 minutes!

The other 5 minutes is me editing, revising, removing those weird AI words, and most importantly, adding a sentence or two about something I know personally regarding the candidate. What took a half hour now takes a quarter of the time, and the quality is sound.

Email Summary and Reply (Time spent: 5-10 minutes)

Every principal gets the dreaded email that is seven pages long, often emotional and hard to decipher. It is nearing lunch time and I need to get through it fast!

Typically, I prompt the AI to summarize the email, and then reply in a tone that is kind but firm, or whatever is necessary. This is a big time saver! I am not glued to my laptop, reading and rereading run-on sentences to find the essence of a message buried deep. AI does a great job of siphoning this off and producing a quality reply. Onward!

Sensitive Guidance (Time spent: 5 minutes)

Speaking of emotional challenges, every principal has gotten a charged call or email from a parent upset that a school official called the Division of Child Welfare. This is my next task, after lunch.

While school personnel are compelled by strict legal guidelines, parents see it as a personal attack on their parenting. Understandable. I didn’t get this one right the first time, and have to add my state specifics, as well as the tone: sensitive, understanding, pragmatic, etc.

What is produced is excellent. I am provided references to the legal obligation, integrated with an ideal tone. I edit it, print it, and show the parent. Its well-organized format helps me work through the document, and the parent expresses acknowledgement of my understanding.

Picture Of Calendar Events Added To My Calendar (Time spent: 3 minutes)

My day has to have some fun mixed in, right? It’s mid afternoon and I think of that dentist post card for my next visit that I always lose.

I just take a picture and prompt Gemini to add it to my google calendar. This works with Copilot users, too. Even better, I can use it with an email with a series of meetings or events, or my Penn State football schedule from this ugly screen capture–and instantly, I have 12 events (games) added. That’s fast and reliable!

NotebookLM for navigating lengthy documents (Time spent: 3 minutes)

My day is almost over and I spent most of it out of my office because of the AI tricks above. Next, I decide to check on a contract question.

I head over to Google’s NotebookLM, into which I upload a pdf of the teacher contract, and ask how many minutes of pupil contact time a teacher can have. Boom, 287 minutes with the link to the direct reference!

This is priceless for the administrator who often gets questions and different interpretations of contract language. Time-saver, and response can be within the spirit of contracts.

A few more AI time-savers that I have embraced, but not all on the same day, include PD planning, form feedback, and evaluation feedback referencing our evaluation model. I estimate a few hours saved daily–not minutes–and my school wins when I’m present!

Note: Much of the information I use with AI may contain names or other personal data, so I am sure to remove any of that before prompting.

We’ve all been here. A speaker steps to the front of the room at a conference, workshop, school event, or leadership meeting, and everything stops. The screen is dark. The audio does not work. No one knows the WiFi password. Someone calls for “the tech person.” The device goes to sleep. The audience waits while everyone acts as if this is just part of the program.

It should not be.

Every event space, from classroom to boardroom, should have a dedicated presentation station with a device that belongs to the room. Not the speaker. Not the organizer. Not whoever remembered to bring the right adapter.

The presentation station should be part of the space, the same way the projector, screen, microphone, lights, and chairs are part of the space. When people walk into a room to learn, present, or lead, the basic setup should already work.

The BYOD Presenter Model Is Broken

The bring-your-own-device (BYOD) presenter model turns every speaker transition into a new technical gamble with a different laptop, adapter, audio setting, sleep setting, software update, notification popping up at exactly the wrong time, etc.

That is not a presenter problem, but a room design problem.

A well-run event space should not depend on every speaker plugging in, pairing, restarting, authenticating, and hoping for the best. The better model is simple: one room device, many speakers. Get the device working before the event starts, keep it working throughout the program, and stop making every presenter start from scratch.

That means the host provides a presentation station that is already connected, tested, and ready. The WiFi, browser, display, audio, and shared materials should be checked before anyone is standing in front of the room. The device should not go to sleep mid-session or require a password no one in the room knows.

As I shared in Presentation Checklist: 5 Ways to Prepare for Presentations, presenters have preparation to do, but the device setup belongs to the host. The presenter should be focused on the message, not on troubleshooting the room.

Put The Materials In The Cloud, Not Someone’s Laptop

Speakers should submit materials ahead of time and access these through a shared agenda with a short, easy-to-type link. The room device opens the agenda, each speaker clicks their own materials, and the event moves on.

I have been writing about digital agendas for years. In Anatomy of a Killer Agenda and 7 Benefits to Ditching the Paper at Meetings + Events with a Great Agenda, I explain how a strong agenda keeps links, materials, and next steps in one place. The same logic applies here. Put the work in the agenda, and put the agenda on the room device.

This is not about being fancy but reducing failure points. If the only copy of a presentation is trapped on one speaker’s personal laptop, the event is more fragile than it needs to be. If that laptop will not connect, loses power, needs an update, or has the wrong setting, the whole room pays the price.

Access Matters, Too!

A shared agenda also makes the event more accessible. Participants may need to open slides on their own devices, use screen readers, translate text, enlarge content, follow links, or review materials later. That does not happen easily when the content lives only on one presenter’s machine.

In Checklist for Accessible Teaching & Presenting, I explain why digital materials, microphones, links, and accessible design matter. A presentation station does not solve every accessibility issue, but it removes one unnecessary barrier: the idea that access depends on whatever device the speaker brought.

A QR code can help, but it should not be the only way. As I wrote in QR Codes in Education: Use with Care, Not Just Because They’re There, QR codes are useful when they reduce friction. They become a problem when they create one more barrier. Use the QR code, but also provide a short link for anyone who cannot or does not want to use a phone camera.

The goal is not to look high-tech; it is to make access simple.

What Every Presentation Station Should Include

A strong presentation station should have:

• A dedicated room device connected to the display and audio
• Stable internet already connected
• No unknown password barriers
• Settings adjusted so the device does not go to sleep during the event
• Easy access to the agenda and all presentation materials in the cloud
• A browser ready for commonly used platforms such as Google Slides, PowerPoint online, Canva, YouTube, and shared documents
• Sound, camera, microphone, and screen sharing tested in advance
• Accessibility features available, including captions when possible
• A backup plan that does not require switching to random personal devices
• A designated person responsible for checking the setup before participants enter the room

Use this checklist as the standard operating protocol for successful presentations.

The Bottom Line

A presentation station should belong to the space, not the speaker.

When the room is ready, presenters can focus on their ideas, participants can focus on learning, and hosts can run events that feel smooth, professional, and welcoming. The fix is not complicated: provide a reliable room device, stable internet, accessible cloud-based materials, and settings that support the event instead of interrupting it.

Get the room ready. Put the presentation station in place. Make the materials easy to access. Then let the speaker do what they came to do: connect with the audience and deliver a message worth hearing.

Educational technology teams are often evaluated based on uptime, system reliability, ticket closure rates, and, of course, cybersecurity compliance. Those metrics matter. Schools and universities depend on secure, stable digital environments to support teaching, learning, and operations. Yet, many IT departments unintentionally overlook the most critical metric that directly shapes trust in technology services: customer experience and usability.

When faculty, staff, students, or families contact an IT help desk, they are not simply engaging with a technical system but interacting with people. They almost certainly have a problem and often feel frustrated or rushed for time because they need a piece of technology for a meeting or a class. The tone, responsiveness, empathy, and professionalism displayed during those interactions often determine whether users see the IT department as a collaborative partner or as a barrier to getting work done.

Technology support in education is ultimately a service profession. When I used to hire frontline IT staff, the customer service orientation was what I looked for, as technical skills can be taught. Developing customer service skills is much more difficult.

The Human Side of Technical Support

Educational institutions increasingly rely on technology for every operational and instructional function. Learning management and student information systems, classroom presentation tools, cloud storage, multifactor authentication, and digital assessment platforms have become foundational infrastructure.

At the same time, many educators and staff members are under significant pressure, so when systems fail or access becomes difficult, frustration escalates quickly. In those moments, customer service matters as much as technical expertise.

A help desk interaction may only last a few minutes, but it can influence a user’s perception of the entire institution. Small gestures of professionalism and courtesy make a measurable difference. For example, many institutions now require users to verify their identity through live video before account recovery or password reset procedures can occur.

Security concerns make these processes understandable and often necessary. However, if an IT staff member asks a user to turn on their camera and display identification, basic professionalism suggests the support staff member should activate their own video feed. The interaction should feel reciprocal and respectful.

Turning on a camera, introducing oneself, and explaining the process helps establish trust and transparency. Such actions are simple examples of customer-centered thinking.

Courtesy Is Not Optional

In many educational environments, IT departments unintentionally adopt cultures focused primarily on compliance and enforcement, rife with rigid policies, transactional processes, and impersonal communication. This can result in users avoiding support interactions, faculty members delaying the reporting of problems, students disengaging from required security processes, and staff members creating risky workarounds.

Effective IT support teams understand that professionalism includes patience, clarity, and empathy. Users do not always understand technical terminology, nor should they be expected to. An educator struggling with multifactor authentication during class preparation is not primarily concerned with the underlying architecture of identity management systems; they simply need to regain access quickly so they can teach.

Ultimately, support interactions should minimize stress rather than increase it.

Simple practices can significantly improve service quality:

• Introducing oneself to the customer.
• Explaining why a security procedure is necessary
• Using plain language rather than excessive technical jargon; however, don’t talk down to the user
• Providing realistic timelines for resolution
• Sending routine satisfaction surveys, as nothing is more frustrating than getting an email saying a ticket has been closed when the problem has not been resolved.
• Designing support procedures around user workflows rather than institutional convenience

Educational institutions often speak about relationship building in classrooms and student services. The same philosophy should apply to technology support.

Cybersecurity Must Support the Mission

Cybersecurity threats facing schools and universities are real. Ransomware attacks, phishing attempts, credential theft, and data breaches continue to affect educational institutions nationwide. IT leaders are correct to prioritize strong security controls.

However, institutions sometimes implement security practices that significantly impair usability without meaningfully improving protection. In 1755, Benjamin Franklin wrote, “Those who would give up essential Liberty, to purchase a little temporary Safety, deserve neither Liberty nor Safety.”

IT leaders need to consider this concept when tightening security to the point that it is difficult to use the system being protected. Balance becomes essential. As my favorite university registrar has been known to say, he builds systems that address the needs of 90% of the students and faculty. One of the reasons he is my favorite is that he understands there needs to be enough flexibility in systems to address the needs of the remaining 10%.

If security measures or any institutional processes become excessively burdensome, users predictably seek ways around them. Faculty members may store passwords insecurely. Staff may rely on unofficial tools. Students may disengage from institutional systems altogether. Ironically, overly rigid environments can create additional vulnerabilities.

Edtech exists to support learning, communication, research, and organizational effectiveness. Functionality cannot become secondary to process or systems. This requires IT leaders to ask difficult but important questions:

• Does this security process genuinely reduce risk?
• Is the burden on users proportional to the threat?
• Have we designed this procedure from the perspective of the end user?
• Are we building trust or creating frustration?
• Would reasonable people view this process as respectful and transparent?

Strong IT organizations understand that security and usability are not opposing concepts. Effective security should integrate as seamlessly as possible into the user experience.

Leadership Sets the Tone

Leadership shapes customer service cultures; they do not emerge accidentally. If institutional leaders reward only ticket closure counts and compliance metrics, staff members naturally focus on speed and enforcement. If leaders instead emphasize professionalism, communication quality, and relationship building alongside technical competence, they support cultural change.

IT leaders should regularly review support practices through the lens of user experience. Mystery shopper exercises, satisfaction surveys, and direct feedback sessions with faculty and students can provide valuable insights into how support interactions are actually perceived.

Technical expertise alone does not automatically produce strong customer service skills. Help desk professionals should receive training in communication, conflict management, accessibility, and customer interaction strategies just as they receive technical training. In educational settings, especially, technology teams should remember that every interaction contributes to institutional culture. At the end of the day, users rarely remember the specific technical details of a support ticket but how they were treated.

As noted earlier, Benjamin Franklin famously warned against trading essential freedom for temporary security. The digital world presents a similar challenge. Educational institutions must protect systems and data while still preserving accessibility, usability, transparency, and trust. Technology support should help people accomplish their work, not create unnecessary obstacles to it.

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More than 550 students from the United States, Estonia, Taiwan, and Thailand descended on the University of Wisconsin-Madison last month for the 2026 World KidWind Challenge — and for KidWind founder Michael Arquin, the event was proof of something he’s believed since 2009: give kids a wind turbine to build and they will find a way to get there.

“Despite my best attempts to kill it, it keeps going,” Arquin said with a laugh during this recent Tech & Learning podcast conversation.

The 2026 World KidWind Challenge drew 125 teams of 4th–12th graders competing across wind turbine and solar home design categories. Teams were scored on four dimensions: turbine or solar home performance, a presentation before a panel of judges, an “Instant Challenge” requiring on-the-spot problem solving, and a timed Knowledge Test on clean energy concepts.

The structure is deliberate, Arquin says, because clean energy careers are not only for engineers. “There are lots of careers in the energy space that are non-STEM related,” he says. “We’re trying to tell kids: yeah, you might like the engineering side, but you might like the design part, or the communication side.”

For Morgan Berkgren, a physics teacher from Oakley, Kansas, that message landed years ago. Berkgren, who attended graduate school for biology before being handed a physics class in a small Western Kansas district, discovered KidWind around 2018 and built what became one of the program’s most celebrated multi-team programs.

This year she brought four teams to Madison — one middle school squad, the Twisted Sisters, and three high school teams, Power Breeze, Spirit Winds, and Sea Breeze — all competing in the open home-built generator division for the first time. “I looked at them and I’m like, 'Dude, I went to school for biology,'” Berkgren says. “And they’re like, 'Yeah, Ms. Berkgren, but we want to go to the next level.'”

Teachers and coaches interested in bringing KidWind into their classrooms can get started at kidwind.org. KidWind offers funded virtual workshops and will ship a starter kit directly to educators. Local and regional challenges run throughout the school year, with top teams qualifying for the World KidWind Challenge each spring.

Arquin’s advice for anyone on the fence: “Just start. You’re going to learn a lot along the way — and so will your students.”

A recent book review in The New York Times generated controversy after readers noted it echoed certain passages from a previously published review of the same book in The Guardian. After investigating the incident, New York Times editors revealed the writer who wrote their review admitted to using AI and was unaware the chatbot was lifting ideas.

This is a mainstream example of a new type of plagiarism that has become increasingly common in the classroom. I’ve started calling AI plagiarism to distinguish it from both traditional plagiarism and overall AI cheating.

What’s striking is that it emerges both from clearly inappropriate AI use, such as students entering prompts directly into an AI chatbot, as well as from using AI as a brainstorming partner. As such, it has significant implications for how educators think and talk about appropriate AI use and prevent inappropriate use, at least when it comes to writing assignments.

Thinking about these policies recently has changed my approach as a writer and professor. Here’s how I'm overcoming AI plagiarism.

Warning Students About Dangers of AI As A Thought Partner

Since generative AI emerged as a mainstream force in education, the use of AI as a brainstorming tool has been recommended by many educators as an appropriate way for AI to assist the writing process.

Though I was initially open to this idea, I recently became skeptical of its ability to truly help as a writing partner. Students I work with have not improved their writing by asking AI for advice, and if anything, the writing I read, both in and out of class, has gotten much worse in recent years.

Beyond quality concerns, using AI as a “thought partner” places the user at a high risk of committing plagiarism. Also, if everyone is using it for the same assignments, there's potential for a lot of overlap, intentional or not.

I’ve started to tell students that taking advice from AI is like buying art from an unscrupulous art dealer. You can never be certain that the painting you purchased doesn’t belong to someone else.

Focusing On The Plagiarism Part

One positive about AI plagiarism is that it's frequently easier to catch using an old-fashioned plagiarism detector such as SafeAssign and Turnitin, which search the internet for papers submitted by other students and similar content in general.

In the writing courses I teach, when multiple students start using AI to write their papers, similar setups and phrasings start appearing. It’s easier to confront students about this than it is about potential AI use, which is more difficult to prove.

I’m all for giving students more chances and treating these infractions as teachable moments, but I’m taking a harder line than I once did because AI has made this problem more prevalent. The first time I catch students with a line or two of text that’s identical to another student, they might get off with a warning and a conversation with me. The next time, the penalty will be reported for an academic infraction.

Being Less Strict About Small Mistakes

Another way to discourage AI use in general is to let students know that I won’t be taking them to task for little grammatical mistakes as long as what they write sounds like they’re actually writing.

I never thought I’d say this, but I miss the run-on sentences and tortured, passive sentence construction, and all the other common grammar mistakes I used to see constantly. I’d rather flawed human writing than the flat, passionless, and generic owner's manual-type pieces that too many students are submitting these days.

By encouraging students to truly share their writing, without fear, I’m trying to take away one potential incentive for turning to AI.

Remind Students That AI Writing Is Bad Writing

Many people are convinced AI is making their writing better, but plagiarism concerns aside, it isn’t. I can tell when someone on social media starts using AI to write their posts, and I also see when a student turns to the AI dark side mid-semester. It’s not an improvement.

This all bears repeating for students. Writing can be an emotional roller coaster. I’ve been a professional freelance writer for close to two decades and have averaged hundreds of published stories per year. Despite this, in the course of nearly every story I’ve ever written — including this one — there was a point when I stopped and thought I should contact my editor and tell them the story is just not working.

AI catches students, and even professional writers, in this vulnerable state, and suddenly, its self-confident and immensely digestible prose looks really good. Don’t fall for it! When it comes to writing, AI is a con artist that makes you believe it knows better than you. It doesn’t, and that’s the message we should be sharing constantly with students.

It is graduation season, which also means it is time to remind busy school leaders and administrators about what families actually remember most. Families may appreciate the speeches, decorations, music, and venue, but what they truly care about is the moment their student walks across the stage and hears their name announced properly. That is what they record on their phones, celebrate with relatives, and fondly look back on years later.

Recently, an Arizona community college faced intense backlash after an AI-powered system reportedly malfunctioned during commencement and skipped several graduates while reading names. Too quickly, the conversation shifted toward blaming “AI system failure.”

No, I see this less as a technological issue and more as a failure in school leadership and planning. And candidly, if this critique sounds harsh, it is because this feels like such an avoidable and unforced leadership error. No one should have been asking AI to handle this responsibility in the first place.

The reason this failure resonated so strongly is because commencement is not simply an operational event, but a milestone centered on recognition, dignity, and belonging. Names carry culture, history, sacrifice, and identity, particularly for multilingual families, immigrant families, and first-generation graduates whose educational journeys often represent years of perseverance and hope.

Pronunciation is part of inclusion as names matter. Culturally responsive leadership includes taking the time to learn and say names correctly. That is why this responsibility should receive more care, not less.

Using AI to Support, Not Replace, People

Schools spend months preparing commencement ceremonies. Leaders rehearse speeches, coordinate seating charts, organize music and photography, and carefully plan countless operational details. Yet somehow in the example cited above, the reading of student names was treated as a task that could simply be handed off to automation. Even more troubling was the apparent absence of a meaningful backup plan.

Every experienced administrator understands that technology can fail, which is exactly why safeguards and human oversight should have been built into the process from the start. Graduation season is not the time to experiment with new AI systems in ways that directly impact students and families.

Ironically, AI could have supported this process in far more thoughtful ways without replacing people. Institutions could have used AI tools to:

• Help announcers practice difficult pronunciations
• Review student-submitted recordings of their names
• Identify names that may require additional preparation
• Support multilingual pronunciation coaching
• Give human announcers more confidence and accuracy before the ceremony

This is the kind of AI use education should embrace: technology that supports people rather than replaces them.

The best uses of AI in education should help educators and leaders become more thoughtful and prepared. Instead, too many organizations are using AI to distance themselves from the moments that deserve the greatest care. In the process, schools risk damaging traditions and violating the rightful expectations families bring to these ceremonies.

Ultimately, this was leadership failure rooted in poor judgment, poor planning, and the mistaken belief that a deeply personal moment could be outsourced to automation. Graduation did not fail because of technology, but because leaders failed to protect the experience students and families deserved.

In their new vision for public schools, Randi Weingarten and the American Federation of Teachers (AFT) are right: students need more active, human, hands-on learning. They need projects, movement, collaboration, civic engagement, career-connected experiences, and chances to tackle real problems.

That is the strongest part of the AFT’s 10-point plan. The emphasis on career and technical education (CTE), experiential learning, and preparation for the future of work deserves attention. If we are serious about preparing students for modern careers, students need learning experiences that reflect the tools, practices, and expectations they will actually encounter beyond school.

The 10-point plan’s name, “Devices Down, Eyes Up, Hands-On,” may sound appealing, especially to adults exhausted by distraction, low-quality screen use, and Big Tech overreach. The problem is that it aims at the wrong target. Devices are not the enemy of active learning. Passive, drill-and-kill learning is.

A drill-and-kill worksheet on a Chromebook is still a worksheet. A lecture with a slide deck is still a lecture. A multiple-choice test on a screen is still a multiple-choice test. Technology did not create passive learning. In many cases, it exposed how much passive learning was already there and made it easier to scale.

Not every lesson needs a device. Rather than considering whether screens are up or down, the better questions are: what are students doing, why they are doing it, and whether the tool helps them learn, create, connect, solve, or demonstrate understanding.

The AFT plan includes several worthwhile ideas: stronger privacy protections, independent research, limits on manipulative technology, more hands-on learning, and adequate public school funding--all of which is important. But when the frame becomes “devices down,” schools risk confusing a management strategy with a learning strategy.

While the plan’s name is misguided, it is worth looking at each of the 10 points directly. Some parts are right, some need a better frame, and others would create the opposite of what our students and schools actually need.

What follows is where the plan gets it right, where it falls short, and why “devices down” is not the path to meaningful learning.

Point 1. No screens for pre-K through second grade

The AFT calls for no screens for students in pre-K through second grade unless there is a compelling reason, such as support for a student with special needs.

The instinct is understandable. Young children need play, language, movement, stories, relationships, manipulatives, exploration, and hands-on learning. They do not need to spend large parts of the day watching videos or completing low-level digital tasks.

But “no screens” goes too far. A child passively consuming content is not doing the same thing as a child using literacy tools to hear a story, build vocabulary, practice phonics, illustrate writing, translate a word, enlarge text, or share a recording with family. Those are different experiences and policy should treat them differently.

Accessibility should not be treated as an exception or afterthought. Some students read, write, listen, speak, translate, caption, enlarge text, dictate, or produce work more effectively with digital tools. That includes students with disabilities and multilingual learners, but it also includes students who may not have a formal accommodation. A policy that says “no screens unless you have special needs” can unintentionally stigmatize the students who benefit from access.

The standard should be higher than “screen or no screen.” Screens should be purposeful, adult-guided, developmentally appropriate, and never allowed to crowd out human interaction, play, movement, stories, and hands-on work. That is more demanding than a ban. It requires educators and policy makers to consider thoughtful choices.

Point 2. No student-facing AI in elementary schools

The AFT also calls for no student-facing artificial intelligence in elementary schools, supervised AI use for older students, and a ban on social companion chatbots for students under 16.

Most would agree that social companion chatbots do not belong in elementary schools. Schools should not help companies simulate relationships with children. But banning all student-facing AI in elementary school is too broad. There is a difference between a child forming an emotional attachment to a chatbot and a teacher using an AI-supported tool for translation, accessibility, vocabulary support, feedback, or differentiated practice under supervision.

There is also a difference between using AI to avoid thinking and using AI to strengthen thinking. Responsible AI use should start with the learning goal, not with the tool. Teachers need to help students understand when AI supports learning and when it gets in the way.

Young children should not be using AI freely. However, that does not mean they should learn nothing about AI or never encounter an AI-supported learning experience. They need age-appropriate digital and AI literacy: this includes what responsible and appropriate use is, how it should be questioned, and how to think critically before tools are placed in front of them without context.

Avoidance is not preparation. Guardrails, judgment, and age-appropriate instruction are.

Point 3. Redesign schooling around active and career-connected learning

This is where the AFT plan is strongest. Active learning, project-based learning, experiential learning, and career-connected learning should be at the center of the conversation.

If students are bored, distracted, and disengaged, the answer is not simply removing devices. The answer is to design learning worth paying attention to..

This matters most in CTE. Students preparing for healthcare, media, construction, design, engineering, business, computer science, culinary arts, public service, or green energy will not enter device-free workplaces. They will use digital tools to research, design, diagnose, communicate, manage projects, analyze data, create media, document work, collaborate with teams, and solve problems.

If we say we value career-connected learning while making “devices down” the default, we are sending mixed messages. Modern CTE requires meaningful technology use. Students should not just learn about careers, but practice with the tools of those careers in authentic ways.

Every lesson does not need a screen. But when a device helps students do the work more authentically, collaboratively, creatively, or effectively, putting it away makes no sense.

Point 4. Build literacy, numeracy, and civic engagement

Students need strong literacy, numeracy, and civic engagement. But “devices down” is not how students build those skills for the world in which they live.

Students need to read deeply. They also need tools that support vocabulary development, translation, accessibility, feedback, publishing, and access to high-interest texts at different reading levels. Learning to write in a world with AI, or using a strong digital reading platform well, is not the same as random screen time.

Students need number sense. They also need opportunities to explain their thinking, use data, create tutorials, and learn from one another. When students create a video explaining a math concept, they are not just answering a problem. They are making their thinking visible.

Students need civic knowledge. They also need to understand how algorithms, misinformation, surveillance, platforms, AI, and online communities shape public life. Digital citizenship is civic education now. It cannot be treated as an extra lesson after something goes wrong.

A device-down approach does not teach students how to evaluate information, protect their privacy, manage their digital footprint, communicate responsibly, or participate thoughtfully in online spaces. Schools should recognize students are in these spaces.

Point 5. Focus on well-being

The AFT is right to connect learning with student and family well-being. Students do not learn well when they feel unsafe, unsupported, isolated, anxious, sleep-deprived, or disconnected from school.

But well-being is not simply a screen-time issue. If students are struggling, schools should look at schedules, relationships, homework loads, sleep, movement, food insecurity, school climate, mental health support, family communication, social media, and the habits students are developing in digital spaces.

Digital well-being belongs in the curriculum, not just in the device policy. Students need to understand how notifications affect attention, how platforms are designed to keep them engaged, how to protect their privacy, how to communicate respectfully, when to disconnect, and when a face-to-face conversation, movement, rest, or quiet focus is the better choice.

A ban can make adults feel as if they acted meaningfully while leaving the deeper issues untouched. The goal should be healthier learning environments and healthier digital habits, not simply less device access or fewer minutes with screens.

Point 6. Protect academic freedom, intellectual property, and educator judgment

Educators should not be handed technology because a vendor made a good pitch. They also should not be blocked from safe and useful tools because policymakers are scared. Academic freedom matters. Teachers should have the professional judgment to decide how their students learn best. That includes the ability to choose when a digital tool supports learning, when it gets in the way, and when a mandated curriculum or platform is not serving their students well. A district-approved tool is not automatically good instruction. A required platform is not automatically the right fit for every learner, lesson, or classroom.

Educator judgment should be central, but teachers should not be left alone to evaluate every app, platform, privacy policy, accessibility claim, AI feature, and instructional promise. That is not sustainable, and it is not fair.

Schools need trusted public-facing reviews, privacy ratings, accessibility information, safety indicators, AI risk assessments, and quality signals that help educators make better decisions before tools reach students. Common Sense Media, ISTE+ASCD, Digital Promise, and similar organizations have all contributed pieces of this infrastructure, but the work is fragmented, unstable, and unevenly known.

Teacher judgment matters most when it is supported by reliable information and respected in practice. The answer is not to tell every educator to vet everything alone, but to build stronger, transparent systems that help educators understand risk, quality, accessibility, privacy, and instructional value, while still trusting teachers to make the final instructional call for the students in front of them.

Point 7. Establish a gold standard for AI safety and privacy

Schools should demand strong safety and privacy protections from AI providers. Companies that cannot meet those standards should not serve K-12 education.

Student data should not be the price of participation. Schools should know what data is collected, how it is used, who has access to it, how long it is retained, whether it is used to train models, and what happens when a tool is discontinued or sold.

This is where clear standards matter. Schools need transparent contracts, strong data rules, parent communication, accessibility review, bias testing, security review, educator training, and ongoing monitoring. They also need the will to say no to tools that do not meet the standard.

Safety and privacy standards should not become a back door to blanket avoidance. The right response to risk is governance. If AI is going to affect the future of work, schools need to teach students how to use it safely, ethically, and critically. That starts with adults making informed decisions before tools are placed in front of students.

Point 8. Create an independent research consortium

There needs to be independent research on AI, screens, and educational technology, research that is not funded by the industries selling the products. Schools have been flooded with products that make big promises and provide thin evidence. Too often, districts are left to figure out impact on their own while vendors move on to the next sale.

Some useful work already exists through organizations such as Common Sense Media, ISTE+ASCD, Digital Promise, and the EdTech Evidence Exchange. Again, the issue is that this work is scattered, unevenly used, and not always connected to the decisions schools make every day.

The research questions also need to improve. “Are screens good or bad?” is too shallow. We should be asking which kinds of screen use support learning, which kinds undermine learning, for which students, at what ages, under what conditions, with what teacher support, and for what goals.

Passive consumption and active creation should not be treated as the same thing. A student scrolling through entertainment content is not doing the same work as one using a device to interview a community member, analyze data, produce a podcast, translate materials for a family audience, or document a science investigation. That is why the goal should not simply be more research. Educators do not need another disconnected report that treats all screen use the same. They need trusted evidence they can actually use to make better decisions about tools, teaching, and learning.

Point 9. Adequately fund public education

Public schools need adequate funding. That includes staffing, facilities, professional learning, mental health supports, libraries, arts, athletics, career pathways, accessibility tools, modern infrastructure, and technology that works.

It also includes the basics too many people skip over: reliable WiFi, adequate bandwidth, devices that function, enough technical support to keep systems running, and clear guidance on how to use educational technology well. It is not enough to hand schools devices and platforms and then blame teachers when the infrastructure, support, or instructional guidance is not there.

Too often, schools are told to prepare students for the future while being funded for the past. That is especially damaging for CTE. Students need access to current tools, not outdated simulations of work. They also need educators who have the training, support, and time to use those tools in ways that strengthen learning rather than digitize old assignments.

The AFT is right to warn against AI and vouchers becoming another excuse to defund public education. But this is also why the “devices down” frame is risky. Wealthier families will still provide access, tutoring, devices, AI tools, enrichment, internships, and networks outside of school. If public schools pull back from teaching students how to use modern tools well, students with less access outside school lose the most.

That is not equity. That is retreat.

Point 10. Tax Big Tech

The AFT calls for a tech tax on Big Tech earnings and some business operations to address harms and disruptions caused by technology and AI.

A real argument exists for that. Technology companies have profited enormously from public attention, public data, public infrastructure, and public institutions. If their products create costs for schools, workers, communities, and the environment, they should not get to externalize those costs while public systems clean up the mess.

But a tech tax should not become a substitute for better education policy. If money comes from Big Tech, it should support public-interest goals: independent research, educator professional learning, privacy enforcement, accessibility, digital citizenship, AI literacy, CTE infrastructure, and public accountability.

It should not become another funding stream that sends public dollars back to the same companies with minimal oversight.

The better frame is purpose first

The AFT is right about a lot. More hands-on learning. Stronger privacy protections. More scrutiny of Big Tech. Better protection for children from manipulative platforms and simulated relationships. A renewed insistence that education is human work.

But “devices down” is the wrong goal. It makes the device the problem when the deeper issue is often learning design, adult guidance, infrastructure, privacy, safety, and support.

Devices can distract students. Notifications distract. Poorly designed platforms distract. So do boring lessons, unclear tasks, weak relationships, irrelevant assignments, and classrooms where students are asked to comply rather than think.

Removing the device may solve an immediate classroom management problem, but it does not prepare students to manage attention when the device comes back. And it will come back. In college, at work, in civic life, in relationships, and in nearly every modern career, students will need to focus, create, communicate, and make good decisions in technology-rich environments.

Students need explicit practice with digital well-being, attention management, purposeful tool use, and knowing when technology helps and when it gets in the way. They need to learn when to close the screen, when to use it, when to silence notifications, when to collaborate face-to-face, when to use AI, when to question it, and when to do the thinking without it.

Sometimes devices should be down. Students should discuss ideas face-to-face, build with their hands, play outside, conduct experiments, write privately, make art, and listen to one another without a screen in sight.

Other times, devices should be up. Students should use them to research, create, collaborate, publish, translate, document, code, communicate, design, get feedback, and solve problems.

The goal is not more technology or less technology, but better learning, healthier habits, and students who know when and how to use the appropriate tools to think, create, communicate, collaborate, and solve problems.

University of Central Florida associate professor Humberto López Castillo was recently looking at a student project when he saw that it cited a paper by Castillo himself. There was, however, a problem with this citation: López Castillo couldn’t remember ever writing this paper or conducting the research it referenced.

“It was a hallucination,” López Castillo says.

The student had clearly used AI, and López Castillo shares the story as an example of how not to use AI in the classroom. Still, he has plenty of counterexamples.

López Castillo, a trained pediatrician and associate professor in the Department of Health Sciences, has also seen students use AI in creative ways to promote public health understanding, and as a research tool. For one project, López Castillo asks students to explain health concepts from class to non-experts, and since he started encouraging students to use AI, he’s seen the projects get better. Students have created health-themed board games and Hamilton-style rap songs. Others have designed AI to aid in health research in ways that wouldn’t be possible without the technology.

This compassionate and student-centered approach to AI use is part of why López Castillo was named Superhuman (formerly Grammarly’s) 2026 Educator of the Year. The student who nominated López Castillo for the award, Vardhan Avaradi, is a bioscience major, who with López Castillo’s mentorship, is using AI to try to comb through massive data sets with the goal of predicting people who are at risk for cardiovascular disease.

López Castillo shares some advice for other educators on how they can use AI in their classes.

Teaching With AI Effectively: Set Expectations

The first step to positive AI use in a classroom is making it clear to students when they can and can’t use the tool, López Castillo says.

“Perhaps my most important advice is to set expectations up front around what is allowed, what is not allowed, what you are expecting to see, and what is a big no-no for your classroom,” he says.

He adds that at the University of Central Florida, “We have several policies on the menu that we could use. We can say, for example, ‘AI is absolutely not allowed in the classroom,’ all the way to ‘AI is absolutely encouraged in this classroom,’ as long as you specify there's a critical thinking component.”

López Castillo leans toward the permissive approach but makes sure that student AI use is human-driven.

Model Proper Use

Part of encouraging proper AI use is modeling the types of uses you want to see.

For example, for a summer course López Castillo is teaching in Barcelona, he is asking students to share regular blog posts. He tells students, “You may generate some content through AI, but you still have to put in your human element.” He models exactly what he means in his introduction post in which he shares photos, talks about his hobbies, and makes it clear the post was written by a human.

“Without even giving them guidelines on what their introduction post should look like, they just take that as their model, and they go with it,” he says. They can use AI to help generate their posts, “but of course they have to tweak it, and they have to provide their own input on who they are.”

Learn From Students

López Castillo is not afraid to admit that when it comes to using AI in the classroom and health science more broadly, he is frequently as much a student as those he’s teaching.

“I learn a lot from students,” he says. “Teaching and learning is a two-way street, so they are teaching me new tricks, I'm teaching them new tricks on how we use AI, and it has been very collaborative.”

His students, for instance, encouraged him to use AI to code, which, with proper oversight, has been really helpful, López Castillo says. They’ve also used the tool in unexpected ways, from the aforementioned projects looking at cardiovascular health to a virtual escape room in which participants had to correctly diagnose a patient with diabetes in order to escape.

Embrace Teachable Moments

López Castillo has long allowed open-book tests because that is what the clinical world is like.

“I don't think real life operates like, 'We're gonna lock you down in a room without resources, and you have to produce everything from your brain,'” he says. “I'd rather make sure that you have a fair balance of the tools that are available to you.”

He feels the same way about AI, but that doesn’t mean he doesn’t have to work with students to ensure they are using AI correctly. For instance, when that student cited a paper by López Castillo that didn’t exist, he met with the student.

“We sat down, and it was an interesting moment, where I was like, 'Tell me more about this paper by me that you incorporated in your reference list,'” López Castillo says. The student immediately got nervous because he couldn’t provide any details since the paper wasn’t real. When the student admitted to not checking his sources, López Castillo reminded him that even with AI, humans are still in the driver’s seat.

“You are the one who's responsible for that writing,” López Castillo tells his students. “Your name is the only name that's going to be among the published authors, so you are the one who needs to verify those sources.”

He adds that rather than being a drawback, allowing students to make these types of mistakes with AI use in the college setting has value.

“It is a teaching opportunity," López Castillo says. "This is the moment to make those mistakes."

Vibe coding might sound like a hipster on a surfboard with a MacBook, but it’s actually a simple way to describe creating computer code using artificial intelligence.

Instead of writing every line of code yourself, you describe what you want created and the AI generates the code for you. The “vibe” part comes from the idea that you may not actually know exactly how the code is working. You are guiding the AI by feel, asking for changes, testing the result, and moving toward something that works.

While vibe coding can be looked down upon by some in the development community, its popularity is growing fast, as are its capabilities.

When people, including myself, first started vibe coding, they would ask a large language model (LLM), such as ChatGPT, Claude, or Google Gemini, to create the code for a specific app or tool, then copy and paste that code into a code editor and run it manually.

When an error appeared, you would copy that error back into the LLM and it would, hopefully, provide a fix that you would then dutifully copy and paste back. You still needed to be relatively technical, even if you were not a developer, to get all these systems set up and working. It wasn’t elegant, but it could help create simple tools, fix website issues, and build early prototypes.

Things have moved on a lot over the past few years, though. Vibe coding is now much more powerful and can complete more complex tasks and create feature-filled applications. It has also become much more accessible, with bespoke tools now doing much of the heavy lifting and setup work required to get an app or website working.

Lovable, for example, uses a variety of LLMs to create a one-stop vibe coding shop using credits as AI currency. It allows people with very little, or even no, knowledge of how to develop apps to create something quickly just by describing it. Of course, as with everything AI, understanding how to prompt well makes all the difference.

I like using Claude with VS Code when I vibe code, as I know a bit of the programming language Python and want to be able to see what’s happening to the code and do some manual troubleshooting. It’s also relatively cheap.

To be brutally honest, though, AI is far more capable than I am at writing code, and I quickly have to defer to it if and when things don’t go as expected.

The difference between the two approaches is significant. With a tool such as Lovable, the “guts” are hidden and dealt with automatically, whereas with more traditional vibe coding tools you will need to set up your own environments or run programs directly on your computer to get something you can use.

What are the risks of vibe coding?

This leads into one of the biggest risks of vibe coding: you don’t fully hold the reins.

An LLM can misunderstand what you want. It can also make mistakes, confidently create code that does not work, or create fragile systems that look good but are hard to change or maintain. As complexity increases on a project, you might find yourself losing control of it. This can be particularly problematic when the AI ties itself up in knots trying to understand what you want or fix something it does not know how to fix.

This is where choosing the right AI tool can help a lot. In my experience, some AI coding tools can get stuck and claim to have fixed an issue when clearly it has not, or even worse, created a new one somewhere else.

As with all LLMs, you need to be able to decipher the language when it tells you something such as, “Oh sorry, I see what I need to do now, let me fix that,” so you know when it is genuinely correcting the problem and when it is just trying to be helpful but is actually more stuck than Winnie the Pooh in a jar of honey. Best-case scenario this can lead to wasted time or credits being used up without the issue being resolved.

It’s good to be aware of the recent horror stories, such as AI coding tools deleting databases or removing whole features from a product. It’s also worth being careful with the idea that vibe coding means anyone can now create an app. That is true up to a point, particularly for simple prototypes, experiments, and personal tools. But creating a safe, reliable app that other people depend on is a different thing altogether.

You might be able to describe what you want in plain English and get the AI to generate the code, but that does not remove the need to check, test, and understand what has been created. The easier AI makes it to build something, the easier it also becomes to build something fragile without realizing it.

This is why relying solely on a vibe-coded program that is integral to your work can be risky. But there are areas where vibe coding is, frankly, a bit magical.

What are the benefits of vibe coding?

Vibe coding feels like a genuine shift because it lets people who are not developers start making software, in the same way the early web let people publish without owning a printing press.

One of its most powerful benefits is that it allows someone with an idea to create a working proof of concept quickly. That does not mean the end result is ready to be used by hundreds or thousands of people, but it can be enough to show what is possible.

Creating a visual and working prototype can also help guide a development team and stakeholders. Instead of simply describing what you want, you can show an early version of the tool or app and use that to shape the discussion.

Vibe coding can also help you learn by doing. You can ask the AI questions and ask it to explain what it is doing and why. Over time, this can start to give you a better understanding of how the code works.

One of the most powerful uses of vibe coding, in my opinion, is creating small tools or programs that are very specific to your needs. Whether that’s a simple game you want to create for your students, a classroom quiz, a revision tool, or an app that helps keep parents up to speed on school changes, vibe coding can help you get there more quickly and at far less cost than using a development team or, in some cases, paying for a third-party provider.

Vibe coding can also be useful for experienced developers. They might not use it in quite the same way as a beginner, but it can still help them move faster. Sometimes the AI is useful for getting the first rough version of something working, even if the developer then has to go back through it, tidy it up, and make sure it is actually safe and sensible.

The important point is that vibe coding is strongest when it is used for experimentation, learning, and prototyping. It is less suitable when the software needs to be secure, reliable, long-lasting, or used by lots of people without proper review.

How to explain vibe coding to students in class

The best way to explain vibe coding to students is to say that it is giving instructions to an AI and seeing whether the AI can turn those instructions into a working program.

The student is not writing every line of code in the traditional way. They are describing what they want the software to do, then testing whether the AI has understood them correctly. That’s the key difference.

In normal coding lessons, students often start by learning syntax: the rules of the programming language. They learn how to write a variable, a loop, a function, or a condition, and then use those building blocks to make something.

With vibe coding, the starting point is often the idea instead. The student might begin with, “I want a quiz about volcanoes,” or “I want a game that helps me practice spelling.” The AI then creates the first version of the code.

That does not mean the student has finished. In many ways, that is where the learning starts. The student has to work out whether the AI understood the task. Does the program do what they expected? Is the information correct? Does the scoring work? What happens when a user makes a mistake? Can the student explain why the code behaves the way it does?

This is the part students need to understand. Vibe coding can feel instant, but it is not simply pressing a button and getting a finished app. It is a process of describing, testing, questioning, fixing, and improving.

If districts want to strengthen trust, improve engagement, and protect their reputation, communication strategy can’t start in the central office — it has to start in the classroom.

In district communications, it’s tempting to focus on what the central office sends: board updates, emergency alerts, strategic initiatives, press releases, policy changes. These messages matter. They shape public perception and provide essential information to families and staff.

But ask most parents how they actually experience their school district, and the answer is far simpler: through their child’s teacher.

For many families, their perception of a district begins and ends with classroom communication. They may never speak with the superintendent or interact with district administrators. Trust is built — or eroded — through the daily interactions between teachers and families.

Start With the Base Layer

Communications leaders across the country consistently describe the same challenge: keeping a pulse on what’s happening across schools. Major events are easy to track, such as athletic championships, large theatre or concert performances, board recognitions, and grant awards. But the daily classroom wins that quietly build trust are harder to surface–and those small moments matter!

When teachers regularly share classroom successes such as a creative writing project, a science experiment, a milestone achievement, or even a joyful themed dress-up day, they create consistency, demonstrate transparency, and reinforce partnership. Over time, those seemingly small updates build a reservoir of trust that becomes critical when more serious or complex conversations arise.

From a communications perspective, these moments also provide the most authentic storytelling opportunities. These reflect culture in action and humanize the district in ways that top-down announcements cannot.

Seeing What Families See

One of the most promising developments in district communication tools has been the ability to virtually “walk the halls.” With a tool such as ClassDojo for Districts, I have a real-time view of classroom activity across our schools—without requiring additional reports, extra submissions, or new workflows for teachers.

I can see all those little moments as they unfold. I’ve watched a student-written novel project take shape chapter by chapter. I’ve seen transitional kindergarteners celebrate the 101st day of school dressed as Dalmatians. I’ve spotted creative science experiments, collaborative art displays, and the everyday interactions that define the heart of a school community.

Instead of waiting for a principal to forward a story idea, I can see classroom learning and engagement in real time. Instead of missing a joyful milestone quietly happening in one grade level, I can elevate it while enthusiasm is high. Instead of relying solely on formal submissions, I gain organic insight into the daily experiences shaping our district’s culture.

For a geographically large district like ours, that virtual presence is especially powerful. I can stay connected to multiple campuses without physically traveling between them. The result is more timely storytelling, stronger relationships with our school sites, and a greater sense of cohesion across the district—because we’re not just sharing the big wins. We’re celebrating the small moments that truly matter.

Balancing the Narrative

Another overlooked benefit of classroom-level visibility is equity in storytelling.

High schools naturally generate attention through athletics, performances, and large-scale events. Elementary classrooms often have fewer headline-making moments, even though extraordinary learning happens there every day. Without intentional effort, district communications can unintentionally skew toward the most visible campuses and programs.

A districtwide classroom feed allows communications leaders to intentionally balance representation, across grade levels, across schools, and across student groups. It ensures that the joy of a transitional kindergarten celebration receives as much attention as a varsity championship.

From Duplication to Alignment

Many districts face communication fatigue, not because they communicate too much, but because they communicate across too many disconnected platforms–teachers use one tool, the district uses another, and emergency alerts are on yet another. Freestanding websites and social media just add more layers. Families are asked to download multiple apps and monitor multiple streams of information.

When district announcements are aligned with platforms families already check daily, engagement improves naturally. Leaders report stronger read rates and more immediate responses when communications build on existing habits rather than trying to create new ones.

Trust in a Complex Media Landscape

Today’s communications environment is increasingly complex. Social media policies continue to evolve, privacy expectations are heightened, and misinformation can spread quickly in public forums.

Closed-loop communication environments offer a compelling alternative for everyday updates. Families experience a familiar, feed-based interface, but one that is limited to their school community. Teachers benefit from appropriate oversight and documented communication histories. District leaders gain visibility without intrusion.

The goal becomes smarter communication, rooted in relationships that already exist.

Bottom-Up Strategy Is Sustainable Strategy

For communications leaders rethinking their approach, the most important shift may be philosophical. Instead of beginning with the question, “What should the district office send this week?” consider asking, “How are families already receiving communication every day?

”Strengthen that foundation first: support teacher-family communication; increase visibility into classroom moments; and use those authentic stories to inform broader district messaging. Then layer strategic announcements on top of that established trust.

When communication starts in the classroom and scales outward, it feels cohesive rather than imposed and relational rather than transactional. And in K–12 education, in which trust shapes everything from engagement to attendance to community support, that distinction matters.

District communication is most powerful when it reflects what families already see and experience daily. Sometimes, the most strategic move a central office can make is simply to walk the halls.

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.

Since I was young, one of my favorite things to do was read. So naturally, as I got older, I more or less stopped doing it. Part of the problem was that I was waiting for the perfect time and place to read. I wanted a quiet spot with a comfortable chair and enough time to read a chapter or so.

However now, as a father of two young kids juggling multiple teaching and writing experiences, these types of perfect reading moments don’t really exist for me—I'm sure other educators can relate.

Over the years, I’ve had to abandon this idealized reading mindset and adopt a more I-thrive/read-in-chaos mindset. Technology has helped with this, and the combination of using ebooks and audiobooks has allowed me to average about three to four books per month.

I’m certainly no super reader, but as the summer approaches and we all look to get in extra reading, here are some ways in which technology has helped me keep up with my reading.

1. Reading Ebooks

I truly prefer to read a real paper book over an ebook as there’s just something about the way the paper crinkles as you turn the page that can’t be duplicated in a Kindle or other digital reader.

Even so, I read much more when I read ebooks. Unsurprisingly, this is all about convenience. Real books require two hands to hold and proper lighting to see. They also have a habit of getting lost or left at home when you need them most.

Ebooks are always with you. I’ve tried dedicated ebook readers, such as a Kindle, but the best bet for me is just to use a reading app on my phone. This way it's always with me, and when I’m tempted to go online and start scrolling through social media, I remind myself to read for five minutes instead. Often this five minutes turn into 10 or 15 minutes, and little by little, I work my way through more books.

2. Listening to Audio Books

I love going to live author events and listening to them read from their work, but I will admit that during a live reading, my mind often wanders and I can’t follow the plot details. This used to happen to me with audiobooks as well.

To stay engaged, I eventually started adjusting the playback speed. I start at 1.25 speed and then go up from there based on the speed of the narrator. When I’m really dialed into a story, I’ll often listen at 1.7 speed, which approaches the speed at which I actually read.

I also find that audiobooks lend themselves particularly well to nonfiction, first-person narratives, and/or stories with fewer characters. Sprawling multicharacter epics still tend to confuse me a bit when listening. But overall, when we listen to books, we retain about as much information as when we read them, research shows, and that's what I've found.

3. Reading Audio and Ebooks Together

Some worry that when we listen to audiobooks instead of reading, it encourages us to stop reading altogether. This isn’t the case, according to most reading experts. In fact, some have told me that listening to books supports reading books.

I’ve certainly found this to be the case personally, and I've discovered a great strategy to increase my reading is to listen to and read the same book.

Amazon offers Whispersync, a feature that allows you to seamlessly toggle back and forth between your audiobook and Kindle without losing your place. Even if you prefer to use a more independent bookstore-friendly platform such as Libro.FM, I find that with a little effort, it’s not too hard to go back and forth between the two and keep moving forward through the narrative. If I’m listening to an audiobook and want to find my spot in the ebook I’m reading, I just search a particular phrase.

One of the biggest hurdles to me finishing a book is momentum. If I spend too long reading any book, I tend to get tired of it. Listening and reading the same book really fights this and helps me get to 50 pages or so of progress without too much effort.

4. Chunking

As teachers, we are often encouraging our students to make small manageable goals and then start completing those. I believe the same sort of principle is behind what makes social media so addictive. It comes in bite-sized servings. We don’t have to commit to reading any particular text or watching any video, it doesn’t matter if we get interrupted.

I’ve tried to apply this to my reading habits and be less concerned about interruptions. If I have a few minutes to read, I’ll take that opportunity rather than scroll on social media. Audiobooks and ebooks lend themselves well to this technique because chapter and section breaks are less prominent as you read or listen, but the same idea holds true for traditional books as well.

5. Scrolling Past

Another lesson from social media I try to apply to reading is the idea of scrolling past something in which I'm not interested. On social media, the scroll never ends, and if what the algorithm is showing me isn’t interesting, there’s always plenty more to read. The same is, of course, true of regular reading. And those who study literacy say the idea that you must finish every book you start can halt reading progress.

Personally, I’ve started abandoning ship or scrolling past if I find my reading focus waning while reading a book. What I try to do is revisit the book after reading two or three other books, just to see if it was a matter of my being in the wrong mindset. This is occasionally the case, but more often than not, if I don’t finish a book, it’s because it is just not for me. Unless it is something I need to read for research, once I’ve given a book a second try, I move on for good.

A part of me still hates not finishing a book, but I’ve learned to silence that part of me and figuratively scroll past the book I don’t like. This is one of the most effective ways I've found for continuing to read overall.

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.

To address this shift, Tech & Learning is hosting an essential three-part webinar series designed to move beyond the hype. This series provides a roadmap for building a resilient, AI-augmented academic community, focusing on practical implementation, workforce alignment, and rigorous security.

In this series, attendees will get a sneak peek at some of the newest AI tools designed to improve teaching and learning, and get students prepared for the futures they choose.

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Phase 1: The Cutting Edge

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Phase 2: Future-Proofing Students

Date: June 3, 2026

Focus: Solutions That Support Career Readiness

As the global economy shifts toward automation and algorithmic intelligence, the definition of "career ready" is evolving. This session addresses the "how"—specifically, how educators can bridge the widening gap between traditional classroom settings and the modern, AI-integrated workforce.

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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.

Either watch above or listen/download below.

For school districts weighing whether to invest in esports, the conversation often stalls at cost. But according to Jason Bond, who has spent more than 20 years focused on K-12 AV at Extron, the real question administrators should be asking is simpler: what else can that space do?

"It's dual purpose, and that's the key to the return on that investment," Bond explained to me—in person!—at an Extron open house. "You're going to use that esports space for something like graphics design or cybersecurity training during the day, and then in the evening or after school hours, it becomes the esports playing facility."

Bond, who has tracked K-12 AV trends since the early days of classroom projectors, sees esports as the latest inflection point in how schools think about technology spaces. Where AV was once primarily about equipping individual classrooms, districts are now investing in large gathering spaces, media centers, and purpose-built esports facilities — all of which require more sophisticated infrastructure including AV over IP, multi-graphic processing, and specialized audio routing.

The engagement returns, Bond argues, are measurable. Esports programs have been linked to improved attendance, better grades, and stronger community involvement. "It keeps kids in school. It keeps kids getting better grades," he says. "And it's not just a bunch of kids sitting around playing video games — there's a lot of team coordination that promotes communication and engagement."

For districts just starting, Bond's advice is to resist the urge to go big immediately. A computer lab retrofit using existing data and power infrastructure can serve as a functional first step. From there, districts can grow incrementally, layering on technology as budgets allow. AV over IP, he notes, is the foundation worth investing in early — it provides the flexibility to scale in directions a district may not yet have anticipated.

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.

As education budgets tighten and federal relief funding expires, arts programs across the country once again find themselves on the chopping block. Recent federal and state budget proposals reflect a familiar pattern: when policymakers have to make difficult choices, creative subjects are often viewed as supplemental rather than essential. All the while, educators and district leaders continue making the case that the arts support academic achievement, engagement, and whole-child development.

But at a moment defined by rapid AI adoption and ongoing debates about screen time, the argument for protecting and investing in arts education needs to take on a new tone. The arts continue to be one of the most effective places in school for students to build healthier, more intentional relationships with technology.

In short, in the age of AI, we need the arts more than ever.

What Sets Arts Education Apart

While many conversations about classroom technology focus on access, guardrails, or efficiency, arts classrooms offer something different: a model for how digital tools can serve creativity, deepen thinking, and expand opportunity without replacing human judgment. In an era when AI can generate essays, compose music, and create images in seconds, students need structured environments in which technology is used responsibly and reflectively. The arts already provide that structure.

As a district arts coordinator, I have seen this firsthand. A band student of mine who had been uninterested in traditional instruments completely changed when we introduced digital music tools, becoming not only one of the most engaged learners in the class, but also a composer of original music. The introduction of digital tools didn’t replace musicianship — it revealed it. Technology gave this student an accessible entry point into the creative process, and once they had it, their confidence and artistry blossomed.

Digital composition software, notation tools, and recording platforms allow students to experiment, revise, and refine their ideas in ways that would have been far more time-consuming a decade ago. Students can layer tracks, hear immediate playback, annotate their own scores, and collaborate across devices. The same is true in other contexts besides music; in visual arts, for instance, a variety of digital drawing and painting platforms enable students to practice with new mediums, styles, and techniques without having to worry about supplies or messes. But in either case, the core intellectual work of looking and listening critically, understanding structure, and making aesthetic choices remains entirely human and part of the learning.

At every step, technology is embedded within a framework of authorship. Even – and especially – with the help of new tech-enabled tools, students are creators, not just consumers or editors of machine-generated content.

Practically speaking, teachers should encourage students to experiment with tech-enabled arts and music tools. For example, how can each student use the same set of sounds, samples, or colors to create a piece that uses their own unique voice? How can technology be an accelerant for creativity, instead of putting a damper on it?

Tech Also Frees Up Time

Technology in the arts also plays a less visible but equally important role: reducing logistical burdens so teachers can focus on higher-order instruction. Platforms such as MusicFirst streamline assignment distribution, submission, assessment, and feedback with a particular focus on the arts. Instead of collecting paper scores, managing physical recordings, or juggling disparate tools, teachers can handle routine tasks—organizing files, timestamping submissions, tracking revisions—which frees us to spend more time on musical interpretation, technique, and creative problem-solving.

Creating such efficiency also allows for more one-on-one conferencing, more targeted feedback, and more opportunities to guide students through complex artistic decisions. Rather than troubleshooting formatting issues or managing classroom logistics, teachers can ask deeper questions: Why did you choose this harmony? How does this dynamic shift change the emotional arc? What alternatives did you consider?

Cycles of Experimentation

Perhaps most importantly, arts technology creates safe, structured spaces for iteration and failure. In arts software, students can revise endlessly without wasting materials or losing prior drafts — whether that’s tempo and instrumentation in music or color and line in visual art. The experience of creating art enables students to manage pace, embrace iteration, and develop delayed gratification — skills that are increasingly rare in digital environments built around instant results.

The creative process will always require patience and sustained attention. Arts instruction gives students a structured, supportive space to slow down, revisit their thinking, and understand that excellence emerges from cycles of experimentation rather than immediate perfection. That’s an idea that teachers can encourage by dedicating time for students to review their own (or their peers’) work, or to make changes to tempo, color, or tone just to see what happens. They may very well like the result.

Arts Education: Tech At Its Best?

As education leaders navigate AI policies, digital citizenship frameworks, and technology procurement decisions, they should look to arts classrooms as proof of concept. The arts demonstrate what technology integration at its best can look like when it is grounded in human intention, structured creativity, and professional educator guidance.

If we want students to use technology with intention, creativity, and integrity, we must protect the classrooms that already teach all these how. The future of digital citizenship may depend on keeping the arts in our schools.

In traditional American education, there has long been a "middle school mandate" for workforce readiness. The prevailing logic suggests that once students reach the cusp of adolescence, we should begin equipping them with the skills necessary for the professional world.

Waiting until middle school to focus on these abilities is a mistake, says Ellen Galinsky, president of Families and Work Institute and author of The Breakthrough Years and Mind in the Making.

Galinsky’s extensive research in this area has proven that to prepare students for an unknown, rapidly evolving workforce, parents and educators need to shift their focus to include the early development of executive functioning skills—the neurocognitive processes that allow us to manage ourselves and our resources to achieve goals.

Developing these skills in early childhood isn't just about better classroom behavior, but builds the foundation for lifelong career success.

What is “Executive Functioning”?

Executive function is a suite of four core cognitive processes or what she called the New Basics:

• Working Memory: The ability to hold information in mind and mentally work with it.
• Cognitive Flexibility: The ability to switch perspectives or focus and adjust to changed demands or priorities.
• Inhibitory Control (Self-Control): The ability to resist impulses and stay focused on a task despite distractions.
• Reflection: This capacity involves pausing, stepping back and considering alternatives before acting.

"These are attention-regulation skills," Galinsky explains. "What we pay attention to, what we focus on, is what we learn. They are the skills that underlie all intentional learning and well-being. These are the skills that underlie learning to read and to do math as well as the skills that you use to handle challenges when something gets tough."

The Early Start Advantage

The push to start executive functioning development in preschool and early elementary years is rooted in biology, Galinsky says. This is a sensitive period during which the neural connections for executive functions are being wired.

“If you really want children to have these skills, you have to start in the early years," she says. "Two, three, four, five… these are the ages of huge brain plasticity when the connections among various parts of the brain are being formed and the skills learned can be more lasting."

By the time a student reaches middle school, their cognitive habits are becoming more ingrained. While Galinsky emphasizes adolescence is another sensitive period for strengthening these skills and that it is “never too late” to learn them, building the foundation early ensures that when students face the high-pressure environments of modern careers, they aren't struggling to build the engine while already driving the car.

Translating Classroom Skills to the Boardroom

Galinsky notes that in the modern workforce, technical knowledge has a shorter shelf life than ever. The ability to learn, unlearn, and relearn is the ultimate competitive advantage.

“I know from years of working with companies and surveys are increasingly showing that employers look for kids who can communicate, solve problems, and work in teams much more than they look at grade point averages,” Galinsky says. Executive functioning skills are the cognitive building blocks for the real skills that power high-level professional competencies. Some examples:

• Perspective Taking: Built on cognitive flexibility, this allows a salesperson to understand a client's needs or a manager to resolve a conflict.
• Critical Thinking: Built on working memory and reflection, this allows an analyst to see patterns in data rather than just repeating facts.
• Collaboration: Built on inhibitory control, this allows a team member to listen to others' ideas instead of just “blurting out the first thing in their mind.”

The Power of Reflection: “The Active Ingredient”

One of the most practical takeaways from Galinsky’s research is the role of reflection. In a rapid-paced society, we often prioritize checking boxes over meaningful integration. Galinsky sees reflection as the “active ingredient that makes all of the skills work together.”

For a math teacher, this might mean moving away from “right vs. wrong” and asking a student, “How did you remember that formula?” or “Why did you make that error and what can you learn from this mistake?”

This metacognition—thinking about one's own thinking—is what separates a worker who follows instructions from a worker who innovates.

Real Skills for Real Life

The AASA (The School Superintendents Association) has recognized the importance of focusing on executive functioning by making “Real Skills for Real Life” one of its five Public Education Promise pillars for transforming public education. Galinsky is currently working with the organization to launch a curriculum for educators that spans from birth through age 18, ensuring that these neurocognitive muscles are exercised throughout a student's entire journey.

“Executive functioning is the most important asset we have,” Galinsky says. "It has to be reinforced throughout our lifetimes. Nothing is a vaccine forever, but starting in the early years will make a significant difference to kids’ academic success and to their positive mental health”.

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

Watch above or listen/download below.

There are few things more chaotic in the life of a school administrator than tracking who paid for the field trip. Cash in envelopes, multiple payment portals, different logins for every system — and somewhere in the middle of it all, a kid who shouldn't be left behind because her family doesn't have a bank account.

ClassDojo is betting it can fix all of that. ClassDojo Payments is promising to enable schools to collect field trip fees, manage fundraisers, process technology fees, and accept payments from every family, all on the platform that tens of millions of school leaders, teachers, and parents already use already. Powered by Pay Theory’s payment facilitation infrastructure, the technology aims to eliminate the friction of offline payment collections and disconnected third-party portals while ensuring every family can participate—including through cash-to-digital acceptance at thousands of retail locations nationwide.

It’s a big leap, but Chad Stevens, Head of Growth & Partnerships at ClassDojo for Districts, sounds confident. I had the pleasure of chatting with him about their progress.

The vision, according to Chad, is simple: a teacher sends a field trip message through ClassDojo, there's a payment button right in the post, a parent taps it, uses Apple Pay or face ID, and it's done. No envelope, no separate portal, no morning scramble to figure out who still owes money.

But the feature Chad is most excited about is less flashy and arguably more important. An estimated one in six U.S. households are unbanked — families that operate entirely in cash and can't use digital payment systems. Through the PayTheory partnership, ClassDojo plans to let those families pay for school activities by scanning a barcode at CVS, Walgreens, 7-Eleven, or Family Dollar, with the payment routing digitally back to the school district.

"Just because a kid doesn't have a bank doesn't mean they struggle to get to go on a field trip," Chad says. "To me as a former teacher, that was just one of the really exciting things about what we plan to build."

A full launch is targeted for late 2026, with the company signaling it wants to get the back-end accounting and compliance architecture right before flipping the switch.

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.

NCCE is the Pacific Northwest’s largest edtech event, now in its 34th year. Even if NCCE hasn’t been on your radar, it should be: the opening session inspires, the exhibition hall buzzes with innovation, and attendees are set for an ed-tech adventure from the start.

The vendor hall featured 80 vendors, including partners from the Pacific Northwest such as Recycle Boise, the Computer Science Teachers Association, and a K-12 school. It was an opportunity for attendees to meet vendors face-to-face and see what was new and exciting. The conference provided dedicated time during an “Exhibitor Blitz,” ensuring attendees did not miss any sessions.

Having attended many conferences, I noticed some special things in the vendor hall. Here are my top 10 vendor Hall finds:

NCCE slideshow below

1. Ozobots are educational robots designed to teach basic coding through color coding, cards, and markers. This year, they have introduced new features to enhance their instructional capabilities. Ozobot Robotics’ latest product, Ari, now includes a touchscreen interface that allows students to input and execute their own code, in addition to reading color codes, offering a meaningful upgrade from previous Ozobot models.

2. The Epilog Laser. Laser engravers have been on the market for some time, and many schools have adopted these into their maker spaces and fabrication labs. I saw something a little more special at the Epilog Laser booth. The Epilog Laser engraver was flat-out cool. It can engrave, cut, and mark at micro sizes I have never seen before, and it made me think about replacing all my laser engravers immediately. One can’t go wrong with their CO2 laser or fiber laser. Better yet, why not select their dual all-in-1 machine? I must try.

3. EduProtocols paired with Learning Genie is a solution for building instructional units. Together, these tools help teachers design detailed lesson plans that align with standards, high-quality instructional materials, and specific locations, with AI elements supporting the process.

4. MagicSchool.AI is an AI-driven educational platform widely used in classrooms for its versatile tools and award-winning performance. At NCCE, the team showcased the platform’s adaptability through demonstrations and user stories. They also highlighted its Pioneer program at NCCE, where users shared ways they customized MagicSchool's tools to fit their classroom needs, demonstrating how the community helps maximize the platform’s effectiveness.

5. LightSpeed Technologies, which is a leader in educational audio enhancement, was at NCCE in a big way. They are sponsors at many conferences to assist in audio enhancements during presentations. What got them placed on my list was their Topcat Cascadia. It is an audio system that mounts the speaker in the ceiling and has a wearable microphone for the teacher. This microphone can connect to devices for classroom use, such as interactive displays and assistive listening devices, as well as emergency communication devices for active alerting, priority paging, and even two-way calling with location alert. This tool not only enhances audio but also provides some safety.

6. iPevo is a provider of classroom technology products, well-known for its document cameras. Over time, they have diversified their offerings to include additional educational tools. What I was most intrigued by at iPevo at NCCE was access to the audio tools. Their Vurbo.ai family had a little bit of everything. From AI-powered software that comes with each kit to a shared new personal mic for Vlogging. They have moved rapidly in this place to provide solutions for one-to-one audio recordings, to enhancements for virtual meetings.

7. Sphero devices. Sphero bots first came on the scene with their codable, waterproof balls. Sphero bots have helped students learn more about STEM and coding principles in classrooms across the country. Sphero introduced other items, such as their Indi for early learning, a car-shaped, screenless robot that moves based on the color tile used. At NCCE, Sphero launched its Blueprint engineering kits. These kits, incorporating components from the acquired LittleBits, are designed to support engineering and maker education, expanding their range for classrooms and makerspaces.

8. SchoolAI. Not to be outdone by competitors, SchoolAI had a booth featuring “Edu Stars” and held a session featuring “several nationally recognized experts and regional leaders who are shaping the future of AI.” As for the booth information, I was pleased and surprised by their browser extension. It allows for tasks such as chatting with pages, giving personalized writing feedback, and viewing a revision history. What I believe makes this tool powerful is the ability to create student spaces with SchoolAI. These spaces are teacher-monitored personalized AI environments that help engage, tutor, or reinforce the given lesson.

9. IBM. A long-standing technology leader, IBM has demonstrated its continued contributions to education by referencing its historic AI achievement with Watson and highlighting new advances. The show featured IBM SkillsBuild, a free educational platform for developing skills through project-based training and awarding credentials recognized by the industry. These skills include AI literacy and the ability to integrate AI into one's learning ecosystem and classroom. It is a must for those motivated learners of all ages.

10. Micro K12. Micro K12 is a reseller that has been helping schools and districts with their devices for years. Not only do they offer a white-glove service for device deployment post-purchase, but they also have so much more. From network security to esports solutions, Micro K12 has you covered. One of Micro K12’s edtech conferences is notable in the industry, which bring together vendors and resellers and include presentations for IT directors and teachers, as well as E-Rate support and device repair services.

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.

Watch the video above or download/listen below.

When Charles Franklin thinks about his role as a technology leader in one of Texas's largest school districts, everything starts with security. Not because it's the most exciting topic in edtech — he'd readily admit it isn't — but because it underpins everything else the district tries to accomplish.

"Security is widespread around everything that we do," Franklin says. "Even if it's not directly related, there's always some interdependency."

His district is one of only nine in Texas to earn the Trusted Learning Environment (TLE) certification from CoSN, a rigorous credential that requires districts to document and provide evidence of their cybersecurity controls — and then recertify every two years.

The process, Franklin explains, is less about impressing a review board and more about creating institutional discipline. "It's not just that we're saying we're doing something," he says. "We're showing that we're doing it and somebody is reviewing it." That accountability structure also communicates something important to the community: the district takes student data seriously.

On the data privacy side, Franklin's team maintains a dedicated staff member focused exclusively on vendor agreements — a significant investment, but one that pays dividends. Every vendor relationship is governed by a formal data privacy agreement that scopes what data can be collected, how long it's retained, and who bears liability in the event of a breach. The district also leverages the Student Data Privacy Consortium (SDPC) and One EdTech to vet products and adopt existing agreements rather than negotiating from scratch each time.

For smaller districts without those resources, Franklin points to state-level organizations such as TETL — Texas Education Technology Leaders — as a starting point. Through those networks, districts can access templates, share best practices, and connect with peers who have already done the hard work.

Looking ahead, the district is launching a Virtual Pathways program to attract and retain students in a competitive enrollment environment, and a major data lake project designed to help administrators make smarter, evidence-based decisions across curriculum, finance, and student outcomes.