A recent piece in the New York Times - There’s Really Only One Way to Stop Students From Cheating - was a gift that helped frame what I’ve been thinking about all month.

Adam Grant, an organizational psychologist who teaches at Wharton, swapped his traditional multiple choice and short answer exam for an essay question, asking his 95 undergraduates to write about how to motivate students to stop cheating.

He writes:

Their responses shocked me. Hardly any students saw cheating as a violation of integrity. A majority made excuses: We’re under a lot of stress. Everyone else is doing it. We have no choice. They sounded like major league baseball players in the steroid era. ChatGPT was their performance-enhancing drug. (emphasis mine).

Much of this tracked with what I heard in conversations with about a half dozen college students over the past month, though their responses were far more nuanced. While this is not an earth-shattering revelation by any stretch, AI is clearly "normal" technology for college students. Why wouldn't it be? It's freely available, extraordinarily powerful, and designed precisely to do the kind of work assigned in college. It would be a shock if AI use among college students had not gone up exponentially over the past several years. Most people invested in education - professors, deans, college presidents, and parents - have still not grappled with what that actually means for schools going forward. The complete normalization of AI in college has implications all the way down.

This short essay rests on several assumptions based on about a half dozen student interviews.

First, college students use AI for almost everything - academic work, personal inquiries, settling debates over meals, you name it. AI browser tabs are open on virtually every laptop, whether in the classroom or on campus more widely. Surveys and reports have shown an increase in student usage, but I suspect the rates are even higher. I simply do not believe there are a significant number of students who have not incorporated AI into their working routine in some capacity. There may still be a solid number who do not use AI to “cheat,” however they define cheating, but overall usage is utterly normalized.

Second, students are absolutely aware of what they are doing, and rationalizations abound for almost everything. Cheating may be commonplace, but the definition of what is and is not legitimate AI assistance is messy, incomplete, negotiated, and will continue to be contested going forward. No one has any idea where to draw the line, and those who claim it's easy have probably not used the tools very much themselves.1

Third, faculty have adjusted to the new environment in wildly different ways, depending on school, department, and individual faculty member, and there is no uniform overall response except perhaps the recognition that the only work that can be guaranteed authentic is done in class without technology and in front of the professor. Everything else is up for grabs.

Fourth, unless something truly dramatic happens - some kind of major "token tax" that drastically reduces free access to AI platforms, for example - everyone in education is going to have to adjust to a new normal where students can cognitively offload almost any kind of knowledge-related work for the foreseeable future. Arguing whether AI is inevitable is now mostly pointless. It's akin to arguing whether the internet was inevitable.

Online commentators, instructors, professors, administrators, parents, pundits, and consultants need to accept these as the baseline. Many adults are still squabbling over whether students should use AI at all while the students have completely moved on.

The Shame is Gone

That students have been using AI heavily since last year is not really news. What my interviews revealed, however, was the extent to which college students have folded these tools into their daily lives. Of the students I spoke with, there was a wide range of attitudes on the proper use of AI in their academic work, but not a single person disagreed with my premise that almost all students are using AI to assist them. One student shared:

There used to be this shame around using AI for a lot of things. I noticed, at least in high school, people would feel like they had to be discreet when using Claude or ChatGPT or whatever. But now it's commonplace. It's commonplace in academia and it's also commonplace just among students. People are not shy about it.

Another, the student who used AI the least, described sitting in on a large lecture:

I mean, I remember one time I went to a politics class last year and I sat in the back and I could see everybody's computers or whatever. The range of tools that they use is so great, but almost everybody is using it. I remember one kid was literally transliterating everything that the professor said and putting it into a chatbot in order to get more detail … And I've walked through the library and just seen rows of people sitting there with ChatGPT open.

Even those students who are booing AI at college graduations2 and have strong opinions regarding AI’s environmental impact are nonetheless almost forced into using AI to keep up.

This from the same student above:

But yeah, I’d say AI use is absolutely normalized. People use it for every sort of discipline, for little tasks, for huge assignments, even in class. The worst one I saw was some guy asked ChatGPT what he should ask as a question in class, and then he raised his hand and said whatever it said. I mean, really, for anything and everything. So I think yeah, it’s definitely normalized. I would say on my college campus - I think college campuses across the country would find the same, very similar situation.

My thesis has always been that when surveys ask students whether they use AI, they typically hear the question as being about "unauthorized" use rather than any use at all. The student who uses AI to help them study, but would never generate an AI essay, will answer no in good faith. Most of these surveys were also administered a year or more ago, when using AI at all still carried more stigma. It's unlikely any of those numbers capture the reality of fall 2026.

I asked every student I interviewed whether AI was completely normalized on their campus and got an unqualified "yes" every time.3

What Counts as Cheating?

That said, the gradations, qualifications, caveats, parsing, and nuance about AI use were real. The students I interviewed acknowledged using AI in some circumstances and not others, for some assignments and not others, and in some classes and not others. Most were ambivalent about the technology itself. None denied using it. Some were heavy users and unabashed about using it to circumvent actual work. Others were careful to detail what was fair game and what was off limits. All of them pointed to what they considered reasonable use cases.

Some of those uses seemed beneficial. One student, among the hardest-working I've ever taught, made the case for AI as a tutor. I pushed him first on the objection I hear often from teachers, which is that the output can't be trusted.

Here’s his response (and just for context, this is a student who is not one to suffer fools - AI or not. He’s the type who will double-check anything he finds suspicious.)

So now I think most of these models have gotten to the point where if I'm taking an undergraduate introductory [science] class, I don't really need to provide it with source material. It knows its stuff without hallucinating. And the only reason I trust that this is true is because I've just seen this. Maybe if we're talking about really advanced topics and current research in the field, that's different. But we're talking about concepts that have been established for decades now, and I have a lot of trust in it.

He isn't worried about any of this hurting his learning - quite the opposite.

I don’t think it can ever hurt your understanding. And the reason I say that is because it’s really hard to cheat in these classes. At the end of the day, people realize that they’re going to have to learn the material. And AI is better at helping people learn the material, because you can ask all sorts of goofy questions, and you can work through answers, and it’s patient, and it’ll explain things however you want, fits your learning style.

I think AI explains concepts to me in a way that sometimes professors can’t, and I can’t always email a professor anytime I have some small question. So it’s actually perfect for that.

I use it a ton. Everyone uses it a ton, and I think they should. (emphasis added).

Just to be clear, this is a student who is as serious about his education as anyone I’ve ever taught. I was honestly a little taken aback at how much faith he had in the models given his skepticism from just a few years ago.

Many students were adamant that AI cannot think or write as well as they can, so that was where they drew the line. But even these students acknowledged that proofreading a paper was fair game. So was reducing a long reading to bullet points when time was short. So, according to one, was using AI to complete an entire online problem set. This aligns with Grant's student essays.

Another student described the evolution of his own use:

And I didn’t [freshman year] use AI at all, in the sense where I didn’t even use chat to revise my sentences. And my sophomore year, I found that I was an anomaly amongst my classmates, that most people were using it for cosmetic or editorial purposes at some level. Yeah, for editorial or even idea generation, although less so, just because again, at that time the creative functions for AI were not that strong. It’s gotten a little bit better with the deep research functions. Like, I use that a lot. I use it a lot for my research memos when I have to write them - it’s great at just combing data. But if I’m writing a philosophy or literature paper, I still don’t rely heavily on its creative functions.

One student whom I knew was also hesitant to use AI nevertheless pointed to generating practice questions and review sheets as a legitimate use.

Few used agentic tools or sophisticated workflows, and almost none needed paid models to do what they were doing. Many knew classmates who had paid accounts, but the consensus was that almost everything could be done on the free tiers.

Zero Faculty Consistency - But One Universal Trend

That faculty views on AI are all over the place is not especially revelatory. Most schools have delegated AI policies not just to departments but to individual faculty, and students reported an extraordinary range of responses. The humanities were generally more hostile to AI use than STEM courses, but exceptions were everywhere. One thing was consistent, however. Almost none of these students encountered any mention of AI during freshman orientation. Only one could recall anything at all:

There's a chance it came up in orientation, because we had such a long orientation that I wouldn't be surprised if they mentioned it at some point, but it was never in a way that stuck with me or I distinctly remember. But definitely in like the syllabi of most classes at this point, there's like an AI policy to some extent, or at least a mention of AI. So probably within like my first few classes, a professor probably mentioned it.

Policies are all over the place, mostly by design. Almost every school - K-12 included - has delegated them to the teacher. There may be boilerplate language from the institution or the department, but the reality these students described is that their professors are left to their own devices.4

Despite the individual inconsistency on providing guidance for students about AI, a clear trend emerged last year, put most plainly by a student describing an economics class:

My econ professor second semester, he said explicitly, since most of our problem sets can now be solved with AI, we're no longer weighting them as much as we do. They're going to be graded for completion and they're going to be like 10% of your grade, or 5% of your grade. And most of your grade will be exams, because you can't cheat on that. And if you're shameless enough to cheat on that, AI or not, that's not something that AI can make worse per se, because all of our exams are in person and proctored by like 20 people.

Not all professors are demonstrating the same level of awareness. One student described a 10-page-take-home report, requiring research, analysis, and an essay, assigned by a professor who said nothing about AI. The student was blunt - “Nowadays, because of how normalized it is, you have to address that.” He called this a “rookie” mistake.

These interviews captured a wide range of faculty responses to AI, but many supported the overall shift to higher-stakes in-class assessment.

Blue books came roaring back. Many teachers intend to double down on this strategy again this year but I wonder - is this a long-term solution or short-term survival? Are we really prepared to sacrifice the ability to assign any significant work outside the classroom?

What is the Next Step?

Given the reality of student AI use, it’s incumbent on educators to look for ways to inspire and motivate students to develop their own organic capacities, perhaps with a nudge from us, but ultimately they will need to arrive at that conclusion themselves. Several students pointed toward a path forward.

Here is one, unprompted, in the middle of a conversation about how his classmates handle online exams:

At some level, education is a two-way street. The student has to buy into their own education. It’s an investment of some sort.

He was not fixated on integrity. He was talking about value - what was college actually for?

Again, from the student most skeptical of AI, who by that point in our conversation had already told me he no longer judges anyone for using it:

It’s [college] such a privilege. It’s such a great opportunity as a student, regardless of what position or field you want to go into. It’s just such a shame if you approach that experience with the mindset of, “Oh, I’m going to try and make this as easy as possible so I have to do as little work as possible, end up with a good grade,” and none of it matters. I really think that’s a sad way to look at it.

Unless we can get the majority of students to hold this mindset, AI use will continue to pose a threat to the academic experience. Idealistic? Maybe. But why? Have we strayed so far that we now have to convince students they are in school to learn?

Apparently, yes. The way the current system is set up means this won’t be easy. But plenty of teachers have already built classrooms where it’s happening, and it’s worth looking into those rooms to see what they’re doing. I’ve been around long enough to see the difference between teachers who inspire, lead, and role-model and those who mail it in and practically invite cognitive offloading in their classes.

The students I spoke with made clear distinctions among classes, professors, learning environments, and assignments, and made up their own rules when they were and weren’t comfortable leaning on AI. They are figuring it out through trial and error. Before we render any sweeping judgment about their behavior, it’s worth finding out how and why they’re making those decisions. That’s the reality of college and AI in 2026, and it’s already drifting down into high school. We're five academic years into free access to powerful AI and most institutions are still in the same gear. Every year from here on out has got to be better than the one before it. We are running out of time.

Connect With Me

Beyond this newsletter, I work directly with schools, educators, and other institutions about pedagogical questions raised by AI. Take a look at my website and reach out - I’d love to hear what you’re working on.

Back in June, I ordered my transcript for a Masters program I completed online from 2021 - 2025. The St. John’s Great Books program is known for its rigor and unique reading list - not so much for its credentialing. The resulting de-emphasis on grading means SJC faculty only offer comments on papers and don’t share their marks with students at the end of each semester. The only way I could see how I did was to request my transcript.

When it arrived last week, I confess I was curious. Fifty-plus years in education - first as a student and now as a teacher - have conditioned me to accept the absurdity that a single letter grade represents a student’s entire learning experience in a class. In one seminar, we had no papers or other means of assessment - that grade might as well have been pulled from a hat.

Overall, I did well. Yet, none of those letters revealed how well I understood Plato, Kant, or the Bible. The Great Books program is seen more as a calling, a personal quest to actually read the classic texts with like-minded peers. Assigning grades in that context feels almost like an oxymoron, but as a degree-granting institution, it’s a requirement.

Once students reach a certain age, we issue letter grades for everything - papers, tests, projects, quizzes, presentations, and so on. The grading system is so deeply ingrained in schools we almost never question it.

AI has only supercharged the grading arms race. With competition for higher grades at an all-time high, the temptation to offload work, bypass learning, and produce solely for the grade seems higher than ever. The motivation to perform in school is almost entirely extrinsic.

The question we probably don’t want to know the answer to (or, the one deep down we already do) is:

How many students view learning as the primary goal of their time in class?

What Flow Requires

That question is really a question about motivation. The best book I’ve read about motivation was published almost four decades ago and isn’t actually about motivation.

Mihaly Csikszentmihalyi’s Flow: The Psychology of Optimal Experience is more relevant than ever to our AI-saturated world.

Flow is his name for the state in which a task absorbs a person so completely that it shuts out almost everything else - it results in pure enjoyment, even if the activity is difficult (like swimming the English Channel). While he identifies eight components of flow, three of them describe conditions a teacher can actually create. The activity has to demand real skill and a task with a realistic chance of completion. It must have a clear goal. And, crucially, feedback has to be immediate.

Every teacher knows instinctively that tasks - whether in the classroom, on the field, or on your own - need to be calibrated so that the difficulty level matches a student’s edge of capability. Design a task that requires skills students have not yet mastered, and the ensuing frustration prevents “flow.” If it’s too easy, boredom sets in. The golden ratio is that match between ability and difficulty that leaves us engrossed.

Establishing flow conditions in a classroom does not mean cheap entertainment or diluting rigor. In fact, the opposite is usually the case. Matt Brady, a chemistry teacher, opens the year by having his students make gunpowder. They have to convert percentages to grams before they can collect a single ingredient, and write it all up afterward. Teachers like Brady have always existed. As a student, I considered them eccentric. Now I know they were building the optimal conditions for flow all along.

In a section entitled “The Challenge of Lifelong Learning,” Csikszentmihalyi writes:

Extrinsically motivated education is a perfect phrase that captures modern schooling. The motivator is usually grades. Most classes students complete over the course of their academic careers are graduation requirements.

Csikszentmihalyi continues:

But a person who forgoes the use of his symbolic skills is never really free. His thinking will be directed by the opinions of his neighbors, by the editorials in the papers, and by the appeals of television. He will be at the mercy of “experts.” Ideally, the end of extrinsically applied education should be the start of an education that is motivated intrinsically. At that point the goal of studying is no longer to make the grade, earn a diploma, and find a good job. Rather, it is to understand what is happening around one, to develop a personally meaningful sense of what one’s experience is all about. From that will come the profound joy of the thinker …

A student who glides through school, even a successful one, but who has never been taught to question their own opinions or been challenged by someone with more knowledge, is utterly at the mercy of our current media environment. It accounts for so many of the problems with online behavior - toxic forums filled with misinformation, disinformation, hate, and ignorance as well as the superficiality of short-form video, influencer and TikTok culture. Teachers need to, unironically, model the “profound joy of the thinker.”

How Would you Get Someone to Actually Read This?

Human beings are naturally curious creatures - we want to learn new skills and new things all the time. Throughout the bulk of human history, the motivation was not external validation in the form of grades. It was something deeper, more primal, and fundamental, often life-saving.

My teaching log for my graduate program in the late 90’s included this provocative question:

Think back to a time when you had a high level of success in learning something that you feel was challenging to you. What was the nature of the learning situation that enabled you to master these difficult ideas or skills?

I wrote about my response to that question in a previous post, but I want to expand on the distinction between extrinsic and intrinsic motivation.

Motivation is essential when it comes to learning. The course I wrote about was one in which I became internally motivated. The instructor had set up conditions in which the learning - in that case, improving my ability to write well - overrode all other concerns.

For one class, the professor brought in a tool that looked like something from a medieval peasant village - it resembled a huge pair of ancient pliers. We were told to describe what it did1 in preparation for an assignment where we had to explain, in detail, how a common, everyday modern invention worked - something like a microwave or an alarm clock. I chose a stapler. My piece read like an instruction manual. I'll never forget his comment: "Okay, now how would you get someone to actually read this?" A lightbulb went off about the importance of an authentic audience, something never emphasized in my high school classes.

Often, internal motivation does not arise anywhere near a classroom. Remember a time when you tried to pick up a new skill for no reason except your own interest. Maybe it was a new language, an instrument, or a hobby like amateur photography. In those cases, the feedback was self-evident - could you talk to a stranger, play the chord, or take a picture that looked good? If so, you were learning. There was no grade. You pushed yourself solely because of a desire to learn and get better. I wanted to learn how to write a description of how a stapler worked that a reader would find worthwhile.

What scares me when I think about that course today is whether I would have handed the whole problem - observe the stapler, then explain how it works - to an LLM. I like to think that, because of the seriousness with which I approached the class, I would have abstained.

I have been told over and over by kids today that there is an easy solution to the cheating crisis if demonstrating whether the student actually learned the material is the true goal of the professor - all assessments need to be done in analog under teacher supervision. But that still mostly presumes external motivation.

On one level, that saddens me because I’ve also been told by students that when a teacher does rise to the occasion, when they make the course material come alive and infuse their classes with purpose, students want to do the work. They want to learn because it’s been made real for them. That was my experience in that freshman writing course. It is still possible.

Where I See It Work

Teachers who can genuinely inspire, coach, advise, and redirect students to master their area of expertise will become even more valuable in the AI era.

Despite so many stories about student cheating with AI and the lengths to which educators are going to prevent it, I wonder if we can look harder at our own practice. Thousands of teachers around the country routinely lead students to invest their best effort in their courses - whether it's algebra, chemistry, English, or computer science. As I frequently tell my debaters, if you are not convinced by your own argument, you can’t possibly expect to persuade someone else. If a teacher is not sincerely interested and invested in their subject matter, why should their students be?

In my experience, the teachers who tend to draw students in are passionate and highly trained in their field, model lifelong learning, are fair, and demonstrate care for their students. They are also, often, demanding. Even those qualities may not be enough to seed intrinsic motivation - what effective teachers do best is design the work differently.

In my own work, historical simulations and role-plays have had the most success turning the classroom into one where more students adopt an intrinsically motivated mindset. Students inhabit a real historical figure and compete in a goal-oriented activity with clear stakes and immediate feedback - the very conditions Csikszentmihalyi identified as conducive to flow. Those activities, often comprising a week or more, are the ones students bring up when they come back to visit years, sometimes decades later. It shocks me that I can often remember who they played and what they said. Simulations are flow classes for me as well.

I'm extremely fortunate. My class sizes are small, my school is well-resourced, and many of my students are highly motivated to begin with. Yet I still see the difference with these activities. I don't think most kids use AI to write their introductory speeches, position papers, or final reflections. I can't verify that with certainty, but the work is public - they deliver it in front of people who respond in real time - and the kids are invested.

What School is For

Csikszentmihalyi makes a provocative claim about schooling later in the book:

Similarly the value of a school does not depend on its prestige, or its ability to train students to face up to the necessities of life, but rather on the degree of the enjoyment of lifelong learning it can transmit.

The “similarly” refers to his observation that the value of a community should be judged on the opportunities it affords its members to live to their full potential. Thus, Csikszentmihalyi views school as a place to cultivate the importance of learning above all else. Most educators, and Csikszentmihalyi himself, would agree that’s not happening in our current system.

Others might argue that school is a place to obtain skills first and foremost for their entry into society. Why can’t they do both? Why can’t students experience the joy of learning and acquire the necessary skills for work and for life? Too many students experience school as a place that extinguishes the joy of learning rather than celebrates it.

Most of us got into this because learning is not only beneficial and meaningful, but can be profoundly rewarding under the right circumstances. Summer is the time to remain idealistic - I know what happens when the reality of October hits. But as we walk back into classrooms over the next month or so, it's critical to remember that our job as educators is to create, preserve, and sustain those conditions for learning. Hopefully, even with the ability to produce answers and bypass flow just a click away, more students will experience the joy of learning intrinsically in classrooms explicitly designed for it.

Connect With Me

Beyond this newsletter, I work directly with schools, educators, and other institutions about pedagogical questions raised by AI. Take a look at my website and reach out - I’d love to hear what you’re working on.

I’ve started and restarted this post a dozen times as I’ve gotten ready for hip replacement surgery this summer (which finally happened last Wednesday!).1 Experiencing the detailed care of my surgeon, nurses, and physician assistants only reinforced how much their protocols run on technology. Visits are often recorded, images documented, and almost every stage of the process digitized. When I asked whether any of this data was integrated with AI, the short answer was yes - with the privacy and security caveats you would expect. The hospital was the latest reminder that AI is being folded into every industry we encounter, whether we know it or not.

Everyone, including teachers and students, needs to work in an AI world. Almost every story about AI and education invokes the risks of cognitive debt, cognitive offloading, and a loss of cognitive patience. As we enter the 5th academic year where students have had access to powerful AI, most of the news out of schools is negative. More telling, even the most charitable observers would agree that few of us know what we’re doing when it comes to AI. But I’m hopeful that will change this fall.

I've read the reports and documented my AI fears when it comes to unsupervised student use. What I'm trying to square is that my own experience with AI has been mostly positive. Working with AI over the past three and a half years has required a learning curve, but it has made me a better planner, a more precise writer, and it has forced me to slow down and review my work much more intentionally. With billions of people now using AI models, it's clear I'm not the only one benefiting.

So much of the angst around students' AI usage presumes that they will continue to use it the same way they have over the past three years. We have to remember it is still early days. Getting real benefit from AI takes a classic set of skills (or habits or dispositions if you prefer), and every one of them is something we have always taught, long before 2022. Effective AI use requires planning, precision, and patience. They apply whether you let a chatbot into your classroom or not.

Learning Skills Are AI Skills

One major problem for anyone advocating student use of AI - whether in the classroom or on their own - is that there is very little evidence it helps students learn. The further I get from November 2022, the more it shocks me that this technology was unleashed on an unsuspecting public, especially teachers and students, with so little warning. No other “arrival technology” in my 30 year career has disrupted the teacher-student contract as much as generative AI.

I’m aware that my usage rests on skills I formed over decades. Yet, I’m confident we will ultimately find ways to absorb AI into education by leaning on the same meta-cognitive habits we have always valued. Planning ahead, communicating with precision, and having the patience to see a project to completion are all things any teacher in any classroom already models.

The critical question over the next several years is when and where students will continue to build these skills - alongside AI assistance or away from it. Right now, most teachers lean towards the latter, and that may hold for a while. What I don't see, however, is how we are going to have a choice regarding student usage outside of school. My hope for this fall is more evidence of educators successfully modeling these skills, regardless of whether they bring AI directly into the room with their students.2

What Do People Do All Day?

I often share with students the reality of work. Years ago I read that college-bound high school juniors are as busy as CEOs. There is almost no other time in your life when you will be expected to excel at five entirely different subjects. Most of us will eventually get the opportunity to specialize in an area where we are strongest, but that is usually not the case in high school. That fact alone explains a great deal about how students use AI. A kid managing five courses plus outside school commitments has no time to plan, no incentive to be precise, and little patience left for a second draft.

I've explained to students that almost all work is best approached by breaking it into smaller parts. To do that, you need to check your anxiety, step back, and survey what actually needs doing.

AI raises the stakes on that crucial habit. We have all gone down a rabbit hole, and AI rabbit holes are among the worst, partly because the machine flatters you at every turn and will happily lead you somewhere useless or overly complicated if you're not careful. The discipline any project requires has to be in place before you ever go near a chatbot. If you know what you're doing, though, AI can assist with the planning by challenging assumptions and expanding on strategies. It can also help you decide if the task is worth using AI on at all.

Planning

My hunch is that a startling number of people still use their chatbot as a string of disconnected one-offs. If you use AI infrequently, you probably turn to it for a few questions, close the tab, and move on. Which means you have dozens of orphaned chats piling up in a sidebar, unnamed and unrecoverable. If that is still how you work with AI, you are missing what's been happening over the past two years.

I have no interest in writing an AI tutorial - there are dozens available online. But the models have rapidly added organizational tools and none are difficult to learn. Keeping related chats inside a dedicated Project folder means the work stays together and the model can draw on related material. Adding a set of instructions and reference documents within each folder considerably improves the output. These are simply AI versions of separate notebooks and divider tabs, a habit older than most teachers. Gemini, ChatGPT, and Claude offer almost all of these for free, and the lowest-tier paid versions provide access to even more sophisticated capabilities.

We constantly assign students work to do, but in the age of AI, we have to remember that planning out a multi-step activity - whether it is a research paper, a science project, or a presentation - is a meta-cognitive process. Is the goal to produce a document or ask for feedback? Are you creating, analyzing, or organizing material? Is this for you, or for someone else? Nine times out of ten, simply asking yourself what it is you are trying to do will force a decision about whether or not to use AI in the first place.

None of this is new to teachers. We have explained and modeled executive functioning skills for years, through graphic organizers, outlines, and day planners, well before AI was available. I am a longtime evangelist for David Allen's Getting Things Done, and I wish someone had handed it to me before college. Planning ahead does not come naturally to the teenage brain. It never has. That is precisely why we teach it. We need to prioritize the planning before the AI, now more than ever. Refusing to recognize that AI can help with the planning itself, however, is also a mistake. As adults, we must constantly model judgment.

Precision

Using AI has made me a better writer for one simple reason. It’s made the cost of vagueness instantly painful.

I've written previously about growing up in a household filled with lawyers who were maddeningly fixated on using precise language. It created an ideal training ground for writing clearly. Law school provided three more years of practice. Writing with precision and clarity was not an abstract concept in my household - I saw it up close in every brief my father ever showed me.

Iterating a conversation with a chatbot is an exercise in specificity, context, and requirements. If you want to convert notes from a meeting into a bullet-pointed to-do list with each entry no more than five to seven words, then you need to describe those specifications. Or if you want to design a research prompt that will focus on retrieving publicly available academic, government, and other vetted documents only, you need to ask.

When you do not precisely identify what you want, when you do not supply the context of the request, when you are not even sure yourself what you're after, you should not be surprised when the output is subpar. The more detailed and clear your request, the better the results. Whenever I write a prompt, I am constantly revising and scrutinizing my work to ensure there can be no misunderstandings. Most of the disappointing output traces back to an unstated assumption in my request, or some instruction I didn't even realize mattered until I saw how the AI responded.

We do the same thing with students. Using AI has reminded me how much depends on clear instructions. We cannot blame the student for misunderstanding an assignment when we don't take the time to be specific.

This is also true from the student side, and one reason that modeling precision is the province of every writing teacher. Anyone who grades student work has written some version of I don't know what you mean here in the margin - writers often forget that the reader is not inside their heads, and younger writers are especially prone to it. Using AI delivers feedback on fuzzy requests immediately and without mercy. The truth is that a teacher who never allows AI in class is still teaching the skill that matters most if a student opens a chatbot on their own.

Patience

Most kids just want to be done with school, period. They want the current assignment off their plate and the very thought they need to “do it over again” induces horror. Anyone who works around teenagers knows this.

If you put the same kid on the field, however, something surprising happens. They will run the same drill over and over until their coach stops riding them. It’s not that they don’t understand what patience looks like. It just doesn’t map onto their experience with reading, writing, or arithmetic.

I am a firm believer that writing is rewriting, and what is rewriting if not patience? When a student wants to hand in a first draft, the question I ask is whether it is their “best work.” Most of the time they hesitate which tells us all we need to know. Our first drafts are never our best work - adults included.

None of this has anything to do with AI. Patience is what we model every time we tell a class that something polished - a poem, a painting, a song - took longer than you might imagine. Extraordinary results require extraordinary patience.

This is why the way AI often gets demonstrated to the public bothers me.

Ethan Mollick recently dropped another one of his “state of the union” posts on the current abilities of the top models. His work is the most up-to-date and accessible I have found, and I recommend it to anyone who wants to keep track of AI developments.

One area where I think he does his readers a disservice, though, is his demonstrations of impressive and "cool" applications built from a single prompt. In his most recent post he compared one line about a procedural brutalist building creator across two models, with the newer version significantly "better," if better means it looks cooler than the first one. There are a lot of things AI can do that are "cool" - but is that what makes it useful?

I understand his larger point, and the mere fact that these models can do such things, considering where we were even a few months ago, is mind-boggling. But no one doing serious work with AI does so based on a single sentence prompt.

The way AI is going to change work is through the people who figure out how to tease a project apart, separating the human review from the repetitive labor AI excels at. When those people begin to pull ahead of the people who either do not use AI or use it ineffectively, then we will see its impact. That takes the patience to iterate a project from the beginning and see it through, rather than issuing a one-shot ten word prompt.3 Perhaps we will get to a stage where that becomes the norm, but we are not there yet.

The Year Ahead

Two years ago I was fixated on simply being AI-Aware as we were all learning how to handle the emerging AI crisis in schools. Last year my strategy was AI-Proofing, which was defensive by design, as I, along with many others, attempted to recreate assignments that could not be easily outsourced. The evolution makes sense, but it's not where I think we will end up.

My mindset this year turns to instruction - not specific to AI, but a re-commitment to the durable habits of mind that improve learning no matter the tool. By reminding students of the importance of planning ahead, communicating clearly, and exercising patience in everything they do, we will have a successful year -with or without AI.

I have seen all three of these skills converge this summer as I have been building out full websites for my courses.

The planning came first, as I had to think carefully about what I actually wanted these sites to do, what information they needed to contain, and how they were set up. Precision was present throughout, as I needed to specify exactly what I wanted each page to do, where and how I wanted the links to operate, and what kind of layout and formatting would make it most useful for my students.

Patience has been ongoing, as I am still iterating and fine-tuning before the school year starts. A one shot prompt might have created something "cool" but would have produced garbage as far as my actual classes were concerned.

The web design project required skills applicable to any task - this one just happened to incorporate AI.

The Wrong Argument

Hospitals, marketing firms, technology startups, and a whole host of other industries have leaped feet first into AI. No one wants to be left behind. Results have been unsurprisingly mixed even though massive amounts of capital have been invested. How this all shakes out is anyone’s guess but we won’t know for awhile. Amara’s law hovers in the background - “we tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.”4

Education is still behind. Few teachers are taking advantage of the most powerful ways AI can be used to augment their work. Most of them are likely keeping it to themselves or, occasionally writing about it publicly. Many teachers (unlike their students) are still deciding whether AI should be allowed on campuses at all.

That argument, I think, is the wrong one to be having. Teachers who want to use AI with students and teachers who want nothing to do with it can model the same habits, and both can point out that those habits are what separate using these tools well from using them badly. In both cases, student walk away with something that works. Technology changes. Good teaching does not.

Connect With Me

Beyond this newsletter, I work directly with schools, educators, and other institutions about pedagogical questions raised by AI. Take a look at my website and reach out - I’d love to hear what you’re working on.

One of the perks of a teacher’s summer is the pile of books we now have time to tackle. I estimate that I’ve spent upwards of six hours a day reading over the past twenty-five years. That’s not unusual - I suspect many of my readers engaged in knowledge work exceed that count. A rough calculation puts that at more than 54,000 hours of reading. Using a modestly high reading speed, that easily translates to more than a billion words over that time.

This means I can tell at a glance whether a Google AI summary is likely to be on target, whether I need to read deeper on the topic, quickly locate a trusted article or source to skim, zero in on the section I need, or whether I should regroup entirely and start a new search. I do this effortlessly, without much thought, and feel confident about the process. Most students can’t. Students will not or cannot read AI output with the understanding or the skepticism it demands, because most do not have anywhere close to a billion words under their belt to parse its language. The overlooked issue is that if we actually want students to use AI effectively, either in class or at home, the skill we need to worry most about is not writing. It is reading.

How to Read a Book

Last summer I argued that the teaching method in the Great Books program I finished was the one AI can’t touch. One flaw in that argument is if students start off-loading their reading to AI. But even if they do, many students do not have the ability to read AI summaries carefully, let alone ones based on classics like The Republic or The Odyssey.

A book I had heard of but never read before the St. John’s program is Mortimer Adler and Charles Van Doren’s classic How to Read a Book (Van Doren of Quiz Show fame). In it, the authors lay out a method for tackling more advanced texts. The whole book may seem dated, but it is worth reading, especially the opening chapters. My major takeaway is that we rarely teach more advanced reading skills to improve comprehension once we have finished teaching basic decoding in the early grades. More teachers would benefit from applying some version of the strategies they recommend.

A piece by Carl Hendrick sharpened my own thinking on the critical importance of reading.1 A prompt typed into ChatGPT, he points out, contains almost none of the knowledge in the answer it produces. The model’s response comes from everything it absorbed during training. Human readers operate in much the same way, supplying meaning from a lifetime of accumulated vocabulary, background knowledge, and life experience. The difference, of course, is that while AI may have access to billions and billions of words on virtually any topic imaginable, what it doesn’t have is emotions, experiences, or human connections to the world that provide uniquely human interpretation when reading a text. An AI cannot be moved to tears at the conclusion of a novel even if it is able to identify the emotionally charged language embedded in a paragraph.

When students do not have the foundation built through thousands of hours reading on their own, they do not have the vocabulary or muscle memory necessary to impart deeper meaning, especially as texts get more complex. Unsurprisingly, reading is one of those activities which improves exponentially - the more you read, the better you read, the more you understand, and the better you read and understand, the more you want to read.

Having read more than a billion words is a by-product of my disposition and my profession - it provided the training corpus that lets me read as efficiently as I do, and students simply haven’t put in the time. That is one reason why AI’s fluent and confident output often goes unchecked.

As a history teacher, I am often guilty of the very thing Adler warns about, even though I have tried to make reading instruction more a part of my practice and feel even more compelled to do so in the age of AI. I assume kids read better than they do, and I am programmed to spend my attention on content. That assumption needs re-evaluating, especially as we head into a world where delivering content instantly, on demand, synthesized, and drained of its original meaning becomes commonplace. We still need students to handle literary fiction, dense secondary sources, and primary documents.

Different Kinds of Reading

Not all reading is the same, which is a crucial thesis of How to Read a Book. Reading a thriller for its plot is a different act from reading a novel for its ambiguities, which is different from reading a work of history or philosophy for its argument and its evidence. Many people read mostly for pleasure, passively, like watching a film. Few people want to struggle with more difficult texts on their own. Most students, even many of our most proficient readers, rarely tackle tougher texts independently. Online social media rewards short paragraphs, skimmable listicles, and now, because of changes to Google’s search, instant answers that arrive before the question is even fully formed.2 Maryanne Wolf calls the capacity we are losing cognitive patience, the willingness to stay with a difficult passage long enough for its meaning to surface.3 Deep reading has always been the hardest level to sustain and the easiest to lose - even adults frequently report difficulty staying engaged in deep reading in today’s social media saturated environment that rewards and cements ever shorter attention spans. Any child in school today is going to exist in a world where instant answers will be the expectation, not the exception.

Evidence from the Classroom

One of the best courses I ever took, for my Master’s at Columbia’s Teachers College in the late 1990s, was titled Building Bridges and Breaking Down Walls. It was co-taught by two public high school teachers, one in English and one in History, in Manhattan. We met for three hours on Saturdays over twelve weeks. Unlike the education courses I took over the summer, where we read Foucault or Derrida or some other theorist at a fifty-thousand-foot level, I was teaching full time while enrolled in this class, so the fact that it spoke directly to the realities of the classroom allowed me to implement what I learned the following week.

One point that resonated was how English teachers tended to emphasize the language and intent of the author, while history teachers were more concerned that kids understood the content. But the overall focus of the class was the critical importance of literacy in general.

Read it Again

The English teacher related how, early in his career, his reflexive response when a student expressed difficulty with a text was to tell them to read it again. This happened several times, in the same class, with the same student. Each time, he simply said read it again. Finally it dawned on him that the “read it” part of the instruction was where things had gone sideways. The student could not read it in a manner that allowed for comprehension. He had been treating a reading problem as a motivation problem. It finally occurred to him that he needed to explicitly teach reading strategies.

That story has stayed with me because it matches my experience - most high school teachers assume, though rarely examine, that students can already read well. Most of us were trained in our subject, not in the pedagogy of reading. Much has been written about students’ poor reading abilities, some of it designed to go viral,4 but almost always in a helpless, throw-up-our-hands register or scathing dismissal of “kids today”. We are much better at observing the problem than proposing solutions.

I began folding the reading strategies we discussed into my middle school history classes. I built a handout of increasingly difficult passages, and we annotated with marginalia - text to text, text to world, and text to self reflections - along with questions, words or phrases that puzzled us, and whatever we could untangle behind each short section. The point of the lesson was that there are many levels of reading, and you should not feel ashamed or inadequate when you hit an impasse. Friction and struggle were the point.

I believed that because it described my own reading life. I have read everything in front of me since I was ten or so, including many books well over my head. All of that time invested built whatever persistence I have as a reader. You do not need to understand everything to benefit from a book.5 Struggling with a text you cannot fully grasp stretches the muscle and introduces you to ideas, vocabulary, and situations you have to work through yourself.

With students, we need to help them with more advanced texts. Luke Morin has written about watching his students flounder with a single demanding sentence from Ray Bradbury, not for lack of effort, but because they did not know enough of the words to make a coherent interpretation.6 Past a certain point, struggle stops being productive. Students shut down. Assigning useful books is largely a matter of calibration - and sharing your own struggles is a wonderful way to model for students both the joy and the challenges of working your way through a difficult text. One of my most enjoyable teaching experiences in my entire career was reading Plato, Aristotle, and Homer with my Great Books students this past year. I tried to emulate my St. John’s tutors, who offered conditional interpretations and invited students to share their views. Reading a challenging book together can be a magical experience provided everyone is equally invested.7

Reading in the Age of AI

AI has turned reading on its head. The overwhelming focus in school, online, and in faculty meetings is on the impact of AI on writing. That is understandable, since writing is what we grade and what we require as proof of learning.

Reading, though, is the unexamined half of the literacy question, and it is where I think we should be more focused, especially as more schools bring AI into the classroom for students to interact with directly.

Marc Watkins identified this early, back in 2024, in a post bluntly titled “No One is Talking About AI’s Impact on Reading.” He warned that our fixation on writing was letting the reading question slip by unnoticed. Two years later, with AI far more embedded in how students find and process nearly everything, the focus on reading continues to be eclipsed by coverage of AI’s impact on student writing.

The irony buried in all the attention to writing is that reading and writing have never been separable. Ask any writer and you will find a voracious reader. A student who does not read will struggle to write well, no matter how many outlines and graphic organizers we hand them. Schools need to spend as much time thinking about teaching reading as we do about writing, and many of the ways AI is being marketed to help students read are just as controversial as its writing assistance.

Should We Individualize Texts for Ability?

A few years ago I used Brisk8 to help a student learn important material, and the decision perfectly captures the skill-versus-content dilemma identified in my Building Bridges course. I had a tenth grader who could not summon the energy to read in class. During silent reading and annotation her head slumped onto the desk, and she told me, repeatedly, just like the student in my teacher’s story, that she did not understand it. It did not help that the text was a hard secondary source on why the new United States had to abandon the Articles of Confederation. It was filled with abstract terminology - federalism, states’ rights, separation of powers. I needed her to understand the content for the next assignment.

Between classes, I used Brisk to rewrite the material several grade levels down, so she could work through it with our learning specialist. It helped. She understood the basics and was able to participate in the next activity.

Was that the right use of AI? She got the content she would otherwise have missed. The personally tailored version of the reading did its job and was not something I could have replicated easily. On the other hand, the simpler text removed the friction and the struggle. Did that make her a better reader?9

I solved the content problem by skipping the reading problem. But if we multiply that single decision across every assignment in every humanities class, what happens? When every student can dissolve any hard text on demand, where do they have an opportunity to build those crucial skills?

My very first Substack post described an AI exercise I did with students in which I built a custom GPT trained on a chapter from our American history textbook. The key instruction was that students had to read the chapter first, then use the GPT to go deeper and ask questions. Part of the activity was also to hear how they felt about using AI this way.

Looking back, I thought it was an innovative use case, but it’s telling that I have not done it again. I am not opposed to using AI in select circumstances to support reading comprehension and probe a text more deeply, but I was not convinced students would actually read the original first if they knew AI was waiting to help with the analysis. As I said in the post, I would never want students using AI in place of reading the original, but, just as with AI-assisted writing, the risk of reverting to cognitive offloading was too high. The other takeaway was equally clear: the strongest students got the best results. The ones who already read and wrote well quickly figured out how to improve their prompts and, critically, actually read the output with care. That’s something every educator needs to remember whenever AI is brought into the classroom.

As students get older they can find ways to extract AI’s benefits - and there are many - but I am more and more convinced that the bottleneck to effective student AI use is mostly a problem of reading. The sycophancy and confidence of AI output - worsened by the fact that students rarely know sophisticated prompting techniques or have access to the most capable frontier models - means many are not getting the most effective AI experience. And models carry worldviews that almost no one is aware of - a recent piece in the Economist investigated the many ways frontier AI companies shape the values that show up in open-ended responses, according to their own goals and preferences.10

If teachers do use AI in class, they need to think hard about how to structure the interaction to reduce these tendencies. Whether that means asking the AI to respond at an age-appropriate reading level (you can specify the lexile band) or, better, capping each response at 250 words or fewer, the average student user is otherwise likely to be overwhelmed. These workarounds exist, and I am sure many teachers have found them, but from my vantage point, and from what I see in the media coverage of AI in classrooms, almost none of this is discussed.

We Have to Be More Deliberate About Reading Instruction

I say this as someone who considers himself a strong reader, which is what makes my worst school experience instructive. I was terrible at science, in retrospect, largely because I could never get through the textbook. The vocabulary stopped me cold and I never built the discipline to look up each term. When I did, it only led to more technical language, and with no context I understood, the definitions themselves were impenetrable. This is exactly what Hendrick describes. I could “read” as well or better than my peers but had none of the domain knowledge, so I could not puzzle through the passages. It is the same wall plenty of confident readers hit the first time they try to read a dense Supreme Court opinion, and the one our students hit daily in classes where we assume they can read but the content is completely foreign.

Legal reading was the mirror image of my science debacle, though not because it came any easier. Motivated no doubt by early exposure in a family filled with lawyers, I found that my favorite part of practicing law was research, though learning to read a legal opinion still took real time.I often forget that this did not come naturally to me either. It was built from poring over thousands of documents over years - annotating, analyzing, questioning, and writing about the issues. Legal training in many ways can be summed up as learning how to read like a lawyer.

The Pitfall of the AI Summary

Relying on AI summaries without ever examining the source is a serious problem. The difficulty of research today is no longer finding sources - an AI Deep Research report can surface more relevant material in minutes than most of us could in a week before the advent of AI. The difficulty is reading that report, with its hundreds of citations (many from Wikipedia or Reddit unless you write a rigorous prompt restricting it to academic journals and other vetted sources), and doing the real work of tracking down the best ones and reading them carefully. How many students - how many adults - actually do that? Driving home the importance of careful reading is going to be among the most critical things we teach in the coming years.

Reading is so essential to almost everything we do in school that it’s sometimes easy to forget just how important it is to be a fluent reader. It’s the cross-cutting skill for every subject. In the Age of AI, it may be the most overlooked, and most valuable, habit we can model and emphasize for our students. If I could wave a wand and instantly bestow one proficiency on every student I teach, it would be the ability to read deeply. So many doors open once that muscle is built. Reading carefully and critically may be the single most important weapon we can give students when it comes to using AI.

To be clear, I am hardly a reading specialist. I have taken a few graduate classes, read some books on the subject, done some professional development, and, most importantly, worked with kids for over thirty years. I am more and more convinced that reading is what students most need, and that, for most secondary school teachers, it is something we are least prepared to teach.

There are many experts on Substack, but the two previously mentioned - Luke Morin and Terry Underwood - have influenced my thinking more than most, and both know far more about literacy and teaching reading than I do.

Terry Underwood has forgotten more about literacy instruction and research than I will ever know; go back through the archives of Learning to Read, Reading to Learn if reading pedagogy is a passion of yours. As I was finishing this piece he published a characteristically sharp case for how AI, used well, can help reading instruction.

Luke Morin’s Middle School Literacy Project is one of the most useful and humbling repositories of reading strategies I have found. And he writes beautifully.

If reading about reading is your jam, their newsletters are a goldmine for learning more about reading instruction.

Connect With Me

Beyond this newsletter, I work directly with schools, educators, and other institutions about pedagogical questions raised by AI. Take a look at my website and reach out - I’d love to hear what you’re working on.

Since school ended a few weeks ago, I have pulled back a bit from the AI conversation. I needed to get outside and away from my computer after a very busy spring. The World Cup has helped, though not as much with the getting-outside part. The rule changes have improved the viewer experience and the U.S. Men’s team is onto the knockout stage, which is a bonus.

Watching the matches is a reminder of the first time someone demoed ChatGPT for me. It was the first weekend in December 2022, the weekend the U.S. lost in the Round of 16 to the Netherlands in Qatar. I was running a debate tournament. We had a big screen up to display the pairings, and another coach pulled up ChatGPT, which had debuted a little over a week earlier. I hadn’t heard of it. I asked it to write a poem about the World Cup in the style of Shakespeare, which it did, in seconds. I was amazed. What an innocent time!

After millions of words written about AI and schools, here in the summer of 2026, I think students and teachers have arrived at something like a detente. If we intend to move forward productively in the fall, we need to stop fighting about AI long enough to ask what school is actually for.

The AI Arc (Late 2022 - Summer 2026)

Generative AI arrived out of nowhere, and no one was hit harder, day to day, than teachers and students. Amazement was immediately followed by fear - mostly of cheating. For some it generated excitement, then anger and backlash as people learned more about how these systems were trained, what they cost, and their environmental impact. A 2026 Brookings report on K-12 found that, on the current trajectory, the risks of generative AI in schools outweigh the benefits. Skepticism was present from day one - both about the technology itself and even more about predictions of AGI, mass unemployment, and the end of humanity.

Ironically, most of the apocalyptic messages come from the people closest to the technology, who continuously tell us it will eliminate jobs, surpass human intelligence, and remake society in ways they themselves describe as dystopian. The founders seem genuinely surprised by the backlash, even though the frustration so many educators and the public feel about AI is a rational response to being told the technology will upend their lives.

For students, access is now ambient. Anyone with an internet connection can pull up ChatGPT or their favorite model, which is orders of magnitude more powerful than the ones that shipped in 2023. Every assignment now carries a decision for them - how much AI to use, or whether to use it at all. Many are already dependent. But plenty remain wary, and more than a few are frustrated and angry that they were placed in this position without being asked. On that, teachers and students are often in agreement.

The AI question has both prompted and been folded into a larger conversation about phones, laptops, and the screens we have handed children going on two decades.

Schools, caught in the middle, have gone in wildly different directions. Some went all in, embedding AI into their infrastructure and their mission. Some refused and banned it outright, and many are still on their heels, yet to engage with the issue head on, churning out increasingly convoluted AI policies they have no way to enforce.

Most landed somewhere between deer-in-the-headlights and reluctant adoption, pushed along by district and university partnerships with AI companies often made under pressure to do something and not be perceived as falling behind. A few have taken thoughtful steps to confront the situation as it actually exists, resisting both the hype and the doom. And the gap between the people who use AI daily and the people who either reject it altogether or hold it at arm’s length keeps widening.

The school AI conversation sits in a larger AI media ecosystem that constantly veers from one pole to the other, making it even harder to focus on what matters amid the predictions, prophecies, and promises.

That’s the landscape I see as most schools close out the 2025-2026 school year. We’re battered, exhausted, but still standing.

Stalemate, or Detente?

My gut initially told me students and teachers have reached an AI stalemate this past academic year, like two prize-fighters circling each other in the ring, each exhausted by the flurry of punches thrown in the opening rounds. Now they’re stumbling, hoping the ref will step in and call a draw. Students cheated with near impunity for the first couple of years, and teachers fought back with invasive detection software and a return to blue book exams. Honor codes were rewritten, disciplinary outcomes were murky, and most accusations weren’t worth the time-sink to pursue.1

But this is what happens in an arms race: no advantage remains for long. Detection software was answered by humanizers built to defeat it, which were answered by better detectors, which were answered by better humanizers. Every advance invites a counter. Escalation with no decisive edge eventually grinds everyone down.

I don’t think student use of AI fell this year - the statistics tell the opposite story - but my hunch is they were more likely to use AI on the margins, where teachers could not or would not enforce strict “no AI” policies. Professors began, some enthusiastically and others reluctantly, to allow AI in limited circumstances, signaling not surrender so much as a recognition that AI had become an ever-present temptation to offload their work. Many instructors began to think more carefully about what they were asking students to do and why - a much-needed return to designing assignments around the authentic learning we claim to want. If AI leads to more curricular reevaluation that is, on balance, a net salutary effect.

What the Conflict Is Actually About

The detection war looks like a draw. It has created something closer to a detente - a condition where both sides are ready for a path forward. Students, repeatedly and consistently, have told us they want to be taught by human beings. Strip away the panic and that is what remains. The teaching relationship has always been at the heart of education.

No student ten years from now is going to tell a story about the chatbot that went off-script to tell them where it was on 9/11, or the chatbot that kept them after class to cheer them up, or the one that talked them off a ledge before a hard exam. Chatbots cannot read the mood in a room for those “teachable moments,” which are the ones students remember most. This is especially true in K-12 settings.

This is my essential observation. When that 2026 Brookings report concluded the risks of AI in schools currently outweigh the benefits, a central harm it named was the erosion of the relationship between students and teachers. The thing the research worried we were losing is the thing students keep telling me they want most. Whatever AI we let into schools has to protect that first.

This doesn’t mean AI has no role or cannot be used in ways that play to its strengths. Research is already being transformed by AI, and the ability to build bespoke tools through natural-language coding has real potential.2 But the notion that AI should become a central presence in U.S. classrooms, delivering and teaching content independently, has been undermined, especially over the past year.3

This is from my vantage point. If you are a teacher whose AI integration is really working, I’d love to hear about it. I’ve seen some of my colleagues use it creatively, and I know practitioners who are using it thoughtfully.4 But these examples feel like the exception rather than the rule, at least at the moment.

And despite what edtech vendors are selling, I have come to think most AI wrappers built for the classroom are a waste of time. For students who turn to AI to avoid their work, removing friction is the entire point; a wrapper that adds it back is a tool they won’t use. For the ones who want to use it well, they don’t want AI-lite any more than someone handed the keys to a car wants to be handed a bicycle. If we really want to teach older students how to use AI, they need access to the frontier models. For middle school and younger, classroom chatbot use is an entirely different conversation.

None of this means the detente is stable. The student who blew a deadline and is staring at a blank page at midnight on Sunday will still reach for AI, at least to get started - and no strong teacher-student relationship or coherent policy will reliably stop them. And the technology will not stay still long enough for us to get complacent. I have no idea whether the next disruption involves the proliferation of agents that move past chatbots, or ubiquitous smart glasses, or world models that create issues we haven’t even thought of. Anyone who tells you they know where all this is going is practicing a form of divination.

Student Confirmation

I’ve carried this feeling for a while this year, and recent conversations with students returning home from college confirm it. The former students I have talked with this spring keep saying versions of the same thing: most try not to use it but either can’t help themselves or have found specific tasks where it genuinely helps. More than a few have told me that they feel least guilty about using AI when they have a professor who doesn’t teach the material well in the first place.

Marcus Luther, a high school English teacher who writes really thoughtfully about teaching on his substack, The Broken Copier, asked his students again this year whether he should bring AI into his class. 81 percent said no, with only 6 percent in favor. One wrote that using AI would make them feel “way less accomplished.” His tenth graders, asked whether they wanted more or less technology in class, went around the circle saying the same word. Less. Less. Less.

While I have had success with AI tools in my advanced classes, I find myself landing closer and closer to his conviction. Using AI well in a classroom takes real expertise that most of us do not yet have, even as students keep using it everywhere else. We owe them guidance for the world they're heading into, but I'm not convinced how much time that means spending class on AI itself. That’s one of the issues I wrestle with the most. I don’t know how representative his experience is, but his student’s reactions don’t surprise me.

I imagine some of his students use AI, but my guess is not in there. He has built a culture of caring, noticing, and authenticity that makes kids want to learn how to write, how to think, and how to do the work themselves. I suspect I would have felt the same way in his class. The trouble is that not every student gets a teacher like Mr. Luther.

Angela Duckworth has described the same deep ambivalence in young people more broadly, many of them worried the technology is making them “lazy and stupid.”5 The pattern is consistent, and recent articles have named the trend: those using AI the most are often the most critical of it.6 This is the same impulse behind students booing commencement speakers this spring for invoking AI. Students are using AI more and understanding it better, but a growing number do not feel good about what it is doing to their learning.

We still do not have strong evidence that AI improves learning. A 2026 Oxford University Press study found that 72 percent of UK pupils, given the choice, declined to use AI on a school task, even as a majority used it weekly at home.7 One pupil put the distinction better than any researcher could: it “wouldn’t be what I truly think - a vague interpretation of what I’ve told a robot to expand on.” That absence does not prove AI has no place in school, but it does shift the burden onto the people promising the gains. I have found it immensely useful in my own work but have had mixed results using it with students. Talking about it with them helps. Experimenting is the only way we learn what’s effective.

More and more, I think our role in the classroom is to double down on the uniquely human connection, comfort, and guidance we provide. My ideal future is not one in which AI is absent from schools but one where students and teachers decide together what school is actually for, and only then where AI might have a place in it. I believe this will be one of the most important questions for educators over the next decade.

Why the Analogies Fail

I’ve never liked the printing-press and calculator comparisons. I wasn’t around for the printing press, and I barely remember the calculator “wars.” But AI is different in kind from other technologies that have reshaped schools. It strikes at the very heart of the learning process: it generates information instantly, answers the kinds of questions schools tend to ask more efficiently than students can, and easily undermines the very skills we want them to build. At the same time, and I can’t stress this enough, the technology is useful in the right hands and has the potential to let people do some increedible things. I reject any frame that refuses to hold both of these realities at once.

This is not an anti-AI piece. It is a pro-human one. We cannot keep bemoaning the problems AI brings without also examining the opportunities we have as educators to recommit to first principles. It means having the conversations we have spent four years avoiding - between teachers and administrators, between teachers and students, and among administrators, teachers, and parents. If we can do that, the past four years will not have been wasted. They will have set the table for the work that comes next.

A Final Indulgence

Before I published this piece, I asked ChatGPT to write a poem about the U.S. chances in this World Cup, in the style of Shakespeare - essentially the same prompt I used in 2022. Here is a snippet.

Enter Fortune, masked as VAR8

I am that glass-eyed witch whom none can love,

Who halts the shout, delays the leap, the kiss;

I pluck the eagle by the wing above

And whisper, “Was his shoulder off by this?”

The Americans are brave, and bravely young;

Their legs are thunder, yet their hearts must learn

That World Cups are not merely played, but wrung,

And half of glory is the nerve to burn.

Read it and notice your own reaction, which I think is as good a Rorschach test as any for where you stand on AI. If it still amazes you, you are probably someone who finds this technology genuinely remarkable. If all you see is a statistical engine assembling the next token, you’re probably not a fan. While the poem did not shock me the way the first one did in 2022, the VAR reference made me laugh. And a poem is now child’s play next to what these models can do.

But I think that’s the point. Effortlessly mimicking the style of a famous author about any topic imaginable is one thing; the more aware we become of what AI cannot do, the better chance we have of designing classrooms around the human qualities that make them come alive.

Connect With Me

Beyond this newsletter, I work directly with schools, educators, and other institutions about pedagogical questions raised by AI. Take a look at my website and reach out - I’d love to hear what you’re working on.

Getting AI “right” in schools has turned out to be much, much harder than most people anticipated - it doesn’t help that what “right” even means is a huge part of the problem. I have lived through several technology transitions in thirty years of teaching - the internet in the late 1990s, social media and smartphones in the late aughts, the Google ecosystem in the 2010s, and, most recently, the pandemic-era pivot to distance learning coupled with the proliferation of online learning platforms over the past five years. None of them produced anything close to the current level of polarization around AI. The poles in the public debate surrounding it (ban / embrace) often impede the likelihood of having fruitful conversations in schools. The most incendiary pieces on both sides, and the inevitable rounds of hype and dismissal, frequently crowd out reasonable attempts at nuance. As a result, most educators have found themselves caught in a crossfire they are remarkably ill-equipped to navigate. It’s no wonder academic leaders are so paralyzed and confused.

Which is why a recent letter from the President of the University of Chicago caught my attention. The most thoughtful passages read as if at least one institution is finally getting the AI moment right, or as right as the moment allows. I found myself wondering if there were any lessons other schools might learn from their approach.

The Power of Three Simple Words

On June 2, President Paul Alivisatos announced that the University of Chicago has partnered with Anthropic to roll out Claude Enterprise to faculty and staff in July, and to students before fall. The partnership headline got much of the attention (and blowback), but the letter itself is worth parsing carefully.

Alivisatos asks the community to be “skeptical, ethical, and ambitious when it comes to AI.” Those three words rarely appear together and almost certainly weren’t the result of an AI “rule of three” application. Each was carefully chosen.

The first is a reflexive stance to resist hype. The second recognizes that AI’s impact extends far beyond its immediate classroom uses. The third grants permission to use the technology in service of the school’s mission. Taken together, the triad offers a framework more schools could emulate. It cautiously captures the genuine excitement around the technology without surrendering to it, and it acknowledges the legitimate concerns without retreating into prohibition - a position shown to be untenable over the past three years.

What the Letter Actually Does

The letter does more than just take a clear stance, though it does that effectively. It also commits to a set of practical decisions. The newest versions of the tools will be made available with university data protection which means employees currently using private accounts no longer need to. Extending Enterprise to all employees - faculty, staff, and operations - rather than limiting it to academic use is a structural commitment few institutions have made publicly, an implicit recognition that frontier-tier access matters across university infrastructure, not just in the classroom.1

Crucially, the letter grounds AI engagement in what Alivisatos calls the “habits of mind and standards of judgment that define this university.” AI integration is positioned not as a departure from UChicago’s existing intellectual mission but as a continuation of it. The letter also explicitly rejects the “AI is always better” framing: “In some cases, it is clear that AI tools can be brought to bear with the desired results. In others, we will do better by retaining our previous approaches or even developing new policies to expressly protect against misuse.” This line should assure critics who fear AI is going to infuse every aspect of the student learning experience.

Speaking directly to students, Alivisatos writes that:

… the University has a duty of care to ensure that the education offered to you is responsive to these technological developments by teaching you how to think with machines, how to think without them, and how to think about them. (emphasis added).

That single sentence contains more practical wisdom than the densest AI policy memos.

Faculty resources linked in the letter are home-grown, curated, specific, and authentically the product of sustained faculty work. They represent the kind of investment of faculty time across multiple departments that does not materialize in a few months. Two new faculty committees - one on AI and academic life, chaired by William Hubbard of Law and Economics, and an advisory committee on AI and education, chaired by Emily Lynn Osborn of History - signal that institutional commitment is ongoing, not a one-off announcement.

The Process

Chicago’s position didn’t appear overnight. It is the visible output of a faculty process that has been running in the open since early 2025, when Provost Katherine Baicker convened an AI and Education Working Group. That group’s report gave the University its governing phrase: be skeptical, ethical, and ambitious. I haven’t been able to read the report itself (it sits behind a university login), but Alivisatos has described its thrust the same way in every public message I can find, from an October note to faculty through this letter.

The June announcement is the logical next step and culmination of that process. UChicago has run its own tool, PhoenixAI, a custom platform built on OpenAI’s models, available to faculty and students for over a year. Its Becker Friedman Institute has put Anthropic’s models in the hands of its economists. None of this was improvised. Most institutions cannot point to the same trajectory.

A Year Ago - CSU’s Announcement

The UChicago letter contrasts nicely with what happened at California State University last spring. A year ago, the CSU system signed the largest single-institution ChatGPT deployment in the world, a $17 million, 18-month deal with OpenAI rolled out across 22 campuses. CSU renewed in May 2026 for $13 million annually over three years. The New York Times Magazine recently characterized the result as “chaos.”

The UChicago decision, by contrast, emerged from a process the community could see coming. While clearly there is some disagreement within the UChicago community2 - no decision in the current climate around AI is immune - my admiration stems less from the decision3 itself than from the principles and process that produced it. The financial terms of the deal have not been disclosed, which is a significant criticism the partnership has invited. The framework, however, is public. A school community deserves open, defensible principles - and on that score, UChicago has demonstrated intellectual seriousness that suggests they can change course if the rollout ends up doing more harm than good.

No Easy Cases

When we study the Supreme Court in my Government and Politics class, I tell my students that no case that makes it to the Supreme Court is “easy.” Cases that survive the appeals process and are granted certiorari pose hard legal questions. If the decision were obvious, it wouldn’t be in front of the highest court.

The same logic applies to school leadership decisions on AI. One reason school leaders have been reluctant to take a strong stand is that the landscape is incredibly fraught. The technology is improving faster than anyone can follow, threatens the essence of what schools have been doing for decades, and allows students to complete work with little to no evidence of learning. It is forcing a logistical, pedagogical, and existential crisis on a scale schools have rarely had to handle, and it is not going away. At the same time, as Alivisatos notes, “faculty and researchers at all levels are using AI systems in astonishing and inspiring ways.” The juxtaposition of these two realities is what makes determining where AI might have a place on college campuses so difficult.

Alivisatos’s decision will appeal to some and upset others. That comes with the territory of being a school leader. Many schools will not arrive at the same place as UChicago. The relevant question is whether the decision was reached through reasoning that critics can actually engage with. In this case, it was. Critics need to specify exactly what was wrong with the reasoning, not just object to the outcome. Alivisatos’s language is deliberative and forward-looking at the same time, which is precisely the posture schools need to be in right now.

What Other Schools Can Learn From UChicago

Schools downstream of higher education - especially the K-12 institutions I work with directly - now have at least one model of what thoughtful engagement with the AI question looks like. Schools need principles, not just policies. Policies are rules; principles are how those rules adapt.

At the end of the day, any letter to the community about its AI principles is just words. But words matter when they are coupled with significant institutional decisions. The choice to partner with Anthropic (or OpenAI or Google, for that matter) is a major commitment. It’s incumbent on any school, not just UChicago, to follow up on its words with ongoing adjustments, calibrations, and actions across years, regardless of which direction they decide to move in. Many schools still have not chosen to do this kind of work. The few that do are starting to draft Phase II of the AI conversation in education.

Connect With Me

Beyond this newsletter, I work directly with schools, educators, and other institutions about pedagogical questions raised by AI. Take a look at my website and reach out - I’d love to hear what you’re working on.

One of the most unique things about teaching is that we exist within discrete units of time. Our year is the academic calendar, not the seasonal one, and each carries its own distinct themes, accomplishments, and struggles. 2025–2026 was a strange one for me – in some ways one of the more difficult of my thirty-one years, in others one of the most rewarding. I wrote a couple of weeks ago about a kind of AI exhaustion that had settled over me. It still lingers.

Still, maybe because I can practically reach out and taste summer right now, with the daily grind of classes receding, I'm already feeling the pull to return to the AI question in earnest (that didn't take long!). This summer I plan to approach it from three angles: the practical, the pedagogical, and, increasingly, the philosophical.1

The Practical: What’s Hype and What’s Real?

By practical I mean keeping up – not only with the models and the updates, though certainly those - but with the larger issues lurking in the background. I follow the news closely; I can’t help it, it’s my nature. During the school year it’s easy to get lost in the latest shiny thing, but summer is the chance to step back, survey the landscape, and also to play. Playing with AI has only gotten more interesting as the possibilities keep expanding.

Much of the AI discourse2 continues to traffic in sweeping claims that bear little connection to my reality on the ground, fueled by the news of the day and usually folded by each commentator into whatever they already believe. Little of it is grounded in what AI actually does - much of the pontificating marinates in abstraction.3 AI is not all one thing, and yet so much of the writing about it draws confident conclusions and makes definitive predictions that either avoid nuance or refuse to engage a single counterargument. This applies to all sides.

The other end of the AI discussion occupies a more granular and niche slice of the conversation - prompting guides, productivity hacks, and inside baseball secret AI strategies offering to give users an edge in … something. Many of these posts fixate on models, examples, jargon, and workflows. This kind of writing is not informative beyond its narrow purpose and simply assumes AI is mostly beneficial. The loudest of the AI hype crowd online are often these writers and their followers.

The two camps rarely talk: the skeptics don’t engage the boosters, and the boosters write off the skeptics as out of touch. Almost everyone is talking past each other. This has more or less been the case for the better part of two years.

My third way has been to simply move forward using my own experience as the best test case. I’ve seen the studies and try to read all viewpoints. I have some strong opinions but I reserve the right to change my mind, which I’ve done on more than one occasion. I have a list of tools I want to try this summer, mostly to see for myself what’s overhyped and what isn’t. I’ve had success building some simple static sites this year, but I know I’ve barely scratched the surface and I want to explore more.

This is a hobby, but AI has allowed me to do things I’ve wanted to do for years: my current goal is to architect full-fledged online academic courses based entirely on my own specifications, the kind of project that would not have been possible a few years ago. I’ve already started a total redesign of my Government and Politics course I’ll be teaching in the fall, inserting House and Senate trackers for the midterms, a navigable glossary of political terminology, and all eight major units embedded on a single site. I’m unsure whether any of it will justify the hours I’m going to invest, but I’ve never found a better way to learn something than to just do it. The beauty of AI is that if it’s not working or you don’t understand something, you can ask.

It’s also why I think a lot of the negative coverage around AI and education right now is aimed at the wrong target. Much of the commentary is still fixated on the student writing problem – who is using AI, how to catch it, or whether to allow it at all. That concern is legitimate, but I think it’s trailing the reality of where things are headed. Students outsourcing their writing won’t stop being a problem, and in many ways it’s already changed many students’ behavior. But using AI as a default search engine or a writing companion is not where the power is going to lie, and my hunch is that a year from now the use cases that matter will look more sophisticated than the ones we’re arguing about today. I’ll be curious if we’re still litigating the essay with the same intensity twelve months from now.

Pedagogy: Where Does AI Actually Fit?

This is the critical question that matters most for educators. There’s no question AI went through a negative cycle this year – backlashes to backlashes and visceral reactions to screens in general, especially as AI got more powerful and intertwined with everyday life. That makes the environment for talking about it in schools more fraught than ever, with contradictory and often incendiary information competing for attention at exactly the moment when the decision-makers need more clarity, not less.

In a year of conversations with school leaders, a pattern has emerged. More of them realize they need to take some kind of principled stance beyond just policies, because it’s clear that the issue is not going to disappear. But centering a school’s pedagogical questions on AI in particular, I’m increasingly convinced, is the wrong strategy. The schools that thrive moving forward will be the ones with a clear sense of mission and the flexibility to meet the pedagogical issues raised by AI as they continue to evolve. K-12 schools that go all in right now without careful planning, as well as schools that refuse to engage at all, will find themselves either reinventing the wheel or constantly playing catch-up.

So what does thoughtful discussion look like? I’m more and more of the view that it mostly looks towards practices that worked before ChatGPT launched – prioritizing student engagement, attention and time on task, scaffolded learning - the same things skilled teachers have done for decades. Teaching in the age of AI does not require a new kind of teaching - at least, inside secondary classrooms. Anyone who has stayed in this profession long enough is used to adapting to new environments. These techniques can be done with or without technology. Last August I wrote about my AI-Aware Strategy for the Year Ahead, and the honest truth is that centering AI as the main driver in the conversation was the wrong move. What I want this fall is simpler – not an AI-Aware Strategy, but simply an Effective Teaching Strategy, where AI just happens to be something we all have access to.

The Homework Question

The real AI disruption over the past two years is the impact it’s had on the work we assign outside the classroom, and that’s where serious rethinking needs to happen. Many teachers have already started. The uncomfortable truth is that the focus on “learning” – which should have been the priority all along – has drifted further and further from daily practice. That sounds counterintuitive, but for a lot of reasons we stopped making actual understanding the main goal of school a long time ago, and AI is now forcing that conversation at a higher level than we’ve had it in years. That should be a good thing in the long run.

A real and growing number of students are already functionally dependent on AI for much of their written work, and that is only going to grow. A second, likely much smaller cohort, will continue to deliberately avoid it – these are the ones who fell in love with reading and with the slow work of writing, either on their own or under the tutelage of an inspiring teacher. Ideally, we can maintain and even increase the size of this group in the coming years.

But the largest group by far is the middle: students who haven’t thought much about any of this, don’t especially care about the AI-and-writing angst, and are mostly trying to get through the day as best they can. They reach for AI in some moments and not others and don’t think very hard about it either way. In every one of these cases, AI has changed the calculus for how students approach a given task, and no AI detector is going to fix it. This is the group that needs the most attention.

None of that means we should stop asking students to do work outside of class. It does mean that any of that work, if we intend to assess it in a high-stakes way, carries an authenticity problem that is not going away. Harsh penalties may deter the worst offenders, but given the infinite number of ways a student can use AI in the context of an assignment, committing finite resources – in time, energy, and trust – primarily to enforcement feels like a losing proposition in the long run.

This is also why AI is both the cause of and a potential solution to the problem of out-of-class work – the cause, because students will keep outsourcing their thinking if we don’t re-examine the tasks we give them; a possible solution, because we can use AI to help us rethink those tasks. I wrote about this previously and I still believe it: in the absence of access to an expert curricular designer, AI can be used to help a thoughtful teacher analyze what may be working in their course and what isn’t. Running your own curriculum through that kind of audit is a solid place to start, and something I’ll do more of this summer.

That’s for the near-term. A more hopeful future, perhaps hard to envision in the current climate, is one where students come to see AI as something that can augment their own abilities rather than replace them, and where school teaches the core skills first, so that the tools are actually useful once students reach for them. I’m not sure how we get there, but as I watch another group of seniors head off to college, I want to believe that we can.

Student Saviors

What got me through the year, in the end, was the reason I’ve remained in the classroom for over three decades – the kids. Time and time again, my students saved me from myself. Real, genuine human connection is the heart and soul of any classroom. We learned this instinctively during the pandemic. Without hearing the enthusiasm in a student’s question, their frustration when they still don’t understand, and their joy when something actually clicks, none of this really matters. No chatbot can read those cues (despite what the new voice models are claiming). Even if they could, it’s clear that’s not what teenagers want.

My students this year were curious, hardworking, and genuinely wanted to learn. They asked tough questions and put in extraordinary effort, and it is clearer to me than ever that what they want most is a caring adult they respect at the front of the room. They prefer the kind of direct attention that only we can give them. My AI Club lunches, Saturday debate tournaments, and Ethics Bowl run to the finals were all highlights of my school year.

Where AI can help is in the hours we aren’t there – when students are guided and motivated to use it to deepen their understanding, apply new research skills, or get feedback on a topic beyond their teacher’s expertise, ideally after they’ve built the skills that let them use it well. The tyranny of assessment hovers over all of this, distorting and too often undermining the very learning it is meant to measure. In an ideal world we help students reach their full potential with these tools if they want them, once that foundation is in place. This is, of course, easier said than done.

What I don't think serves anyone is pretending AI hasn't already worked its way into so much of student life. Pretending the situation doesn’t exist, or trying to ban and/or punish our way out of it, only guarantees that students will use AI in the shadows. My message in the K-12 setting is to center the learning first, foregrounding student needs with the understanding that many of them are already turning to AI. A former colleague used to say that no technology tool was inherently good or bad - user intent coupled with the potential for positive learning outcomes made all the difference. It would be a major breakthrough if we reach the stage of discussing AI in school settings where those considerations drive the conversation.

Connect With Me

Beyond this newsletter, I work directly with schools, educators, and other institutions about pedagogical questions raised by AI. Take a look at my website and reach out - I’d love to hear what you’re working on.

Last Tuesday, Google held its annual I/O conference, and I’m one of those people who watch the entire keynote. I came away both overwhelmed and underwhelmed. The new models are genuinely impressive, with real agentic capability and a new fast-and-cheap model, Gemini 3.5 Flash, that does frontier-level work at a fraction of the cost. Charts sped by showing AI usage exploding across the Google ecosystem. The technology is real, and it is improving ahead of schedule. What frustrated me was not the technology. It was the way Google chose to show us what they want us to use it for.

A handful of lines also stood out, particularly from Sundar Pichai, Google’s CEO, and Demis Hassabis, the CEO of Google DeepMind and the company’s resident visionary. Pichai casually folded in two examples of how people are using AI: students studying for final exams, and his own use of it to “make sense of my parents’ doctor visits.” Later, Hassabis stated, essentially as fact, that we are just a “few years away” from AGI. I know these conferences are victory laps, with their obligatory pauses for applause, like a State of the Union address. Even so, Pichai reached for two of the most contested uses of AI in the culture right now - student work and medical diagnosis - and presented both as if the debate were over and decided in their favor. Hassabis has moved his AGI timeline up to the point where he’ll say it flatly, on a stage he knows the world is watching. Each of these lines struck me wrong. But it was the demos where they really lost me.

A year ago I wrote about a significant development most people missed that rolled out at the same conference, and we’ve all been living with the consequences ever since. AI Mode, built directly into the search bar, is now just part of the Google experience. This made AI unavoidable for the billions of people who use Google search, including our students. This year, Google is clearly doubling down on its AI infrastructure investment. To what end?

All That Intelligence, for This?

The demos, one after another, were mostly small in a way that’s oddly hard to describe. Gemini Spark, the company’s shiny new personal agent, was put to work organizing a block party. In Josh Woodward’s demo it tracked down RSVPs, updated a Google Sheet, drafted emails to the neighbors who hadn’t replied, and built a hype deck in Slides, all of it running in the background. A second presenter, Robby Stein, showed something different: generative UI in Search, where it built a custom app on the fly, in this case a weekend planner for his family, which he then shared with his wife for her seal of approval. A little later came the universal shopping cart that follows you across the web, and that was the clincher for me. All that money, all that intelligence, and it was mostly pitched as a better way to throw a barbecue and a faster way to check out after an online shopping spree.

The promise of all this is that AI does things for you. The demos kept showing me how AI could build things, even directly within the search box.1 I laughed at the weekend planner, because I know exactly what would happen if I built an app to plan our weekend and sent it to my wife. She would probably throw her phone at me. Who actually wants this? When I use Google Maps to find a coffee shop, I want the coffee shop, not to spend time fiddling with a custom-built coffee-shop-finding dashboard I can come back to later.

Over and over, they handed me solutions to problems I don’t have. So much of it felt less like help and more like homework, an endless proliferation of apps and dashboards for things families have managed on their own for decades. Google’s bet is that making these will become so fast and so frictionless that we’ll reach for them without thinking. Maybe they’re right. I’m skeptical.2

It’s Shopping All the Way Down

The part that left me most cynical is how obviously Google wants us to shop more. People already shop across Google more than a billion times a day, and the company intends to make that even more effortless. The new shopping agents work around the clock. Your cart follows you everywhere, hunts for deals while you sleep, and can be authorized to buy on your behalf with you out of the loop. One slide called it “shopping with superpowers.” The commerce segment ended on the words “happy shopping.” Then, just in case the message hadn’t landed, they scored the transition to Depeche Mode’s “Just Can’t Get Enough,” hitting Gen X squarely in the solar plexus.

I’ve no doubt some people may love this. Still, a discount on something you’d never have bought without a shopping agent doesn’t count as “savings.” It’s just a more efficient way to spend more. “Shopping with superpowers” is really a demand that we all shop with superhuman discipline, which is not a thing most of us are good at. We’ve already built a world where we can want something at eleven at night and have it on the doorstep the next day. That last layer of friction, between the impulse and actually clicking Buy, was arguably the one thing protecting some of us from ourselves, and Google’s plan is to remove even that. In a spring when gas is closing in on five dollars a gallon, inflation is climbing, and consumer confidence just hit an all-time low, do young people, and the rest of us for that matter, really need to be trained to turn every passing whim into a transaction?

Glasses That Can Whisper the Answers

Near the end came the demo I’d been dreading, and the one that should scare teachers most. Google’s first audio glasses, which speak information privately into your ear, arrive this fall; the display glasses, which show it on a lens in front of your eye, are still in limited testing. The demo was kept deliberately mundane, because the general creepiness of AI headgear is the part Google would rather not dwell on. Shahram Izadi, Google’s XR lead, and Nishtha Bhatia walked through ordering a cold brew through the frames, summarizing muted texts, and turning the crowd into a cartoon on her watch. Cool, I suppose, but do consumers really need this?

For a teacher, a device built to feed you information invisibly, whispered in your ear or shown on a lens only you can see, is an assessment-integrity problem on a scale we haven't faced, and it is not waiting for Google's fall launch. You can already buy prescription glasses with AI built in. Meta has sold millions, and a student in my class had a pair last year. Can we ban prescription AI glasses? The blue book and the in-class exam, the defenses we've fallen back on this year, do not survive glasses that give students answers in private. Beyond cheating, the glasses are just the next layer of AI envelopment Google keeps working on to make it all feel inevitable, arriving at a moment when the backlash is real and a vocal public is asking everyone to slow down. The whole keynote felt tone-deaf to it.

The Argument They Skipped

The debate about AI and education is the one the keynote treated as a closed case. The references that did appear were all upbeat - Pichai's line about students prepping for final exams, and Josh Woodward's quick run through Gemini's student features, guided learning, practice tests, and a nod to NotebookLM.3 The closest the keynote came to demonstrating actual learning was Robby Stein's astrophysics moment, where Search built a custom interactive visual explaining how black holes affect space-time. Even that was staged as an exciting feature of what students can do. Yet the question that keeps teachers up at night, whether these tools are actually helping students learn, never came up. Google ignored the most contested issue in my world and declared it settled while the rest of us are still figuring it out. It won the argument by declining to have it.

After the keynote, Dan Fitzpatrick (no relation), a highly sought-after voice in the AI-and-education world, sat down with Chris Phillips, the man who runs education at Google, and came away with a much more nuanced, teacher-centered case the keynote never bothered to make. His piece is worth reading in full.

Two of his takeaways landed, for different reasons. The first described one of the most impressive demos in which Antigravity 2.0, Google's revamped agentic coding platform, built a working operating system from scratch, mostly on its own, for under a thousand dollars. Building something that complex for that little has real ramifications for developers, but it is not likely to impact teachers any time soon. It is just another example of the gap between what the frontier can do and what the 2026 classroom is actually concerned with.

His larger point is that Gemini 3.5 Flash is now good enough to do almost everything schools are asking for, and the new price point will lower the cost barrier keeping AI out of districts willing to make the switch but unable to commit the resources. I suspect it will make AI in schools more likely, not less.

Though Dan’s piece might be interpreted as more bullish than I am on where AI in schools is headed, it did not offset my visceral and mostly negative reaction to the keynote itself.

Even though it’s clear from his reporting that Google has a thoughtful account of what its tools do for learning, it’s telling that it chose not to put that on the stage.

The Promise vs. the Product

The keynote opened on a sizzle reel of possibility - a young boy on a tablet doing something undefined but clearly interesting with AI, a teacher mid-music-lesson, a few small-business owners, over a voiceover about solving disease and “making something that matters.” There were legitimate use cases: a Korean immigrant who used these tools to build a hiring platform for her neighbors, and creative AI tools for musicians and filmmakers.4 It closed on Hassabis invoking the “foothills of the singularity.”

Hassabis is the AI paradox in one man. He came out early to announce that AGI is just a few years away, and he returned at the very end as the closer, touting AlphaFold, Isomorphic Labs chasing the goal of one day “solving all disease,” and WeatherNext, the forecasting model that called Hurricane Melissa’s path days early, in time to get people out of the way. These are all real ways AI has advanced scientific development. He promises a “new golden age of scientific discovery” that improves lives everywhere. The demo that ran just before that close was the cold brew, ordered through a pair of glasses. From a cold brew to the singularity in a single cut. Everything between his two appearances, the apps, the shopping, the glasses, was the version of AI its skeptics fear most. Which future is Google actually building?

To What End?

Pichai mentioned that Google expects to spend something like $180 to $190 billion on infrastructure this year, roughly six times what it spent in 2022. Spark, the advanced agentic tool covered in the keynote that works while your laptop is closed, requires a Google AI Ultra plan, which runs between $100 and $200 a month. What's free for everyone is the new search box, the on-the-fly generative results, and the cart. Google would tell you the cart is what pays for the moonshot. Maybe. AGI is held out as a tantalizing promise, but the cart is what turns a billion shopping searches a day into the money that pays for it.

There were genuine standouts - the aforementioned Antigravity build and Gemini Omni, the new video model, were both impressive. With Google Flow’s editing tools I could see both the fear and the possibility in it for anyone who fools around with photos, film, and music. We will likely absorb Spark, the in-search app builds, and the universal cart the same way we absorbed AI Overviews, without too much fanfare. Maybe I’m just grumpy, but a lot of it felt more like gimmicks than upgrades.

This all tells me that with agentic AI now baked into how we use Google, the fantasy of “banning” AI in schools should be over, if it was ever a serious position to begin with. Yet we still barely know whether, or how, to teach with these tools at all. For the first time, as someone usually curious and fascinated by this stuff, I’m feeling more AI fatigue than excitement. Maybe I am just suffering from AI exhaustion.

Still, what stuck with me most was the shopping. The direction Google is pointing, as best I can tell, is to get me to buy more, build disposable apps to help me do it, and surrender to a world where every experience is something to digitize, optimize, and monetize. AGI promised something bigger than this. Do I really need a customized shopping experience, or bespoke apps for using the web? And do I want a pair of glasses talking into my ear or digitally interpreting my world so I never have to engage with it? The 2026 keynote was a tale of two futures, and the one that feels likeliest after watching it is the one I want least.

Connect With Me

Beyond this newsletter, I work directly with schools, educators, and other institutions about pedagogical questions raised by AI. Take a look at my website and reach out - I’d love to hear what you’re working on.

It’s mid-May and there is light at the end of the tunnel. Our school year ends in three weeks. Instead of anticipation, what I feel is something closer to exhaustion. The exhaustion is partially due to an especially brutal workload, but it also comes from spending nine months watching the gap between what AI actually is and how schools engage with it grow wider, not narrower. I feel like the conversation itself is stuck.

Below I’ll share a few observations as we wind down the academic calendar. None are especially surprising or groundbreaking. Taken together, though, they suggest significantly more work ahead for teachers, students, and institutions. My key takeaway is that the 2025 - 2026 school year will be remembered as the one where AI became ambient, unavoidable, and still mostly unaddressed.

Ambient AI

At the risk of stating the obvious, AI is everywhere. It’s still mostly associated with ChatGPT, but as of May 2026, unless you live under a rock, almost everyone is aware that several major companies have powerful AI models. More importantly, regardless of whether you know the names, understand the difference between Claude Code and Codex, or have experimented with any agentic capabilities - everyone on the internet has encountered AI images, audio, and sophisticated AI-generated content.

This was not true a year ago. The deep reasoning models had only emerged at the end of 2024 and I recall shocking teachers with demonstrations of what they could do last spring. The speed with which add-ons, plug-ins, and upgrades have rolled out means we are well past the ability to shock.

The implication for educators is simple. Unless we are prepared to keep our students from accessing Google, they will always be able to use a powerful frontier AI model in the form of Gemini through AI mode. I’ve argued since last June that teachers who claim they “forbid” AI are engaging in semantic theatre once a student is on the internet - we all encounter AI overviews whether we like it or not. Google’s AI mode allows for the critical cognitive off-loading educators fear, with the ability to have an LLM write, analyze, and work with uploaded documents baked into search. Literally the same capabilities students can access through ChatGPT. They do not need paid models.

Any notion that AI will not have a significant impact on teaching and learning is pure nostalgia.1

Schools Are Finally Reacting, But The Gap Is Still Growing

The good news is that schools are finally taking AI seriously beyond writing policies. More administrators and district leaders are grappling with what’s actually happening rather than merely drafting documents that are increasingly opaque, difficult to enforce, and not designed to withstand the speed with which the technology is moving.

The bad news is there is no consensus on what the reaction should be. The recent trend is to zero in on devices themselves - a movement grounded in legitimate concerns but one that also conveniently feels like it “solves” the AI problem. If students don’t have their phones or access to laptops, there’s no technology to turn to. Essentially, it asks us to return to an idealized past where kids all wrote by hand and played outside. As a father of a 6th grader, I see the appeal. I really do.

But the majority of school districts already have technology so embedded in their infrastructure that an unraveling is not nearly as easy as it looks. Many are going in the opposite direction, having invested heavily in a future where AI is a significant part of their mission. With over 13,000 districts in the U.S., we will see almost every permutation along the technology and AI adoption spectrum. At least these experiments will give us more data and more opportunities to evaluate what works.2

The deeper problem is that even the conversations finally taking place are still mostly happening in the context of 2023 AI. Few schools that I know of are seriously engaging with where the models are now and where they are heading. Over the past year, AI has moved well beyond the text generation that prompted most school policies - it now builds functional applications, conducts multi-step research, and produces professional-quality documents and presentations, capabilities that most school conversations haven’t begun to address. And these are the paid frontier models - what the average teacher or administrator is actually encountering is likely already a generation behind.

I agree with those who argue that mastering current models shouldn’t be the goal of AI literacy - they will continue to evolve and what we use in May 2026 will not be the same as September 2026, let alone May 2030. But I depart from those who claim that learning AI is “easy” and that our responsibilities as educators do not include preparing students for the world they are actually entering.

The reason I disagree is the same reason I would have pushed back in the early 2000s when internet access arrived at scale. We had responsibilities then to demonstrate best practices, explain how to evaluate information, and model effective use. Some of those early digital literacy strategies didn’t hold up over time - and that will likely be the case for some of what we teach about AI today. But talking about AI in the abstract without showing students what these tools do, how they work, and what kinds of output they produce is not serving them well. Especially when we know almost all of them are using it outside the classroom.

The Big Questions Are Finally Landing

Perhaps the most meaningful development of the year is that AI has forced educators to confront questions about purpose. More people - even those most viscerally resistant to AI - are recognizing that these systems will likely be a significant part of our lives going forward. Skeptics are also wrestling with something harder to dismiss: that AI can organize, synthesize, and generate information in ways that people engaged in knowledge work find genuinely useful whether or not those opposed to it use AI themselves.

These capabilities are forcing hard, but necessary conversations. Not simply about cheating, which dominated the past three years, but about purpose. What are schools actually for when obtaining and presenting information - in virtually any format we require - is approaching zero cost? What does a research assignment mean when AI can search, synthesize, and organize information faster than the student writing it? These are existential questions, no longer the province of the futurists and technologists who have been raising them for years. They are finally landing in faculty meetings and department conversations - perhaps under duress, but landing nonetheless.

Maybe it was never about AI in the first place. It’s always been about the value of what we assign and what we ask students to do - questions we could avoid as long as the products students handed in were plausibly their own. It’s dawning on most of us, if we hadn’t realized it already, that the skills hardest to automate are the ones the humanities have always tried to teach: empathy, curiosity, the ability to ask good questions, and the patience to sit with ones that don't have clear answers. It's worth noting that many in the industry are arriving at the same conclusion.3

What It Cost Me This Year

I want to add one observation that is entirely anecdotal but may resonate with other teachers. I covered less content this year. The constant vigilance about AI, the reduced number of assignments I gave outside of class, the time spent on research projects completed under supervision rather than at home - it all added up. I simply could not cover as much material as I have in previous years.

I don’t know if this was specific to my circumstances or something more widespread.4 But it strikes me as one of the hidden costs of this transition that we are not yet accounting for. AI has added more monitoring, more in-class assessment, and more rethinking of assignments to a curriculum that was never conceived with it in mind. If schools don't change their approach, something has to give - and right now, for me, it's content. Maybe that’s not the worst thing when virtually unlimited access to information and the ability for a motivated student to learn on their own are higher than they’ve ever been. But Sal Khan himself recently acknowledged that Khanmigo was a 'non-event' for most students - they simply didn't use it.5 Students are constantly using AI, just not the way adults design it for them.

As I Head Into Summer …

2025 - 2026 is ending more or less where we started, but with even more uncertainty - and significantly more work ahead within our schools, with our students, our parents, and our colleagues, to catch up to the reality of what AI has done to the academic project. I can’t imagine 2026 - 2027 won’t bring more of the same.

I’m exhausted by it and I suspect many teachers are as well. But the questions won’t disappear just because we’re afraid of asking them. If anything, we’re just getting started.

Connect With Me

Beyond this newsletter, I work directly with schools, educators, and other institutions about pedagogical questions raised by AI. Take a look at my website and reach out - I’d love to hear what you’re working on.

In March of 2020, millions of teachers and students across the country pivoted online for distance learning because of the pandemic. Districts that did not have 1:1 laptop programs swiftly invested in Chromebooks for every child. Despite the imperfect nature of the experience, we would not have been able to teach or connect with our students without those devices. Chromebooks became a lifeline between many students and school, particularly those in the least resourced communities. Yet, even for schools with 1:1 devices before 2020, most never carefully designed pedagogy around providing every child with one. Recent cell phone bans and the rise of generative AI have created a perfect storm for an anti-screen movement. The impulse is both understandable and rooted in legitimate concern. My fear is that the same lack of forethought that led to bringing laptops into the classroom in the first place will result in an overreactive push to eliminate them at exactly the moment students most need to learn how to navigate an AI-infused digital landscape.

Recent mainstream media articles report an accelerating trend.1 Parents and policy makers are demanding technology rollbacks in Los Angeles, New York, and elsewhere. Some middle schools have limited student access to Chromebooks. Others are trying to get rid of them altogether. The reporting describes negative reactions to the prevalence of screens in classrooms, with AI as a visceral catalyst that has helped transform long-standing discomfort into a national movement. The backlash is both understandable and real, and the responses range widely - from sensible restrictions on where, how, and for whom the devices are used, to outright calls for removal.

Bans are not the same thing as reforms - the distinction matters.

Any teacher who has spent time in a classroom over the past decade knows that screens can be the death of attention. I am sympathetic to the occasional urge to remove them. Yet, for lessons and activities designed well, digital devices that connect to the internet are one of the most powerful tools ever devised. Everyone reading this piece is either using a cell phone, a tablet, or a computer. Internet access is a necessity to participate in modern life.

The clash of these two realities - the difficulty of maintaining attention in the presence of a phone or computer that can deliver and create unlimited content - is at the heart of the conflict. At what age and in what circumstances can students be expected to responsibly use a device? How can teachers leverage intentional digital instruction and what can schools do to best ensure those devices are used appropriately? What does it mean when every child has their own device in the current paradigm, and what would it mean if we suddenly took these away?

The fundamental question around the issue is: Are the devices inherently the problem, or is the fact that we never developed real pedagogy around them - despite over a decade of trying - the real culprit? Some in the screen-removal movement increasingly claim it’s the laptops themselves. I've seen Chromebooks and classroom technology work for kids. Like almost everything when it comes to teaching, the results are context and classroom dependent.

Chromebooks were issued almost as a wartime emergency measure in 2020 and 2021. The devices have subsequently let in, among other things, first YouTube and now AI as a Trojan Horse, causing many of the problems outlined in the articles. Rather than ditching devices entirely, a better strategy is to pinpoint where the technology is doing more harm than good. Pulling the football away now, after a decade of telling kids and teachers digital and media literacy is essential, signals one thing above all: the adults have no idea what they are doing.

What the Movement Gets Right

Most of the horror stories in the recent coverage focus on the K-8 case, where the concern is clearly warranted. Before the pandemic, only about forty percent of districts had given devices to every elementary student. By March 2021, that number had jumped to eighty-four percent.2 The pandemic's distinctive contribution was pushing screens down to the youngest students at scale, before anyone had figured out what those devices were supposed to be doing in elementary classrooms. Putting screens in the hands of seven and eight year olds as a primary means of learning is not pedagogically defensible (though I suspect most elementary school teachers would chafe at the characterization of what they are doing). Nevertheless, the bombshells in the reporting - a seventh-grader in Wichita accessing more than 13,000 YouTube videos over three months on his school-issued account, and a Los Angeles 4th grade classroom where an AI art assignment produced sexualized images of a children's book character - are damning evidence that issuing 1:1 devices to younger students is walking a tightrope at best. It doesn’t take a neuroscientist to support eliminating or, at the very least, significantly restricting screen time for kids still learning how to read.

The Larger Failure We’ve Yet to Grapple With

The stories about Chromebook misuse and unlimited access to YouTube treat the screens themselves as the problem. A more nuanced view is that most districts that issued devices never built the institutional capacity to manage them. Many schools that gave kids the keys to the kingdom in 2020 did not plan for what classroom use would look like after the emergency. For example, as the WSJ reported, internal Google documents showed that by 2019 the company itself acknowledged the YouTube experience in schools was broken and that filters were trivially easy for students to bypass. Schools have known this for years. Most have done very little. But some have used a scalpel rather than a hammer: Watertown Public Schools in Massachusetts blocked YouTube districtwide in January. They did not eliminate Chromebooks.

What the Reversal Would Actually Cost

Banning devices from older students as well as younger ones also does not realistically engage with what a full reversal would require. More than 88% of public schools now operate 1:1 device programs, according to government survey data cited by the Wall Street Journal. Schools have rebuilt their pedagogy around digital delivery for more than a decade. Teachers have rewritten units, redesigned assessments, and built classroom workflows that assume students can access shared documents, online texts, video resources, all layered on top of learning management systems.3 Standardized assessments are now digital. For many students, a school-issued device is their only means of completing their work in the upper grades. The infrastructure is not removable on a whim. It is replaceable, but not in a year, and not without enormous transitional cost. We rolled these devices out under emergency conditions in 2020 because there was no time to plan. Even though we have more time now, nothing suggests we are using it any better. Recent research on cell phone bans in the United States and on Australia's under-16 social media ban shows how hard it is for bans to deliver the outcomes their advocates promise.4

The K-8 Case for a Ban Is Not the 9-12 Case

The screen-removal movement is least defensible where it shifts from real concerns about K-8 into a blanket case for total bans. The districts at the forefront of the movement are pulling or scaling back 1:1 for elementary, and a few are now extending that to middle school. McPherson Middle School in Kansas collected back student Chromebooks in December and now keeps them in classroom carts, available for specific assignments. Bend, Oregon blocked YouTube for lower grades and is reviewing high school access where it remains the most-used app. Granville County, North Carolina is phasing out 1:1 for elementary while keeping it for upper grades. Even the most aggressive recent moves are almost all grade-specific.5 The rhetoric, however, is not making the developmental distinction explicit. More significantly, if we overcorrect and ban all devices without thinking through the consequences, re-introducing them later will be that much harder once the political moment passes. Better to do the slower work of assessing what we have than to make rash decisions that cannot easily be undone.

Chromebooks for second graders are entirely different from high school seniors learning to research with AI in preparation for college. Younger students still primarily need handwriting practice and tactile, deliberate work with physical tools in the classroom. Twelfth graders are about to enter an environment in which AI is already embedded across every common platform, and most of their academic experience will be mediated through devices. These are not the same question. A screen-removal movement that treats them as one risks landing somewhere that will likely need revisiting within a few years. Too wide a pendulum swing will compound the mistakes.

A More Thoughtful Path

Most of what is actually being reported and adopted is not a ban. It is a rollback. The districts pulling back are restricting YouTube, eliminating devices for the youngest students, scaling back from 1:1 to classroom carts, and developing screen time limits for higher grades. These are scaled-back approaches that require careful management but preserve the option to use the devices when it makes sense.

The same type of approach could be applied more broadly. Restrict use in lower grades. Conduct better and more rigorous research in middle and high school on device usage. Lock down YouTube and other sites of maximal distraction. Give parents real opt-outs. Tech-Free Tuesdays and Thursdays is a more elegant solution than immediate removal. The screen-use ratio should reverse over the course of an academic career - nearly 100% analog in lower grades, moving to more realistic usage by senior year of high school. As with so many debates, framing the issue as a binary - ban laptops or keep them - is what makes the conversation so contentious.

A Year Later: What’s the Message?

What message would laptop bans send to students? A year ago, the New York Times was profiling Miami-Dade for rolling out Google’s Gemini to 105,000 high school students. To my knowledge, the Times has not run a follow-up on how that experiment is going.6 Notably, the same reporter who profiled Miami-Dade's rollout, Natasha Singer, has since written two of the articles driving the backlash narrative - but not a follow-up on whether the largest AI deployment in American public schools delivered results.

In March, the Times also reported that NYC Public Schools, the largest district in the country, released formal guidance designating AI lesson planning, brainstorming, and drafting as “green” applications. Two of the nation’s largest districts, plus a growing number of others including Chicago, Denver, and Charlotte-Mecklenburg, are leaning in carefully. Now the mainstream media is running cover stories on the screen backlash, with at least one arguing for wholesale device removal. Across the country, many states are finally catching up by developing AI literacy plans while others are pursuing rollbacks and investigating outright device bans. These messages are more contradictory than complementary.

This is whiplash for teachers as much as for students. Many state legislatures, education experts, and consultants have spent the past two years building AI literacy frameworks and integration guidelines on the assumption that students would have access to devices. The screen-removal movement now wants to remove those devices. The signal to everyone is that, after fifteen years of use, we still have no consensus on what these tools are for, how to use them, or whether they belong in classrooms at all.7 Students are at the mercy of the adults in charge, and the adults in charge keep changing their minds.

How Can We Best Serve Our Students?

My fear is that education's response to AI will be to retreat rather than to engage. AI literacy taught in the abstract is not really AI literacy at all. That is not the future many states have spent the past two years building toward. That does not mean integrating AI into elementary classrooms. It does mean addressing AI head-on, which includes explaining, demonstrating, and recognizing where students are using it. That requires them to have access to devices at some stage of their academic career.

Older students, especially those in the years prior to college and university, need hands-on experience and guidance with teachers about both practical and ethical AI use. What that precisely looks like, how often and in what context, will be debated for the foreseeable future. To pull the rug out from under both students and teachers after almost a decade of training on digital content delivery would send a powerful message that we are moving backwards.

For some schools and communities, a flat out device ban in certain grades may best reflect their values. Claiming that 1:1 laptop programs have been the problem all along, however, when so many of our challenges in education pre-date the pandemic is to make the same reactive mistake over and over again. Students will encounter screens, devices, and AI outside of school regardless of what their schools decide. For students with limited home access, school may be the only place they will encounter these tools under adult guidance. Removing the devices does not remove the technology from their lives. It just shifts where, when, and how they encounter it, and which students get any meaningful instruction at all.

The K-8 screen reckoning is overdue, and if AI is the reason it finally arrived, thoughtfully considered rollbacks happening now are largely the right response. But banning screens at every grade level, in reaction to a technology that has barely had time to be understood, would throw away almost fifteen years of pedagogical infrastructure to fix a problem most schools have not yet seriously tried to manage. Students need to learn to master the machines at some point in their education. When and how to do that in the face of emerging and powerful technologies will be an enduring question. Let schools and teachers make these decisions based on their own experiences, not in response to a reactive and contradictory national conversation simultaneously demanding AI literacy while calling for the removal of the devices students would need to develop it. We need to get this right.

I am especially interested in hearing from anyone making the case for high school bans. Tell me what I am missing.

Connect With Me

Beyond this newsletter, I work directly with schools, educators, and other institutions about pedagogical questions raised by AI. Take a look at my website and reach out - I’d love to hear what you’re working on.

A slew of frontier model updates and new capabilities have recently dropped in quick succession.1 One, Claude Design, got lost in the shuffle. While playing around with it over the past week, something became clear. Now that code generation rather than text is the leading edge of AI capability, it comes with a cost. AI is getting more complicated to use. Vibe coding was a thing a year ago2 - non-coders using natural language to make AI write code for them. What’s emerging now is something new: native interfaces specifically tailored to create things - a document, a presentation, an app, a web tool. Most people are not taking advantage. The barrier to entry to really leverage what the most advanced AIs are capable of now requires thinking like a coder.3

Unfortunately, I’ve never been a coder. Most teachers reading this probably aren’t either. An entirely new vocabulary, one I've been learning mostly through newsletters geared toward entrepreneurs, is leaking into everyday conversation: APIs, tokens, context windows, agents, MCPs, harnesses, CLIs. You can’t read deeply in the AI space now without encountering hyperventilating claims about new workflows that produce all sorts of new digital artifacts and prototypes. Software engineers, marketers, and designers are rapidly absorbing these new tools into their jobs. That’s the hype. But hype doesn’t mean things aren’t actually changing. My experience is ahead of most teachers', but the wall is real - and humanities teachers will hit it hardest.

For most of my professional life I knew, in some abstract way, that everything I typed and clicked online was built by someone in a backroom who knew how to make it work. That was the deal humanities educators made with the digital age. We taught our students how to read critically, write cogently, and think deeply, all while seamlessly using digital tools - the internet, computers, and software. The people who knew the secret language of the machines handled the machines. All that happened somewhere else.

That deal is changing.

The original promise of generative AI was democratization through natural language. Anyone able to type can access expertise. That was true for the first wave of chatbots and it’s still true today. A teacher can quickly draft a rubric or a lesson plan. Someone who knows nothing about auto mechanics can teach themselves rudimentary maintenance tasks. AI quickly became an interactive YouTube tutorial. The friction between having a question and getting a coherent answer collapsed almost overnight.

But the simple chatbot is now the floor. The most powerful uses of AI sit several levels above where most people are currently working, which means we are drifting back toward a real expertise gap. To build something really useful with Claude Design - or Cowork, or Codex, or any of the platforms sprouting up everywhere - you have to think like someone who knows what a digital artifact really is, when to deploy versus refine, how to prompt at a granular level of specificity, and when to ask for code rather than revision. Hardest of all: how to recognize when the AI is offering you a design choice instead of a final product. A mistaken entry has consequences - in token use, in time spent waiting for an output, and in the rate limits you'll hit faster than you expect.

You don’t have to write any code. You do have to think the way coders think, make the kinds of decisions they make, and understand the language they use. A new threshold needs to be crossed.

Models, Apps, and Harnesses

If you want to see the issue up close, read Ethan Mollick’s latest post on GPT-5.5. Mollick is one of the most accessible writers about AI and he works hard to translate for non-technical readers. But even his current framing includes assumptions about what general readers are tracking. If you haven’t kept up, his posts are liable to be overwhelming.

He encourages us to think about AI as three interlinked concepts: models, apps, and harnesses. Models are things like Opus 4.7 and GPT-5.5. Apps are the products that let models do real work - claude.ai, chatgpt.com, Claude Code, and Codex. Harnesses are the tools the AI uses and how it is hooked up to them. That last one is probably the deal breaker for most teachers. How many educators do you think are linking APIs to GitHub? Setting up MCP servers? Are you still with me? This is where the real power resides for serious AI users, and it is currently beyond the reach and expertise of most educators.

Things are moving so fast that even the most vigilant cannot keep up. That is becoming more true by the week. A humanities teacher reading the GPT-5.5 piece is not reading about whether students can cheat on an essay. Mollick describes generating a near-PhD-quality academic paper from four prompts using Codex and an interactive 3D simulation of an evolving town across 6,000 years from a single prompt. These are now ordinary outputs from someone who knows how to integrate models, apps, and harnesses into a repeatable workflow.

Even Mollick has acknowledged this gap. In a post from late March, writing about the most powerful tools currently available, he admitted:

These tools are terrific, but they are really built for programmers. They assume you know Python and Git. Their interfaces look like a 1980s computer lab. For the 99% of knowledge workers who are not developers, these powerful AI tools are not optimized for them.

Claude Dispatch and the Power of Interfaces

Tools like Claude Design are the industry's attempt to fix this. The capability is there but what is new is the scaffolding meant to let non-technical users reach it. The catch, based on my experience, is that the new scaffolding is itself technical. The interface is better, but the conceptual demands are still out of reach for most. Maybe that will change, but right now it’s an impediment.

Hitting A Wall

Last December I appeared on the leaderboard of a brand new platform called AI Cred4, and the founder’s follow-up post name checked me as one of the surprises - a 30-year history teacher with no coding background showing up alongside engineers and consultants. In February I wrote that Claude Cowork “just worked” for me, and I meant it. Building online tools took some figuring out, but I did it. I suspect I'll get there eventually with Claude Design, but I'm finding myself out of my depth more often than I once was.

I need to reread every sentence twice. I’m having flashbacks to my days fumbling through FORTRAN and Pascal in the 80s. The skills that got me on that leaderboard last fall haven’t gone away. The problem is everything that’s piled on top of them since. Keeping up with the models and new releases is practically a full-time job.

The tantalizing thing is that I do see the value. I understand why these tools are a breakthrough and eventually worth learning. But taking the time to master them is an opportunity cost I can’t currently spend. It all makes me nervous about what comes next.

Last week I asked Claude Design to revise a one-page professional summary I’ve used for years. The draft came back elegant and the formatting impeccable. I was inserting updates from the last few years and needed to know how best to export the file. It recommended a standalone HTML file - not PDF and not Word. Then it offered an unsolicited note: “Start a new chat to save 107k tokens of context. This uses your rate limits more effectively.”

I knew what “standalone HTML” meant because of the Cowork projects I've done. I also knew that saving “107k tokens of context” was a good thing. A teacher who recently upgraded to Claude Pro to try Claude Design might not have known what either phrase meant.

It is not that learning these capabilities is hard, exactly. You can ask the AI when you get stuck and it will generally walk you through it. What the companies are doing, however, is putting the newer models out of reach for all but the most dedicated users with the resources and time necessary to devote to it. It’s understandable why companies will invest in this. It’s not yet clear why schools should.

The Eternal Skills Question

The best argument against using AI in classrooms goes like this. Teach students the eternal skills - writing, judgment, critical thinking, the ability to refine ideas - and AI fluency will follow. Kids need to know how to think before ever touching a chatbot. Concrete elements of AI instruction, like prompting, can be learned later. I tend to agree with all of this in the abstract. The strongest version of the argument is that schools exist to build durable human capacities, not to chase whatever the workforce happens to need this year.

But the eternal skills argument assumes students will not be using AI during that time. That is impossible unless we lock them in a room without technology. Some parents would willingly make that tradeoff. Most schools cannot.

It also assumes the AI students encounter at twenty-three will resemble the AI they are using at fifteen. It won’t. Students graduating this May and June will matriculate to college and eventually workplaces where the entry-level fluency expectation is already past the chatbot tier. The AI of 2034 will be unrecognizable to them, and the judgment required to use it well is not something that can be snapped on at the end of college. AI is the first tool that talks back in any meaningful sense - it argues, flatters, redirects, and sometimes deceives. Nothing in our experience tells us how long it takes to develop fluency with something like that. Withholding AI from students until they’re “ready” assumes a static technology which currently doesn’t exist.

I'm not convinced the eternal skills will be enough on their own, or at least, they are going to have to be developed alongside the reality of student use. AI literacy is on track to become a baseline professional expectation and the cost of being late is likely to compound quickly.

Class of 2032

My middle-school daughter graduates high school in 2032. Even if you accept the argument that high school is not workplace training, not exposing her to AI and what it is capable of during her high school years feels like an abdication of our educational responsibilities. We know they're using it already - every conversation I've had with actual students confirms it. Teachers cannot use AI themselves while refusing to explain it to them. The question no one has been able to answer yet is how to do that well. Can we learn this with them rather than ahead of them? The challenge is how to teach a tool we’re still figuring out ourselves.

This is uncomfortable territory because it implies that the humanities teachers leaning into the AI conversation in schools - and I am one of them - need to learn things we did not sign up to learn. Not exactly how to code, but how to adopt the mindset and understand what computer code is. We need to recognize when a tool should be used as an artifact builder rather than a chatbot and, most importantly, when it should not be used at all. Even more daunting is we may need to adapt the vocabulary of the software engineer to the vocabulary of the classroom.

The Home Stretch

As we head into the home stretch of the school year, I have been reflecting on what has changed for me over the past twelve months. “Everyone is Cheating Their Way Through College” was published on May 7th, 2025. That is old news now, and a tired take, though I do not know exactly how much has actually changed in classrooms. There may yet be a wave of success stories about blue books and oral exams. My experience has been mixed.

The real story for me is how I have moved from a purely text-based use of AI to one predicated on the ability to generate code to do things. Yes, it is partly the “agentic layer” that was promised last year and is now taking root. But the more important point is that it is going to require an entirely new set of skills for non-coders to do the kinds of things that come naturally to coders.

When I spent a summer as a paralegal, one of the partners explained to me that every profession has its own secret vocabulary, its own language that sets it apart. Generative AI gave us, briefly, the illusion that we could keep swimming solely in the world of words we understood. The truth is we have been living in someone else's domain all along. We just did not have to see it. Humanities teachers may have to learn to read in a language we did not train for, if we intend to keep up.

Connect With Me

Beyond this newsletter, I work directly with schools, educators, and other institutions about pedagogical questions raised by AI. Take a look at my website and reach out - I’d love to hear what you’re working on.

It's late April and I'm running on fumes. Students and teachers everywhere can see the finish line, but it's still far enough away to be more distraction than relief. Overextended and buried under a to-do list a mile long, if ever there was a moment when a time-saving tool would be critical, this is it. And yet AI is not that tool - at least not in the way it’s frequently pitched.

The availability of powerful generative AI makes it very easy to do things that probably should not be done at all. A core skill in the coming years will be determining when, why, and for what purpose AI should be used. Because, make no mistake, it will continue to be used by millions of people, and many of those people will be students and teachers.

Six Weeks of Time Savings? Really?

If there is one message that unites nearly every report, conference keynote, and vendor claim about AI in education, it is that AI can save teachers time. The January Brookings report on AI and students summarized the consensus:

AI’s benefits extend to teachers: by reducing time on tasks, AI allows teachers to focus on individualized student attention and enhance curriculum and instruction.

NPR’s coverage of the report cited a U.S. study claiming teachers who use AI save an average of nearly six hours a week and about six weeks over a full school year. That number deserves scrutiny.

The "U.S. study" in question is a Walton Family Foundation/Gallup survey from June 2025 - predating the Brookings report, which cites it. The methodology was key -teachers who already used AI weekly were asked to estimate, to the nearest half-hour, how much time AI saved them on individual tasks. Those self-reported estimates were then summed. The result was 5.9 hours per week or, cumulatively, six weeks per year.

I don't buy it. The Brookings report itself hedges the claim in a section titled "Time savings only occur if teacher workloads remain steady" (Box 05 on p. 40 of the full report). More importantly, neither the Brookings report nor the Walton study can specify what is actually being done with the time saved. As Tom Daccord noted on his Substack:

Even if AI is saving teachers time, the benefits only accrue if that time goes back into individualized attention and deeper instruction. AI time savings might simply mean that teachers pack up and go home earlier. It might mean the time is absorbed into more administrative and ancillary tasks. The difference matters because if one of the major selling points for teachers using AI is to “save time,” then it is imperative that the time saved is used wisely. But I question the entire narrative that AI is saving teachers time in the first place.

The Walton/Gallup survey's own data complicates the six-week headline. Look at the frequency chart. Eighty percent of teachers have never used AI for one-on-one tutoring. Fifty-nine percent have never used it to make assessments. Seventy-three percent have never used it to supplement instruction. The "six weeks" figure comes from frequent users - a group that represents, at most, a third of the teaching population. Which means, for most teachers, AI time savings remain entirely theoretical. It’s also worth asking - if the time savings is so dramatic, why aren’t more teachers using it?1

The majority of tasks for which teachers are using AI are either tasks they were doing already or, in the case of something like modifying materials to meet student needs, ones which AI enables in a way that wasn’t feasible before.

This exposes a subtler problem with how time savings get measured. Consider a teacher who uses AI to build an elaborate differentiated worksheet in 20 minutes. She estimates that creating it on her own would have taken an hour and reports 40 minutes saved. But without AI, that worksheet never gets made. The actual time impact is plus 20 minutes, not minus 40. In that instance, the survey measures savings against a hypothetical task that would never have been undertaken. Multiply that logic across nine task categories and you get 5.9 hours of savings that potentially exists only on paper. The worksheet may well be superior. That's a quality argument, and a potentially legitimate one. But it is not a time-savings argument.

What the Workplace Data Actually Shows

Regardless of the inconsistency of self-reports, there is data available that undercuts the notion that AI time savings in the workplace translate into actual efficiency gains at all. Recent studies measuring behavior rather than perception tell a different story.

The ActivTrak 2026 State of the Workplace report, released in March 2026, analyzed 443 million hours of behavioral data across 1,111 organizations and more than 163,000 employees. Their conclusion was blunt: “The data is unambiguous: AI does not reduce workloads.” Among users comparing 180 days before and after AI adoption, time spent across every measured work category increased - ranging from 27% to 346%. No category decreased. Meanwhile, the average focused, uninterrupted work session fell to just over 13 minutes, down 9%, and continuing a three-year downward trend. The optimal band of AI usage turned out to be extraordinarily narrow - employees spending 7-10% of their work hours in AI tools showed the highest productivity of any group, yet only 3% of workers fell within that range.

A separate BCG study of nearly 1,500 full-time U.S. workers, published in the Harvard Business Review, also in March 2026, identified a phenomenon they called “AI brain fry.” The primary driver wasn’t AI use itself but the cognitive burden of overseeing its output. Workers with high AI oversight loads reported 14% more mental effort, 12% greater mental fatigue, and 19% more information overload. Those experiencing the condition made 39% more major errors and were significantly more likely to consider leaving their jobs.

These are not education-specific studies. But I’m not sure why we would assume the findings would be significantly different for teachers. Evaluating what AI produces is mentally demanding work, and the overall pattern across industries is consistent: AI adds new layers of work rather than reducing existing ones.

The Novice Teacher Problem

Imagine you are new to teaching. You’ve completed your training and you are organizing your course materials for the first time. What lesson should you start with? How should you set up your classroom? An AI chatbot will happily provide answers to all of these questions and some might be generically helpful. But which ones are right for your students, in your room, given your strengths? You have no way to know.

The ability to navigate the hundreds of decisions that go into teaching comes with experience - the kind that requires following your own instincts, getting real-time feedback from actual students, and adjusting the next day to adapt to your circumstances.

LLMs are great at generating ideas. Many are creative and potentially useful. An experienced teacher can scan them quickly and locate the one or two that might lead to a productive tweak. Newer teachers don’t have that luxury. They haven’t made the hundreds of mistakes that allow them to grab the one useful suggestion among dozens.

This is a judgment problem, and chatbots cannot impart judgment.

The “brain fry” finding is particularly relevant here. The primary driver of cognitive strain wasn’t using AI itself but overseeing the output. And that presupposes a major assumption – that teachers are actually carefully reviewing what AI produces. As models become more advanced and more verbose, the cumulative effect of reading and evaluating AI-generated text can easily take longer than starting from scratch.

Veteran Teachers Have A Different Problem

Even for experienced teachers, the time-savings pitch doesn’t hold up, though for different reasons.

Over the past year, I’ve written about several ways I use AI in my own practice - building a Claude Skill to audit lessons using the Understanding by Design framework, and creating interactive web modules for specific activities in my classes. Each of these was a significant investment that I believe paid off (or will pay off) in better student outcomes. But none of them saved time.

The lesson audit produced useful, actionable feedback. It identified misalignment between what I wanted students to learn and what my activities actually assessed. But evaluating every suggestion required 30 years of teaching experience. It was helpful but not “efficient” – at least not in the way the Brookings report envisions “time savings for teachers.” Performing an AI audit for every lesson during the school year is simply not realistic. That’s useful summer work.

The interactive modules are, I think, genuinely effective teaching tools. They are also time-consuming to build and require significant design thinking before AI can execute anything usable. My abiding mantra when creating or redesigning a lesson has been: what is the value add, and is it worth doing? AI hasn't changed that question.

Even for teachers who aren’t building custom tools, AI is still unlikely to save them time. Generating a quiz is easy. Proofreading questions, verifying outputs, and ensuring the product meets your classroom standards is not. Creating new worksheets from AI-generated ideas may be rewarding and great for your students. But the measure of “time savings” is always against the alternative – using the original lesson. If AI leads you to do something different and do it well, it’s a worthy goal, ideally one that leads to better teaching and learning. It’s not a shortcut.

Depth, Not Speed

The argument I am making is not that AI cannot help teachers teach better. Used effectively, AI is exceptionally good at helping teachers improve their own work. The newer models and capabilities offer possibilities for teachers with the curiosity and - most critically - the time to learn about and play with them.

All of the best use cases require significant up-front investment. Using AI effectively may also increasingly require access to the top-tier frontier models - an entirely different issue.2 These two points cannot be sugarcoated. AI opens up new possibilities precisely because it enables work that wasn’t possible before - not because it makes existing work faster.

The ActivTrak data confirmed what I’ve found in practice. AI doesn’t replace what we’re already doing. It amplifies it. But the claim that it saves time the way a dishwasher saves time or a calculator saves time is not what I've experienced.

Which brings me back to Drucker. AI makes it efficient to do things that shouldn’t be done at all - churning out generic lesson plans no teacher has examined, producing feedback no student will read, and generating worksheets that exist because they’re easy to create rather than because they serve a clear learning goal. If that’s the primary way in which teachers are using AI, that’s the Drucker problem in a nutshell. Efficiency without judgment is worse than inefficiency.

Time is the most important resource a teacher has. Be wary of anyone who tells you AI will give you more of it. What AI can do - for teachers willing to go deeper - is open up choices that didn't exist before. Those choices mean more decisions, and every one costs time and attention.

I’d love to hear from other teachers about their experience with AI as a time-saving tool. Am I wrong? What am I missing? Let me know your thoughts in the comments.

Connect With Me

Beyond this newsletter, I work directly with schools, educators, and other institutions about pedagogical questions raised by AI. Take a look at my website and reach out - I’d love to hear what you’re working on.

Schools should screen the new AI Doc - teachers, students, and parents should all watch this film. Preferably together. The reason is not because the documentary is especially illuminating on education. It isn’t. The current controversial and pressing questions about AI and schools are not the focus. This movie is not about AI and writing or cognitive offloading.

The reason the film should be viewed is to provide common vocabulary for us to have conversations about a technology predicted to be the most transformative in history. More importantly, it serves as an excellent primer for viewers unfamiliar with the existential debates surrounding AI acceleration and gives them exposure to both the apocalyptic and messianic positions those closest to the technology hold. Citizens, especially students, cannot challenge positions they don't know exist.

I recently saw The AI Doc: Or How I Became An Apocaloptimist with a colleague. The documentary arrives at a critical juncture. I recommend the documentary to everyone because it details the range of extreme positions taken by AI’s creators, risk assessors, and others in the field who are setting the framework for the debates.

What the Film Does

The basic conceit of the film is that co-director Daniel Roher1, an artist and Oscar-winning documentary filmmaker who serves as a stand-in for the audience, and his wife Caroline (also a filmmaker), are expecting their first child. Both are smart and likable - but Daniel's anxiety grows the more and more he learns about AI. He decides to investigate and make a documentary.

Some of the most recognizable names in tech agreed to sit and be interviewed in his makeshift studio, including Sam Altman, Demis Hassabis, and Dario Amodei, the CEO of OpenAI, CEO and Co-Founder of Google DeepMind, and CEO of Anthropic, respectively. The reaction shots of Daniel as he listens to one extraordinary claim after another are priceless - first the doomers predicting catastrophic outcomes due to unregulated AI development followed by a parade of AI optimists who tell Daniel this is absolutely the best time ever to be born. The doomers talk about AI leading to the collapse of humanity (or, as Eliezer Yudkowsky politely interjects, “abrupt extermination”) while the optimists describe a utopia where no one has to work, cancer is cured, and AI ushers in a world of abundance such as we’ve never seen.

For viewers who are hearing tech folks speak this way for the first time, Daniel is an everyman who articulates what the audience is thinking. Why are we doing this if these horrific outcomes are possible? How much and who am I supposed to believe about any of this? He falls into despair. Mid-film, there is an amusing and creative discussion with his wife via a cartoon flip-page notebook in which she begs him to become more hopeful as she carries their first child to term.

To watch the film is to accept the premise that AI is going to be a very, very, big deal.

But the hyperbolic venting of all these people is the point. In the back half of the movie, Roher comes to the quite reasonable conclusion that most of the issues associated with these fantastical prognostications are coming from the founders and developers themselves.

Altman comes off the strangest here in my opinion, partially due to the incredibly long pauses and cryptic answers he gives to some of the most direct questions. When asked if there are protocols in place for what happens if one of his models goes rogue and starts to do tremendous damage he says yes, but fails to elaborate on what they are. The fact that he is more than aware and clearly planning for the possibility is disturbing enough.

Why It Matters for Schools

Students are understandably tired of only hearing about AI in the context of cheating. Teachers are tired of harping on it. Education will be dealing with the fallout of AI for years, but the conversation is going to expand and only get more divisive and confusing as the technology continues to improve. I’ve argued previously that schools need to engage the larger issues about AI and The AI:Doc offers precisely that entry point. I can easily envision assemblies, advisory discussions, and guest speakers brought in to elaborate and answer questions about issues which, while they are going to affect everyone, will have a disproportionate impact on students enrolled right now. The purpose is not to accept the views of the film’s participants as gospel but to serve as a jumping-off point: for additional readings, other interviews, and opportunities to hear missing pieces and critical commentary.

Students will be rattled by the film. Public viewings will spur useful conversations that rise above the simple context of cheating and cognitive offloading that currently swamps the debate about AI and education. If this documentary is any indication, those issues are going to quickly become supplanted by much larger ones and I think everyone, but especially the younger generation, should gain access to the different sides of the debates.

These are the actual conversations a very small number of people are having when it comes to AI. What The AI Doc does is put forward a series of arguments and positions in front of the audience who may not be inclined to realize exactly how much and why Silicon Valley is focused on AI to the exclusion of almost everything else. There are statements made in the film that give the sense that the people who are closest to AI development lie awake at night contemplating the end of humanity. Others insist we're standing at the threshold of unprecedented human flourishing. Those are radically different perspectives.

There is an amusing sequence as Roher does a series of Google searches investigating how to survive off the grid if things go kaput, basically modeling how and why some people are turning into “preppers” for the world’s collapse.

The primary argument of the documentary is what might happen if we end up getting AI wrong, even if it approaches only a fraction of what’s being predicted, and how the general public should respond. The issues are significant enough that politicians, policy makers, educators, and anyone with an interest in the future for their children, need to be aware of what the people building these models think is at stake.

Yes, the Middle Position is Missing

Critics of the film will rightly point out that the missing middle conversation is left out. Yann LeCun and Gary Marcus, both well-known skeptics of imminent AGI, are not featured (Emily Bender gets a single quote).2 There is no exploration of “AI as Normal Technology.” Exponential AI scaling is taken as a given and the assumed timelines for AGI by most of the speakers are on the aggressive end of the spectrum. But I actually think that’s a benefit. It will be instructive to look back in 5 years to see whether this film was prescient or overblown.

One participant has invoked similarities to The Day After and I would also liken it to An Inconvenient Truth. The AI:Doc is a wake-up call. The people with the most direct knowledge of where AI is headed are genuinely alarmed, and quite a few are begging for government regulation - that includes the ones building it, like Amodei.

The sooner more people learn about these predictions, investigate these claims, and learn for themselves what’s likely to be true and what isn’t, the better. If screening the AI:Doc in schools has that effect, it can’t come soon enough.

Connect With Me

Beyond this newsletter, I work directly with schools, educators, and other institutions about pedagogical questions raised by AI. Take a look at my website and reach out - I’d love to hear what you’re working on.

One of the best things about being a teacher is that work literally stops over break. Unlike other jobs, nobody needs someone to cover for you back at the office. I spent the better part of the past week away from a screen and it was exactly what I needed. It also showed me something I’ve known intuitively but hadn’t experienced directly - normal people are not following the AI news cycle with any kind of regularity. I am not normal.

So when I came back online this weekend, one story caught my eye. As of this posting, the story about Claude Mythos is a little more than 72 hours old. If you haven’t heard about it, join the club. While it will likely be lost in the deluge of the AI news cycle, it foreshadows an issue that will only become more relevant in the coming months - the staggering cost of running the most powerful AI models and who gets access to them.

How I Found the Story

My substack feed led me to Mythos through Carlo Iacono’s most recent post. It sparked my curiosity and gave me an opportunity to engage in the same kind of source analysis I model for students. How do you verify a story with so many moving parts and so little actual information to go on?

To find out, I turned to the SIFT framework, developed by digital literacy researcher Mike Caulfield.1 It stands for Stop, Investigate the source, Find better coverage, and Trace claims to their origin. Caulfield and Stanford researcher Sam Wineburg expanded on this approach in their book Verified.

What SIFT Looks Like in Practice

Here’s what I found when I traced the story back.

The original reporting came from Fortune’s Beatrice Nolan. Every hot take and every inflated claim on X flows downstream from those articles.2 What I would tell my students, however, is that even Fortune's reporting was based on documents independently discovered by two cybersecurity researchers - Roy Paz of LayerX Security and Alexandre Pauwels of the University of Cambridge - in a publicly searchable data store that Anthropic had failed to secure. Those draft blog posts and internal assets were the real primary source material. Anthropic locked down the data store after Fortune's inquiry, though by then cached copies had already begun circulating.3

Fortune’s reporting establishes the following: Anthropic has trained a new model currently called “Claude Mythos” (also referred to as Capybara), which is the next tier above its current flagship, Opus. The company confirmed it represents “a step change” in capabilities with “meaningful advances” in reasoning, coding, and cybersecurity.

The leaked draft is slightly more assertive than the public statement - it describes the model as “far ahead of any other AI model in cyber capabilities” and warns of “an upcoming wave of models that can exploit vulnerabilities in ways that far outpace the efforts of defenders.” Because of this risk, the planned rollout gives organizations early access to protect their systems before wider release.

The leak itself was caused by a content management system that made uploaded files public by default. Anthropic called it “human error” and said it was unrelated to any of its AI tools. It’s worth noting the irony that the company that has been warning about AI-powered cyberattacks lost control of its own documents through the kind of vulnerability its model is designed to detect.4

The article is based on internal draft “blog posts” which are essentially marketing copy written for a product launch. These are not internal memos or unfiltered emails. We're getting what Anthropic intended to say publicly, just before they were ready to say it. That's quite different from actually reading what they say privately and led to some commenters claiming the “leak” was intentional and simply a way to gin up hype. It wouldn’t be the first time a company engaged in such tactics, though Anthropic does not have that reputation.

That’s the gist of the verified story. Lots of people took this and ran with it.

Separating the Signal From the Noise

The Mythos story is a perfect example of a pattern all students and readers should know well by now. One verified story often generates a dozen unverified extrapolations. Within hours, new and unconfirmed details - ten trillion parameters, ten billion dollars to train - were circulating alongside claims that came directly from Fortune's reporting. One widely shared post mixed the two so seamlessly that a casual reader couldn't tell which claims were verified and which were invented.

That’s how it works - inferences, embellishments, and flat-out fabrications get shared more widely than the original reporting and errors get lost in the shuffle. Social media turbocharges the phenomenon and by the time most people encounter a story like this, the verified facts are indistinguishable from the noise.

The number one question students should ask when confronted with a claim that requires confirmation: can I trace it to a named source with direct access to the evidence? For the Mythos leak, the answer separates a surprisingly short list of confirmed facts from a much longer list of speculation.

One confirmed detail that matters most for educators was buried underneath the cybersecurity headlines. The model will be “very expensive for us to serve, and will be very expensive for our customers to use.”5

The Tiering Problem

Most students and teachers, if they are using AI at all, are using free tiers. Few people know which specific models power these free tiers, let alone how they differ from the most advanced versions. The default models on the free tiers of Claude, ChatGPT, and Gemini are all more powerful and multi-modal than what was available one year ago, and their performance is driving almost all of the AI and education conversation.

As the past few years have made clear, these base models are more than capable of disrupting the entire educational experience.

Yet these are not the models that make the boldest headlines. Each company now offers premium tiers - Claude Opus, ChatGPT Pro, Gemini Ultra - that are significantly more powerful and reserved for companies and individuals willing to pay top dollar for access.6 Meanwhile, the free tiers are tightening. Usage limits are getting stricter, resource-intensive features are being pulled back, and even paid subscribers are seeing throttled access during peak hours. The gap isn't just opening from the top - it's also closing from the bottom.7

What the Mythos story makes clear is that these frontier models are going to keep pulling away.

Opus, Anthropic’s current top-tier model, is already primarily an enterprise and power-user product. Mythos would add a tier above that and it’s explicitly described as too expensive for general release. The intense market competition among the three major AI developers - OpenAI, Anthropic, and Google - practically requires that the next model from each keep pace.

As AI models become even more powerful, they will be available first, and for an unknown period, only to organizations that can pay enterprise prices. Those organizations will not be public school districts. They'll be the European executives invited to preview unreleased Claude capabilities at an intimate retreat at an English country manor - a detail which was also in the leaked cache.

I wrote about this dynamic in February when the Moltbook story broke:

Mythos is Anthropic’s clear confirmation that’s where we are headed. For a while it seemed as if everyone was going to have similar access to the most advanced AI models available. By the end of 2026, I don’t think that’s going to be the case any longer.

The gap between what AI can actually do and what the average teacher or student has access to is about to get wider.

When we talk about AI in education, we tend to talk about “AI” as if it were one thing. As if the tool a student uses on the free tier of ChatGPT is the same as the one a top cybersecurity firm is using to audit its codebase. We talk interchangeably about the experience of a teacher using Gemini for the first time and the one a software developer has with Claude Cowork’s agentic process.

These are entirely different interactions with AI and it's one of the most overlooked dynamics in the education conversation. The tiering gap doesn't change what most teachers are dealing with right now - students can already do tremendous damage to their own learning with the free versions of these tools. We know this.

But when someone goes online and starts reading about what AI can and cannot do - as if each model is identical - it's clearer than ever why those conversations go sideways so often. Much of the commentary is based on premium models most users never experience, and the companies aren't exactly helping - OpenAI has quietly stripped model names from its interface, making the gap between tiers all but invisible.

The Same AI Experience? I Doubt It

It’s obviously too soon to tell whether Claude Mythos will be as consequential as Anthropic’s leaked promotional materials suggest. If the past is any indication, it will likely be another incremental improvement and garner attention mostly from within a small sphere of AI insiders. Anthropic has gotten more press recently, but it’s still a niche company. Most people you know have never heard of Mythos and probably never will. But if you’re someone who follows AI closely, it’s worth getting the facts straight. The newest and most powerful model priced at a level the average user cannot access means, if nothing else, that we are not all going to be having the same AI experience.

Connect With Me

Beyond this newsletter, I work directly with schools, educators, and other institutions about pedagogical questions raised by AI. Take a look at my website and reach out - I’d love to hear what you’re working on.

Back in December I sat down with a handful of HS students on a couple different occasions to talk about AI - their thoughts, attitudes, and descriptions of how students were using it in school. There were lots of observations - most unsurprising - but an overall confirmation that student AI usage had become a mainstream part of their academic lives. With the 2025 - 2026 school year more than half over and as we headed into a much needed spring break, I had an opportunity to sit down with about a dozen HS students in our AI club - 9th, 10th, and 11th graders - to revisit the conversation about AI and school.

One asked about the recent issue between Anthropic and the Pentagon.1 Another was willing to share how he uses AI as a study aid. And several talked about how AI was impacting the corporate world. On the whole, the recent conversation made something even clearer - the AI gap between teachers and students continues to grow despite more and more schools trying to catch up.

I know this is one group of kids at one school - members of an AI club, not exactly a representative sample, though one that notably included several girls whose voices have often been absent from this conversation.2 But I suspect the dynamic is recognizable to teachers well beyond our building.

These kids were thoughtful, specific, and honest. They had ideas, critiques, and an underlying message - please help us understand how AI fits or doesn’t fit into school because we are using it ALL. THE. TIME.

How They’re Actually Using It

I opened the meeting with a simple question: how are you and your peers using AI?

One student shared some screenshots with me of several ways he uses AI as a learning tool. Using Claude’s presentation feature, I stitched them together into a short deck, which he walked us through. He showed us interactive math diagrams with adjustable parameters, AI-generated quizzes with built-in explanations, flashcard sets with shuffle modes, and gamified study tools - all generated from very simple prompts.

Most educators (me included) wouldn’t characterize this as AI misuse. This student has figured out that AI can function as a personalized tutor, study partner, and test designer. When I first started writing about AI a little more than a year ago, few if any of these features were available. The AI issue has rapidly moved beyond mere text generation.

Another student described using AI for product research and hobby work:

“It’s really good at taking two products and comparing and contrasting them in a way that Google can’t. If I give it two things I’m looking at, it’ll give me pros and cons.”

When I asked whether he trusted the output, he didn’t hesitate:

“For any research I’m doing, I ask it to give me the main points of what I’m researching with citations, and then I go through those citations to see what’s actually critical.”

That was encouraging though I suspect it’s not the norm.

The “Neanderthal” Problem

One student offered an opinion with typical teenage bluntness:

I think for a lot of people my age, their understanding of AI is pretty Neanderthal. I think right now a lot of people just see it as a shortcut versus something you integrate within your daily life to improve your efficiency. There’s just so many more uses that people overlook - they see it as a very two-dimensional thing.

This obviously complicates the narrative that students are simply using AI to cheat. These students - granted, again, these are kids in an AI club - were drawing a clear line between those who use AI as a shortcut and those who use it as a tool - and they were frustrated that teachers, and even their own peers, couldn’t always see the difference.

Another student connected this to what awaits them in the future:

I think a lot of people skip over the fact that AI is being used way more in the workforce. I’ve heard a lot of people who work in technology and business - they’re starting to use AI models because it’s just a way more efficient way to get stuff done. I think that’s going to be really interesting to see the landscape of the next twenty years.

I told her I wasn’t sure it would take twenty. More like five.

What They Want Teachers to Know

I tried to shift the discussion from description to prescription. I asked the group directly: if you were a teacher right now, how would you handle AI in your classes?

One student referenced something one teacher had done - create a custom chatbot designed around specific assignment guidelines that students could use within defined parameters:

One thing I really liked is [when a teacher] made ... a chatbot thing that was specifically designed to follow the guidelines [they] set up. We could go into it, and it was limited. I think that's a good way to do it, because telling kids just the guidelines - like, [another teacher's] guideline for AI is just none, zero - I think that's very hard for students, because then it makes every single thing they do with AI feel very bad. And they’re still going to do it.” [emphasis added].

This is probably least surprising but also most difficult to swallow. Students will use AI whether we forbid it or not. That’s just the reality.

When I asked her to confirm - students are using AI regardless of the policy? - she didn’t hesitate:

“Yeah, definitely … I think teachers should be aware that kids are going to use AI. And I think I would want to create my own space that has guidelines and make sure all the students are in there, under my supervision, learning how to use it effectively. Because I think learning how to use it effectively is better than trying to make kids not use it.”

Another student made the case against prohibition even more directly:

“I think criminalizing AI, especially in the classroom, does way more harm than allowing it in moderation. If AI is just outright banned, it gains this association of being negative, like it’s cheating, like it’s bad. Whereas if students are taught to use AI in moderation - because they’re going to use it, they’re going to use it regardless - this way it teaches them to be conscious and aware of their AI usage and learn how to use it effectively.”

One more offered a practical recommendation:

“If I was a teacher, in any other class like English or creative arts, I would actually teach how to use AI to make a study guide for large blocks of information. It’s beneficial for memorization and studying key terms. But if it’s an in-class writing assignment, a skills check - you shouldn’t use AI just to get the work done faster. If it’s studying, it’s one thing. If it’s writing whole sentences for you, whole paragraphs, that’s another thing.”

These kids are proposing precisely what many commentators are advocating - context-dependent policies that distinguish between AI as a study aid versus AI as a substitute for thinking.

Pushing It Underground

A repeated observation popped up about the difference between AIs with guardrails and typical models found online. This is the problem Marc Watkins tackled in a recent post - what we might call the “free version” problem. A student who had experience with our AI wrapper remarked:

“I used Flint on my own time, and I have to say, compared to other models, it was incredibly frustrating because it’s designed the way teachers want you to use it. It wouldn’t give me direct answers - it would be like, ‘What do you think?’ It wasn’t that helpful in my opinion.”

Which is exactly what Flint and similar paid AI wrappers are supposed to do - push the student to think rather than hand them answers. Institutions are pouring money into these products. But the notion that kids will default to AIs deliberately designed to create “friction” when ample free versions exist that do not is more than unrealistic. It’s fantasy. When I asked how many had used or were even aware of the “study mode” drop-down selections baked into all the frontier models, they just stared at me.

Another student was even more direct about what happens when schools take a punitive approach:

“I think a big issue with how schools are dealing with AI right now is that so many kids are getting in trouble for it, and I don’t think it necessarily discourages the use of it. If anything, it just pushes it underground even more. I think making it into a learning movement - learning from mistakes and understanding the line of what’s okay and what’s not - rather than just punishing would be more effective.”

Obviously, these are teenagers. They didn’t catch the contradictions (introduce guardrails but we don’t want guardrails!) nor do they see the larger issues through the same lens as experienced teachers, almost all of whom learned their academic skills in a world where generative AI didn’t exist. These kids don’t know that world. They are trying to adapt in real time while many teachers are still assigning the same kinds of work that can now be done faster and better by AI than by high school students. That’s not their fault.

How Will We Close the Gap?

When students consistently said that teachers should be teaching them how to use AI effectively, I shared something I’ve observed in my conversations with educators across the country: many teachers don’t know how to use AI effectively. Quite a few are openly critical, many more are ambivalent at best, and those few who do feel comfortable enough to teach with it often find themselves navigating colleagues and administrators who are dealing with a hundred other priorities.

The result is that student use of AI is probably more experimental and varied than what most teachers are doing. Only a small fraction are particularly fluent with it. Many have serious reservations - and I understand why.

One student responded:

“I think a lot of teachers see AI as a bad thing because they don’t really know how to use it themselves. They only think of students plugging in a prompt and it just spitting out an answer. But I think if teachers develop this AI literacy - I think AI might see a lot more healthy use in classrooms than it’s seeing right now.”

He was getting at something I’ve written about before - the idea that teacher engagement with AI, even imperfect engagement, might shift student behavior more than any policy could:

“I think by teachers showing students how to navigate the ropes of AI, it creates an environment where students might respect the boundaries a bit more, yet still use AI in a way that’s beneficial to them but not overly harmful.”

I don’t know if he’s right about that, and I suspect many teachers would disagree. When I asked how many of their teachers had ever designed an AI-integrated lesson or even demonstrated what they consider appropriate AI use, the answer was almost none.

What I Showed Them

Towards the end of our club meeting, I shared some of the things I’ve been building (which I documented in my previous post) - an interactive pre-Civil War module to support a discussion-based activity, a Constitutional Convention simulation where students adopt delegate personas, and a data visualization of American war casualties over the centuries. I also showed them Claude’s newest visualization features and a history simulator built by Ben Breen, a college professor, to give them a sense of what other educators were experimenting with.

Nearly everything I build with AI stays within my subject area, and I frequently use my own materials - primary sources, discussion questions, lesson content - to help create additional documents and assignments. But the best way to understand the limits of AI is to test it on something you don’t know well.

When I suggested on the spot that I demo something in STEM (not my area of expertise!), a student suggested an interactive graphic of Bohr’s model.

I was honest:

“When I use AI, I tend to use it in subject matters where I know a lot, so I can get a sense of whether it’s way off. And for the most part, it’s really pretty good. So I’m going to extrapolate that this is still decent. But I don’t know - if I showed this to a physics teacher, he might be like, ‘Oh, this is totally wrong,’ and I would have no idea.”

The point wasn't to impress them. It was to model what using AI as a design tool looks like - and to be transparent about its limitations. I can build rich, interactive history modules because I provide the content and generally know when something is wrong. Expertise has to come first. AI is most powerful when the human in the loop actually knows what they're doing.

What’s Changed Since December

In my December conversations, students described, in part, a world of clandestine workarounds - split screens, retyping AI output by hand, and switching platforms when one gets banned. The vibe was deception and an acknowledgment of a cat and mouse game. How can I use it without getting caught?

This conversation was different. Not because these students aren’t using AI to get through some of their schoolwork. I’m not so naive as to think none of them do. But in the discussion on the day before break, they were willing to go further than that. They had more opinions about what responsible AI use looks like and specific proposals for what teachers should do differently, probably because I asked. They had critiques of existing tools and policies that were articulate and nuanced.

The fundamental dynamic still hasn’t changed with students still navigating AI largely on their own and minimal guidance from teachers. The gap between what students know about AI and what most educators can offer is also real.

One student observation from the December conversation still holds:

“A lot of adults have this idea of AI and all these things about AI, and they think they’re so right about it. So I think they should be more open to changing their views on how another demographic - like students - may use AI, and avoid the stigma around it.”

The Underlying Ask

What struck me most about this conversation wasn’t any single quote. It was the consistency of the comments. Across different students, different grade levels, and different levels of AI sophistication, they were all saying versions of the same thing: they want help understanding this new technology and how to use it for learning. Telling us not to use it and just punishing us when we do is not a long-term solution. Give us a space where we can be honest about what we’re doing without fear that honesty becomes a way to indict us.

That’s not an unfair ask.

Whether most schools can deliver on that right now - with the training gaps, philosophical objections, institutional inertia, and the sheer pace of technological change - is anyone’s guess, but I know more schools and more teachers are trying. Students will speak up if given the chance, and their voices still need to be heard.

The question is how many of us are listening.

Connect With Me

Beyond this newsletter, I work directly with schools, educators, and other institutions about pedagogical questions raised by AI. Take a look at my website and reach out - I’d love to hear what you’re working on.

One of my most cherished accomplishments in high school was constructing a small wooden box in what was commonly referred to as “shop” in the 1980s. A graduation requirement, the class was held in what today might be considered a maker space but at the time contained a table saw, hammers, and other carpentry equipment rather than 3-D printers. The sole assignment for the semester was to make something - in my case, as someone not especially inclined towards building things, I chose perhaps the simplest project imaginable - a box.

I had to measure the wood, cut the pieces, align the sides, affix the hinges, and sand and polish it until it gleamed. Aside from a research paper I wrote on Andrew Jackson, that box was one of the few assignments I still clearly remember. Unlike the paper, that physical box traveled with me from high school to college, two summers spent living in Japan, law school, and it even made it to my first real apartment, finally getting lost between moves sometime in the early 2000’s. I had that box for almost 20 years, storing change, receipts, and other miscellaneous detritus from daily life, and every time I looked at it I took pride in one of the few things I actually made with my own hands.

I’ve been thinking about that box recently. Not because of any sudden nostalgia for high school, but because I’ve started building things again - this time with AI. And the experience has reminded me that the act of making something, of designing and constructing something that didn’t exist before you willed it into being, engages a part of your brain that merely asking for information does not.

From Prompting to Building

In a recent post, I argued that educators should approach the new capabilities of agentic AI with imagination as well as caution. That piece was about what these tools can do. This one is about what kind of thinking they actually require - and why that thinking might reassure educators rather than alarm them. What I didn’t fully appreciate at the time was how much that thinking would resemble the work teachers already do every day.

The shift I’ve felt over the past several months isn’t entirely new, but one of degree. Agentic tools - AI that can build entire applications and websites rather than just generate text - have been available since at least the middle of 2025, but since the new year they've become significantly easier for non-coders to use. What’s changed for me is the focus: from generating content to designing experiences.

When I first started using AI seriously, I was doing what most teachers do - asking questions, iterating on responses, but primarily generating text - converting ideas into polished plans and getting feedback and analysis on my own work. AI remains useful for all of those tasks. But agentic AI introduces something fundamentally different. I can now craft an interactive learning experience - deciding what it should do, how it should be organized, who would use it, and what problem it needs to solve. That’s a much different - and more involved - cognitive activity.

Design Thinking

About a decade ago, our school brought in a consultant from IDEO - the design firm that essentially pioneered what’s now called “design thinking” - for a professional development workshop. I remember finding the session interesting: its emphasis on starting with user needs, prototyping repeatedly, and treating failure as information to act on rather than giving up in defeat. Like most PD workshops, it was engaging in the moment and then I went back to doing roughly the same things I’d always done. Every teacher knows this feeling.

But the notion of “design thinking” stuck with me and now seems more relevant than ever. A core insight - that how something is built and presented to its users matters as much as what it contains - turns out to be a helpful way to approach AI now that teachers have the tools at their fingertips that allow them to act on it.1

Design thinking is not an alien concept for teachers. Every unit you’ve ever planned, every set of learning objectives you’ve ever sequenced, every decision you’ve ever made about what to include and what to cut are design decisions. We’ve been doing this work our entire careers. But for most of us, unless you happen to have a degree in computer science, using technology in the classroom has been a mostly passive experience. The products and platforms we used were designed by someone else.

We worked inside whatever template Blackbaud or Canvas or Schoology provided and made the best of it. During the pandemic, many of us became more adept at leveraging our LMS through embedded videos, links to platforms like EdPuzzle or Desmos, all in service of organizing our materials online for student access. But we were constrained by the limitations of our school’s chosen product.

The difference now is that teachers can create their own bespoke LMS, limited only by their imagination. That is new and genuinely exciting.

What Design Might Look Like

For years, I’ve run a Constitutional Convention simulation in my U.S. history class. It’s always been a favorite - students are assigned state delegates in attendance at the 1787 Philadelphia gathering, debate the structure of the new government, and vote on the major compromises that shaped the Constitution. I've run this activity for more than 20 years and refined it considerably.

The materials I provide are detailed but sprawling. Students each have a delegate position sheet, primary and secondary source material, and a variety of other excerpts from digital and analog resources. Managing the various documents can be overwhelming, even when students get them ahead of time. This year’s convention went smoothly, but as any teacher knows, there is always room for improvement.

Curious about what was possible, and off the success of several earlier builds using Claude Cowork, I set out to create a single, self-contained online module that would house everything for next year.

Using a dedicated Claude Project, I worked through how to organize and display the material. I settled on a series of easily navigable tabs, each containing exactly what students needed at that moment. That decision required knowing how the activity actually flows in a classroom, which information students reach for and when, and where the bottlenecks have historically been.

I had to decide what belonged inside each section. The Delegate tab needed more than biographical information - it needed each delegate’s positions on every voting issue, organized so students could quickly find where their representative stood. But I also realized that understanding the Convention requires seeing how delegates diverged on the same questions - disagreements about government structure, determining representation, and the limits of executive power. During the design conversation, I described that specific pedagogical need, and Claude suggested a comparison feature where students could pull up two or three delegates side by side.

The comparison feature only existed because I articulated a teaching problem. That pattern repeated at every stage. Whether primary source documents should live inside the module or be linked out (when students click external links, the risk of distraction soars - if the source isn’t right there, it doesn’t get read). Whether my voting spreadsheet could be replaced with a live tracker built into the module itself (it could, and could be downloaded at the conclusion of each day’s session).

None of these decisions required coding knowledge. All of them required teaching knowledge. AI handled the execution while I managed the design. The result is a functioning interactive web application, and early feedback on the modules I’ve used this year suggests it will be an upgrade.

Two Kinds of AI Use

The design dimension of agentic AI changes the conversation in ways that haven't been fully appreciated. To see why, you have to distinguish between two fundamentally different kinds of AI use that are often treated as if they’re the same thing.

The way most students approach AI is one-shot or, at best, slightly iterative. They type a question to get an answer, ask for an essay draft, or request feedback on a problem. This is the cognitive offloading conversation we’ve been having for more than three years. The emerging research confirms what most teachers already suspect - when students use AI as a question-and-answer machine, the offloading is real and any learning gains, if they happen at all, are ephemeral.2 Students generate a product without doing the intellectual work that product is supposed to represent.

But designing with agentic AI is a fundamentally different activity. When a user sets out to build something - an interactive module, a web application, or some other tool to be digitally deployed - the thinking has to be front loaded or the result is useless. You can’t open an agentic tool and type “make me a Constitutional Convention website” and get anything worth sharing with students. You might get a generic approximation - perhaps not awful, but mostly another version of AI slop.

The tool needs design specifications that reflect real expertise: what the activity is for, how information should be organized, what the user needs at each stage, and what problem the whole thing is solving. Without that, the AI produces something that may look impressive but serves no one.

My experience over the past several months suggests that the cognitive offloading concern inverts when you shift from content generation to design. Design-based AI work is cognitively demanding in exactly the ways educators value - strategic planning, audience awareness, thoughtful information integration, and the ability to evaluate whether something actually works the way the user intended. The process of designing with these tools isn’t a shortcut around thinking. It’s a more complex kind of thinking than what most traditional assignments ask students to do.

Why Design Rewards Careful Thinking

There’s a practical dimension to this shift that I didn’t fully appreciate until I’d been building for a while.

When you’re chatting with an AI - asking questions, generating text, iterating on a response - the iteration is immediate and forgiving. If you don’t like the output, you try again. You can afford to think loosely because the cost of experimentation is essentially zero. This is what most users have been doing for the past several years.

Design-based work doesn't operate that way. When you're building something with multiple interconnected components, specifications need to be thought through carefully or the features don't fit. This includes asking whether what you're considering is worthy of an online module in the first place. Good questions matter as much as good answers, and careful follow-up matters even more. All of this needs to happen before you ask the model to start building.

You can iterate your way to a good result, but every cycle of build, review, and rebuild costs time and adds complexity. Wobbly thinking at the start means rework at every subsequent stage.

Anyone who has ever renovated a kitchen understands this principle intuitively. You can tear out a wall and start over. But nobody who has done an expensive home improvement project thinks that’s a good plan. You hire an architect to inspect the space and make those hard decisions carefully before you ever start demolition. The economics of construction - whether physical or digital - reward careful upfront thinking and punish sloppy work.

A Note on Students

I’ve been mostly focused on the teacher side of AI use for a reason. The overwhelming majority of students are not using AI this way - not because they aren’t capable, but because they either don’t know agentic tools exist or have no incentive to use them. We don’t typically assign students to build interactive modules, so the motivation to use agentic AI for cheating in this context just isn’t there.3

I'm actually less worried about student agentic AI use than I am about basic chatbot use. A student who could design and build a working module - who could think through the architecture, map the content to a user’s needs, and make deliberate decisions about what to include and how to organize it - would have demonstrated real intellectual work. If a student showed up in my class having built something like that, my reaction would not be to worry about their academic integrity. It would be to ask them to show me how they did it.

This could change. We may reach a point where a two-sentence prompt delivers something spectacular and the design work gets automated along with everything else. But right now, the cognitive demands of designing with agentic AI are creative and significant enough that I think it offers more of an opportunity than a threat.

The Box and the Module

Building an online teaching module with AI is clearly not the same as building a wooden box with your hands. There’s something about physical construction - the weight of the material and the permanence of the object - that represents tangible evidence you made a thing that exists in the world. Digital creation doesn't replicate it.

But the pride I felt in making that box came from the same place as what I feel when I look at the Convention module: I made something I didn’t know I could make. The box taught me that I could measure, cut, align, and finish a physical object despite having no particular aptitude for building things. The module taught me that I could architect, organize, sequence, and deploy a digital one.

AI is rapidly becoming a design medium. It extends what a thoughtful practitioner can do, in the same way that a table saw extended what a clumsy teenager could build in a shop class in 1985. The quality of what comes out still depends entirely on the quality of the thinking that goes in.

Will teachers be willing to think of themselves as designers? The skills required to do that well are the skills most good teachers have spent their careers developing. The only thing that’s new is what those skills can now produce.

Going Forward in 2026

2025 was supposed to be the year of agentic AI. In 2026, it’s actually arriving. The tools are doing many of the things their proponents suggested they would - AI is being integrated into more workflows, gaining the ability to handle longer and more complex tasks, and becoming increasingly accessible to people without technical backgrounds. That’s a significant development, and I’m trying to explain what it actually feels like from inside a classroom - not as a prediction about where things are heading, but as an account of what’s possible right now.

The current conversation about AI in education is still largely split between two worlds. In one, AI companies demo features to administrators and pitch adoption at scale. In the other, schools struggle with the logistics - procurement decisions, acceptable use policies, and faculty training that never quite keeps up. And that’s for schools actually engaging at all. What's been harder to find are concrete use cases - a teacher sitting down with these tools and building something for a specific group of students.

The gap between what I’m describing and where most teachers are is significant. The pedagogical design instinct transfers - most experienced teachers already think this way about their materials and activities. But the technical fluency required to translate that instinct into a built product is real, and bridging it requires sustained investment in time and development that most schools are not yet making. I’ve had the curiosity and the circumstances to develop that fluency. Most of my colleagues haven't. But if the trajectory of the models over the past six months is any indication, that gap will start closing fast.

Yes, the concerns about cheating, privacy, security, and whether AI will ultimately improve learning are real and must continue to be debated. I’ve written about many of them. But I’ve also been paying attention to what these tools actually require when you use them seriously, and what I've found is that using AI effectively is more demanding and more intellectually rigorous than most of the conversation assumes. If that's true, the question isn't whether we need better chatbots. It's whether we need better assignments. I think we will.

The window to shape how these tools get used in education remains open, and the people willing to experiment - not caught up in predicting the future or surrendering to a predetermined narrative, but actually building things for their students and sharing what works and what doesn't - are the ones who will figure it out first.

If design thinking was ever going to find its way into how teachers use AI, this is when it starts.

Connect With Me

Beyond this newsletter, I work directly with schools, educators, and other institutions about pedagogically questions raised by AI. Take a look at my website and reach out - I’d love to hear what you’re working on.

Recently, a friend called about a dilemma he was having involving AI. He’s been sponsoring student entries to a high school essay contest for years. This year, before submitting the essays, he ran them through an AI detector. The results came back with several showing “various levels of AI usage.” The contest rules were ambiguous on the use of AI - they'd included AI language the previous year but quietly stripped it out - but his school's position was not. With less than 24 hours to decide, he pulled the flagged entries.

Following that conversation, I started looking into how student writing competitions handle AI in their submission policies. What I found mirrors what's happening in classrooms across the country - inconsistent expectations, opaque enforcement, and almost no solid ground for a conscientious student or teacher to stand on.

But I also found something I didn't expect. One organization, operating under constraints more severe than what classroom teachers face, has built an approach that most schools haven't reached yet. And even the ones that haven't gotten there are instructive - their struggles to get the policy right look a lot like ours.

What the Research Revealed

I looked at almost a dozen major high school writing competitions and one academic journal submission policy (the Concord Review). These are national and international programs that attract ambitious student writers - the kind of opportunities that carry scholarships, prize money, and college application weight. My survey isn’t exhaustive, but I looked at enough to see the pattern.1

Among the organizations that address AI, the approaches are wildly different - each with their own specific language, sub-clauses, and links to FAQ documents about plagiarism.2 None are transparent about their methods of detection and enforcement. Many ban AI outright. Several allow limited use with vague attribution requirements. One of the most prestigious international contests acknowledges the role AI is playing in student work. Others say nothing at all.

The Four Approaches

I've chosen to focus on four that represent distinct philosophical orientations toward AI and student writing.

The Concord Review: Legacy Language in an AI World

The Concord Review is one I know best. I’ve had the privilege of working with students whose papers were accepted. It treats high school academic writing with the seriousness it deserves.

While not technically a competition (their FAQ explicitly states it’s “an international quarterly academic review for the history research papers of secondary students”), the Concord Review was founded in 1987 and publishes only about 5% of submissions. These are rigorous and substantial history papers, typically 5,000-10,000 words, including Turabian endnotes and extensive bibliographies.

Their submission guidelines include this language, bolded on the page:

You must be the sole author. If at any point during the review process we discover that your work is not an original paper, we will notify your school, which could result in withdrawal of college admissions, among other things.

The threat of school notification and an explicit reference to the “withdrawal of college admissions” is one I’ve not seen elsewhere. It’s also, I discovered, identical language dating back to at least August of 2015, seven years before the public version of ChatGPT debuted.3

In 2026, AI is mentioned nowhere on its website or its FAQ. Does a student who uses ChatGPT to stress-test her thesis or runs her draft through Grammarly for feedback on syntax invalidate the “sole author” requirement? She’d probably say no. Some teachers and professional writers might disagree. My interpretation is that the severity of the language implies a blanket AI ban, but they have not engaged that question explicitly. And the enforcement mechanism - notifying the school and jeopardizing college admissions - assumes a level of certainty about the violation that AI detection software simply cannot provide, if they even use one at all.

I’m surprised they have not updated their guidelines to reflect the present-day reality. Perhaps they believe that the depth of the work functions as a structural defense against the worst forms of AI misuse. After all, it’s much harder to fake expertise at that length and level of detail than in a 700-word essay.

But I simply don’t know. What I do know is that the published language gives the student no specific guidance about where AI falls in the integrity framework. If the goal is to make a student think twice about even going near AI, I imagine it is having the intended effect, but only they know for sure.

JFK Profile in Courage: Ban, No Questions Asked

The John F. Kennedy Presidential Library’s Profile in Courage Essay Contest takes the opposite approach, and it does it in a single sentence: “Essays may be checked for AI assistance and are disqualified if AI is detected at any point in the essay or bibliography,” though its Tips for Avoiding Plagiarism makes no mention of AI at all.

I've had students submit to this contest in past years, both prior to 2022. The prize is substantial - $10,000 for first place - and the prompt is unique: describe an act of political courage by a U.S. elected official.

The policy guideline, however, is worth unpacking. The “may be checked” language is hedged. It preserves the rule without committing to universal screening, which is likely impractical. But as is the case for every competition I looked at, there are no specifics on how AI is actually being detected. With what tool? And at what threshold?

The contest says “at any point in the essay or bibliography,” but there’s no distinction between a student who generated an entire essay with ChatGPT and a student who used it to locate sources or double-check their citation formatting. There’s no acknowledgment of the spectrum of AI use that exists. Like the Concord Review, the message the JFK contest is sending is clear - don’t touch AI at any part of the process when writing or researching this paper.

One important piece to note is that, unlike with the Concord Review, the JFK contest requires a nominating teacher. The teacher’s published role is to “review the essay for syntax, grammatical, typographical and spelling errors and ensure the essay meets the contest requirements” [emphasis added]. By mandating a teacher’s involvement, the contest has cleverly created a first line of defense. Because the teacher’s name is attached, the teacher’s sign-off implicitly extends to authenticity - even though the guidelines never say so directly.

The deeper issue is how the policy employs scare tactics to police the reality of student AI usage in 2026. Imagine if at some point in your process you asked ChatGPT whether an example you were considering qualifies as “political courage” under the contest’s definition. Does that count as “AI assistance”? It’s impossible to “catch” but may induce enough anxiety for honest students to avoid AI entirely while emboldening less scrupulous ones who understand that most of how students use AI today will evade detection.

John Locke Institute: A Strategic Retreat

The third competition I want to discuss is the John Locke Institute, an international contest I was previously unfamiliar with. It had the most sophisticated but somewhat contradictory AI language I found anywhere.

The John Locke Institute is a UK-based organization (offices in Oxford and Princeton) that runs what it describes as “the world’s most prestigious essay competition,” attracting tens of thousands of submissions from over 150 countries. It’s open to students under 19 and the essay prompts are intellectually ambitious. (One of this year’s questions in Science & Technology: “Should we be polite to ChatGPT?” - which is an amusing thing to ask in a competition that has complicated feelings about students using ChatGPT to help with their paper.)

The Locke essay competition shows an organization actively evolving on the AI question: in 2025, they enforced a strict ban, with detected use resulting in automatic disqualification. This year they’ve significantly changed their approach.

Their current policy reads:

Contestants are welcome to make use of large language models (LLMs) and other related tools in order to develop their thinking on the subject of their essays, and to stress test their arguments. Such online tools should be used for research support and as a thought partner, but certainly not as a substitute for the human author.

At first glance, that looks like the most encouraging language around AI usage I found. Then, the policy continues:

Bear in mind that the kind of essay that will be produced by AI will generally be inferior - and markedly less original - than an essay produced by a human author who is engaging creatively and critically with automated tools.

But scroll down the FAQs and the real message emerges:

Since any use of AI (that does not result in disqualification) can only negatively affect our assessment of your work relative to that of work that is done without using AI, your safest course of action is simply not to use it at all.

That’s a warning. The policy page says “welcome” while the FAQ says, to be on the safe side, “don’t.” What looked initially like an embrace of AI is closer to a strategic retreat - they appear to have concluded that an outright ban was unenforceable (their own 2025 experience may have demonstrated this) and shifted to a deterrence model: we understand how many of you might be tempted to use AI, but it probably won’t help you and likely will put you at a disadvantage.

To their credit, the Locke competition is still doing something no other policy I reviewed attempts. Their language treats students as rational actors who might be persuaded that their own thinking will produce stronger work than anything AI generates. Even if the underlying message is "don't use it," the method of delivery - argument rather than threat - is a meaningful departure from the JFK approach.

But the tension between the policy and the FAQ reveals that even an organization engaging with the complex issues around AI and writing can’t quite decide whether to welcome it or warn against it. They also require an academic referee - more explicitly than other competitions that merely ask for a sponsoring educator. Their guidelines mandate that students provide the name of a teacher familiar with the student’s work, whose email they will actually contact to verify originality.

They reserve the right to ask entrants to discuss their essay with a member of the Institute’s faculty. And the guidelines reference “the Institute’s own examination techniques, and the assessment tools we ourselves have developed” that are “carefully designed to recognize and reward original thought and expression.”

Like every other organization, however, they don’t specify what those assessment tools are or what they do. The language is confident but vague. The Locke Institute’s guidelines may be the most articulate of the four I examined, but there was still a mixed message. Their policy simultaneously permits, discourages, penalizes, and verifies. In some ways, it perfectly encapsulates the full spectrum of academic attitudes when it comes to AI in 2026.

National History Day: The Honest Outlier

National History Day represents something different from the other three: a policy built on pedagogy rather than enforcement.

NHD has been around since 1974, founded at Case Western Reserve University, growing into a program that reaches over half a million students annually in grades 6-12. Students submit not just papers but documentaries, exhibits, performances, and websites.

In January 2024, NHD published a guidance document (color coded with vivid examples - something that could be posted in a classroom) that does something almost no other competition - and, frankly, very few schools - has been willing to do. They drew a clear, specific line between process uses of AI (brainstorming topics, generating research keywords, checking grammar, simplifying difficult source language) and product uses (having AI write text, create visuals, or generate analysis). Students can do the former. They cannot do the latter. If they use AI at all, they must cite it in their annotated bibliography as a secondary source and explain how they used it in their process paper.

NHD is the only contest I reviewed that requires every entry, in every category, to include a “process paper” - a separate 500-word document in which students explain how they chose their topic, conducted their research, and created their project. It’s been a core requirement for years, long before AI entered the picture. So when NHD needed an AI disclosure mechanism, they didn’t have to invent new compliance guidelines. A student who used ChatGPT to brainstorm keywords has to explain that choice in a document judges will read before they even review the submission.

And the classification of AI tools as “secondary sources” makes sense. It fits AI into a framework NHD students already understand - the primary/secondary distinction the program has taught for decades. You don’t need an entirely new set of rules if you can extend existing ones.

That’s a much more considered policy than most. But critically, NHD’s guidance also includes this:

“While AI detection tools continually evolve, there are no tools that are 100% effective at identifying whether content is created by AI or a human. Unlike plagiarism, where judges could find a source that a student copied without citation, there is no way to be completely sure that a student used AI inappropriately.”

This is an admission that many schools and teachers still don’t accept - detection tools don’t reliably work. Period.

And rather than pretending otherwise, NHD leaned into something else they already had to help assess authenticity: the interview. NHD judges talk to students about their work. They ask how you did your research and how you created your project. And the guidance tells judges to “please believe what students say during the interview” and to avoid accusatory language - specifically warning against questions like “Did you copy from AI?” in favor of more constructive framing like “I was not always sure which parts of your project were your own words or the words of others.” How they do that at scale is something I'd love to know more about, but the policy itself is far-sighted.

NHD has acknowledged a problem it cannot fully solve, built the best verification system available (a process paper; actual conversations with actual students), and asked its judges to extend trust. That’s a level of institutional honesty that few organizations have managed.

One complication I found worth noting: NHD’s Colorado affiliate bans AI “in any capacity” - at odds with the national guidance that permits brainstorming and grammar checking. Even within this organization, the rules aren’t consistent.4

It’s worth asking - what message does that send to students in Colorado?

What None of Them Have

Three things are missing across the board.

First: I could find no public reporting from any organization I examined - not just the four I profiled - on how many entries have been disqualified for AI use. I have no idea, for example, whether the Concord Review has actually notified a school about a plagiarized or AI-written submission. This absence of data means the deterrent effect of these policies operates entirely on faith. Students are being told “we may check” and the question of whether anyone has ever actually been caught remains unanswered.5

Second: with the exception of NHD, which is the only one honest enough to say outright that AI detection is flawed, none of the other contests are clear about how detection works. These tools don’t return a simple yes or no. They produce probability scores - a 40% “AI likelihood” rating on a student’s essay is exactly the kind of ambiguous result that leaves a teacher with no actionable information. My friend’s “various levels of AI usage” is a textbook example.

Third: what happens to a student who is wrongly flagged? I could find no competition publicly describing a process for handling false positives. The JFK contest says disqualification occurs “if AI is detected” - but what if the detection is wrong? Legitimate human writing gets flagged regularly, particularly writing by non-native English speakers. High schools and colleges are dealing with this on a daily basis. The John Locke Institute states that “our determinations in all such matters are final” - the opposite of an appeals process. Only NHD addresses this at all, by telling judges to believe students and avoid direct accusations.

These aren’t just academic observations. My friend’s experience reveals exactly what happens when competing guidelines conflict. He was carrying an institutional burden the organization never acknowledged, placed in an untenable position that required him to exercise judgment using tools that produce inconsistent results. His school had once subscribed to a detection platform but no longer did, leaving him to improvise with browser extensions on a 24-hour timeline. Not every teacher from every school is likely to be so careful.

The Lesson Nobody Intended

What must all of this look like from the student’s perspective?

A student researching for just two of these competitions would encounter three contradictory positions before writing a word. The John Locke Institute's policy page says LLMs are welcome thought partners. Its FAQ says the safest course is not to use AI at all. The JFK contest says if AI is detected, you're disqualified.

Further complicating matters, your school has its own AI policy - maybe it allows AI for brainstorming but not drafting, maybe it bans AI entirely, or maybe it hasn’t said anything at all. If your school bans AI and you submit to the John Locke competition using AI as a “thought partner,” you are violating your school’s policy even though you’re following the competition’s rules. If your school allows AI with disclosure and you plan to submit to the JFK contest, you’re not permitted to do something your school considers routine. Both competitions require a sponsoring teacher to sign off on the submission - a teacher now caught between the school's policy and the competition's. For contests that don’t require one, students are left to their own devices, many of whom will submit AI-assisted work, some of which will get past whatever gatekeepers exist.

But there is an even bigger lesson lurking beneath these contradictions. NHD has drawn a clear line between process and product uses of AI and built a disclosure system around it. What message does it send students when a national competition with real stakes - prize money, prestige, college applications - are actively wrestling with how AI fits into serious academic work while their own classrooms are avoiding the conversation?

If a student's English class treats ChatGPT as cheating, but an extracurricular essay competition invites her to stress-test her arguments with it, she's going to start asking which environment is preparing her for the world she's actually entering. That's a question almost none of these organizations - and very few schools - are ready to tackle.

Here’s another question I’d have for competition judges: do papers with AI attribution bear some kind of stigma? If you’re a student submitting to a competition that allows AI with disclosure, and you honestly disclose your use, does that disclosure work against you? The Locke Institute suggests that, yes, it does. Their FAQ states that AI use “can only negatively affect” a student’s assessment. So what’s the incentive to disclose? Anyone following the discourse knows that many educators are deeply skeptical of AI-assisted writing. Locke made that skepticism policy.

And what about rejections? When a student’s paper is rejected - which for most programs is the vast majority of entries - do they receive any indication of why? If a paper was flagged by an AI detector, does the student ever find out? Or does it disappear into the ether alongside hundreds of others, with the student left wondering whether their work wasn’t good enough or whether an algorithm decided they were a cheater? The process makes it impossible to know.

When the rules are contradictory and detection and enforcement methods are unclear, students can sniff out hypocrisy like bloodhounds. If the adults in charge of determining contest "winners" have conflicting approaches to what's authentic and what's not, how can students have any confidence that what's being evaluated is fair?

Back in the Classroom

My research started because of a phone call. I keep ending up back in my classroom because the parallels are impossible to ignore.

The four approaches I’ve described - silence, ban, strategic deterrence, and structural integration - map almost exactly onto what I see happening in schools across the country. Some departments ban AI entirely and treat any use as plagiarism. Others allow it in theory but make clear - through grading rubrics, pointed comments, or classroom culture - that AI-infused work will be viewed negatively. Others have built sophisticated frameworks distinguishing between process and product uses. And a not-insignificant number continue to say very little, hoping the problem resolves itself.

The writing competitions are a microcosm of the same fragmentation. They're easier to study because the rules are public and the outcomes are clear - you're either published, selected, or you're not. But the underlying dynamic is identical: students using AI in the same way might be rewarded in one context, punished in another, and face no consequences at all in a third.

A Silver Lining?

Here's what I didn't fully appreciate until I started this research. The organizations that are doing the most thinking about AI in student writing operate under constraints that are in many ways more severe than what classroom teachers face. They can’t fall back on “I know my students’ writing.” They can’t rely on watching a kid struggle through a draft over three weeks. They don’t have a writing sample from September to compare against a February submission. They have to make a judgment based on a single artifact.

That’s a harder problem than what most of us face in our classrooms. And the two organizations that engaged with it most seriously arrived at complementary insights. NHD built something practical - a process paper, an annotated bibliography, and an interview - a pedagogical methodology that was never designed for AI but turned out to be perfectly suited to it. The Locke Institute identified the right principle: that human thinking, creatively and critically applied, produces better work than AI can generate. They also require an academic referee who can vouch for a student’s work and reserve the right to ask contestants to discuss their essays with faculty. This is Locke’s first year under the new policy. The pieces are in place. The next step, if possible, would be making those conversations routine rather than discretionary, especially when questions arise.

Competitions and classrooms are obviously different writing environments with different goals and purposes. A contest that sees a student once can’t do what a teacher who sees her every day can do. But the reverse is also true. If organizations processing tens of thousands of anonymous entries have found ways to evaluate authenticity beyond simply running a document through a detection tool, teachers who know their students personally have more opportunities to try - and fewer excuses not to do so.

Making Thinking Visible … Again

We know that human thinking produces more original and more compelling work. That’s the argument the Locke Institute makes emphatically. But saying so isn't enough.

As I’ve said before, we need to build the assignments that make student thinking visible. This is what my own department is attempting this spring with our redesigned research papers, and it's what both surprised and impressed me about NHD. When a student has to narrate how she arrived at her thesis, what she learned from one of her sources, and where her argument changed direction, AI becomes one tool in an observable process rather than just a black box that produces a finished product.

The competitions that will thrive in an AI world are the ones that stop treating AI as a compliance problem and start treating it as a pedagogical one. That’s harder, more time-consuming, resource-intensive, and messier than a ban.

My friend was placed in an impossible position. He had a deadline, a jury-rigged detection strategy, and no framework from anyone - not the contest, not his school - for what to do. So he did the cautious thing and pulled the essays. The question this piece is asking is why, in 2026, that's still the only defensible option available to a conscientious teacher.

If you have a story about how you’ve shepherded a student through a competitive writing submission in the age of AI that you’re willing to share, I’d love to hear about it in the comments.

Connect With Me

Beyond this newsletter, I work directly with schools, educators, and organizations navigating AI Awareness and integration. Take a look at my website and reach out - I’d love to hear what you’re working on.

One thing is clear - AI cannot shovel snow.

Agentic AI is currently all the rage - coding tools released by OpenAI and Anthropic over the past six weeks have driven the conversation across legacy media and Substack alike. It does not require accepting the hype to acknowledge that something has shifted. As I sit and watch a late February blizzard blanket my neighborhood, I’ve been reading and thinking about what the rise of agentic AI means for teachers, students, and schools, and it underscores something I’ve been reluctant to say outright.

Practically since its arrival, schools have treated AI mostly as a problem - how do we stop students from using it to cheat? What can we do to slow all this down? How can schools design policies to prevent AI from destroying authentic student work? These are real and ongoing issues and they deserve serious treatment. I’ve catalogued many of them.1 But the “AI is all negative for schools” stance has hardened into orthodoxy and I worry that it’s making it harder to see anything else.

The increasing recognition that AI is evolving from a series of chat interactions into the ability to build entire projects from scratch introduces a fundamentally different question. And that question is going to require more imagination than schools may be capable of given where most teachers currently are.

For starters, I simply don’t think that many educators are aware of how quickly the landscape is shifting. I applaud those teachers who have paid enough attention to try to get more knowledgeable and fluent with the more advanced models. But it seems that just when many schools are finally deciding it’s time to confront the “AI issue” - either by paying for AI wrappers like Magic School (a decision with more consequences than many realize) or bringing in outside consultants to review the basics - the conversation is moving elsewhere. It doesn’t mean that learning the basics and understanding the strengths and weaknesses of LLMs isn’t important. It’s just that, for anyone who follows this closely, it’s clear it isn’t going to be enough.2

I’ve watched the response to agentic AI unfold over the past several weeks across a dozen Substacks and podcasts, and I’m struck by how much of it has followed a familiar pattern: here is a powerful new capability, but the focus in schools is mostly on why it’s dangerous. The concerns raised - more cheating, student data privacy, and the security risks of connecting AI agents to institutional accounts - are all legitimate and will need to be addressed. But when the primary response to a genuinely new development is to mostly fixate on the ways it might go wrong, I think the conversation is incomplete. A preoccupation with the downsides that crowds out everything else may prevent educators from recognizing the potential usefulness of a transformative technology.

Seeing Is Believing

I remember the first time I saw a student share a Google Slides presentation from the cloud. It seemed like magic at the time - the ability to host digital work that could be accessed anytime, anywhere, from any machine. I hadn’t used the Google suite of products very much in the early aughts and that one demonstration convinced me that cloud computing was the future. I needed to see it to understand it. Google Drive was once unknown to me, as it was to most teachers, but it’s now an essential part of how I store, organize, and work with all my digital materials. It happened over many years and now I can’t imagine living without it.

Technology works like that. You can read as much as you want, but until you see what everyone is talking about, until you watch an AI agent design and deploy an online module or website in real time, until you begin to realize what this means for simply the manner in which we produce documents - spreadsheets, presentations, memos, reports, and virtually anything else that requires organizing information into precise digital formats - you are not likely to appreciate how this will affect our work habits.

What Agentic AI Has Allowed Me To Do

In my Government and Politics class, I designed an Ethics Bowl case built collaboratively with AI. I could not find a suitable article summarizing the past year of U.S. foreign policy that was not written from a hyper-partisan perspective. Instead, I co-created a thorough analysis of the current administration’s foreign policy decisions, framed through the lens of Thucydides’ Melian Dialogue and Kantian ethics. The case went through multiple drafts across several days. I subsequently used a separate AI project to fact-check every quote, date, and statistic, catching one major error that I corrected. I am confident I could not have produced a case of that depth and rigor in the time I had without AI collaboration.

I also wanted students to have an interactive preparation tool - something they could use independently to click through each policy area, toggle between factual summaries and competing arguments, review key terms from the textbook chapter, and test their comprehension before the live discussion. Six months ago it would have been impossible. Building an interactive web application is not something I knew how to do. I don’t know how to code.

Using Anthropic’s Cowork - one of the agentic tools that everyone is currently writing about - I described what I wanted. I uploaded my case document and assignment instructions. And over the course of a few working sessions, the AI built it. A functioning interactive web application with tabbed navigation, expandable case sections, a glossary connecting textbook vocabulary to the specific case, verified source citations, and a comprehension quiz with immediate feedback. I had it hosted at a live URL and shared it with students through our learning management system.3

The same session also produced a fifteen-slide introductory presentation for the activity. All from the same source documents.

The process was not seamless and I needed to troubleshoot a handful of steps. The tool needed my professional judgment - what to include, what to cut, when something wasn’t right, how to redirect. It took some time. But it was work that produced something I could not have produced alone, and it was work that would have been unthinkable for a classroom teacher even a year ago.

That was the moment when the possibilities crystallized.

The Gap

Many teachers don’t have the time or inclination to do this and many won’t for months or even years.4

I’ve had a curiosity about digital tools from an early age and invested in the conditions to make it possible to create the use case described above: a paid subscription to advanced frontier models, custom environments I’ve built over months of experimentation, and a specific pedagogical need that gave me a reason to investigate what the tools can do right now. I’ve spoken with enough colleagues and led enough conversations to know that most educators haven’t encountered these tools, let alone used them.

Knowing that I have the ability to build interactive web applications means I can’t unknow it. More use cases and possibilities will keep coming. The distance between those two realities - between where most educators are right now and where the technology already allows motivated practitioners to go - is likely to create more of a divide in schools, especially if students begin to incorporate these skills into their work on their own.

Imagination As Well As Caution

The dominant response to AI in the education space - currently extending to agentic AI - is primarily to focus on what’s gone wrong.

And I understand why agentic AI is so scary for schools - when someone describes a tool that can autonomously access a student’s Google Drive, browse the web, and execute tasks without supervision, the immediate instinct is to worry about data privacy and security. That makes sense.

However, I’d love to see the conversation broaden. What I’m arguing for is imagination as well as caution. Believe me, I understand that generative AI, specifically agentic AI, represents significantly more threats than Google Drive. But if we want to treat it as “normal technology,” as many who resist the hype are preaching, then we need to understand the typical progression that “normal technology” takes. I’m not suggesting we ignore the risks. I’m suggesting we match them with equal imagination about the possibilities.

An excessive focus on what could go wrong produces a profession that is perpetually reactive - always responding to what the technology does, never shaping what it could do. And it produces a widening gap between what educators currently think AI is (simply a cheating tool, a security risk, a privacy nightmare) and what it may actually become (a genuinely powerful instrument for creating things that were previously impossible for non-technical professionals to build). I realize this may be a counter-intuitive take at the moment, but I think it needs to be part of the dialogue. There are still people who see AI as nothing but a threat to education, but my experience and that of a growing number of practitioners suggests otherwise.

What if the question encompasses more than just “how do we protect students from agentic AI” but also “what could we eventually ask students to build with these tools that they couldn’t build before?” What if assignments evolved beyond “write an essay” or “make a presentation” toward “design an interactive tool that helps someone understand this problem?” That question requires imagination and a sense of possibility, both of which are sorely needed right now.

Where This Leaves Me

Schools are likely to escape the full implications from agentic AI, at least on the student side, for at least another year or so, if the pattern of adoption rates is anything like it’s been up until now - mostly slow, uneven, and imperfect. Teachers have not been incentivized or trained to integrate agentic workflows into their routines. But just like Google Drive, I suspect agentic AI tools will simply become part of how many of us work in the near future. I’ve already seen too much value to pretend otherwise.

Moving through the current moment as an educator is going to require more imagination than has been demonstrated up until now. The same concerns about cheating and authenticity will persist, but I suggest that instead of panic and surrender, we embrace both the uncertainty and the possibility. What AI is now capable of is genuinely helpful and, to me, remarkable - while at the same time unsettling and, for many, frightening. Those things will have to coexist.

I’ve been reluctant to make predictions in this newsletter - the field moves too fast and most of what’s coming is outside my area of expertise. But I do know the classroom and I’ve been creating curricular materials for three decades. The nature of how AI tools are evolving represents the single most interesting and exciting development on the teacher side I’ve ever experienced.

With respect to AI’s overall impact and whether it does what its most ardent proponents promise or its most informed critics predict, I still don’t know. Frankly, no one else does either.

The provocative title of this piece is a movie reference, but I also think it will be the literal truth. AI is going to be everything, everywhere, all at once. The question for educators is what we are going to do about it and how we frame it - as an ongoing problem to manage or as something that might actually change what’s possible in a classroom. I’m holding out that we can rise to the occasion. I don’t see that we have much of a choice.

Connect With Me

Beyond this newsletter, I work directly with schools, educators, and organizations navigating AI integration. Take a look at my website and reach out - I’d love to hear what you’re working on.

AI is getting boring. Not boring in the sense that each new AI story isn’t interesting – but boring in its predictability. Watching my sixth grader do her homework last week may have taught me more about where AI in education is actually headed than a year of reading the current debates.

New capabilities are announced. Some herald these developments as game changers, arguing that a radical revolution is just around the corner, while others dismiss the hype, focusing primarily on the costs – economic, environmental, and human – and downplaying the technology. And around the merry-go-round we go. It’s getting tiresome.

This past week alone, a 26-year-old CEO’s viral essay about how something enormous is happening was viewed more than 50 million times. Unsurprisingly, one of the most prominent AI skeptics responded by calling it ridiculous and asking for evidence. Another post I saw made a compelling, rigorous, data-driven case that the trajectory of AI improvement is essentially vertical and that anyone who isn’t freaking out simply isn’t paying attention. Addressing precisely this trend, a well-known writer made an equally sharp case that the AI discourse itself is the problem - that the technology is being deliberately mystified by the companies who profit from the mystification and that the actual utility, while real, is far more modest than the hype suggests.

The camps are still entrenched. The arguments recycle. Rinse, wash, repeat.

And that's just a sampling of headlines from the past week or so. None of them are decisive, all are exhausting, and all underscore how impossible it is for educators to navigate the AI information ecosystem while also trying to teach.

For the past year, I've tried to see all of this through a slightly different lens.

I published my first post on this Substack a year ago. I was cautious and full of questions I didn't have answers to. I quoted William Goldman's line about the movie business - "Nobody knows anything" - because it felt like the most honest thing I could say about AI in education at the time. Sixty posts and over eight thousand subscribers later, I still don't have many of the answers. But I do feel like I know at least a few things.

What a year of writing, teaching, and watching from inside a classroom has made clear is that AI is here, it's being absorbed into schools the way every technology before it has been, and we are all going to have to figure it out - mostly on our own and without the luxury of certainty.

Normal Technology

If I’m fortunate, I will remain in the classroom for another ten or perhaps even fifteen years. The decade ahead could be the most exciting of my career or the most frustrating. Likely both. The pattern is remarkably consistent. Personal computers, laptops, the internet, smart boards, learning management systems – each arrived with transformative promises, and each was incorporated into the existing structure of schooling without fundamentally altering it. The tools changed but the teaching, for the most part, didn’t.

I wonder what I might look back on in ten years as the moment when I realized that generative AI was just going to be another technology we were asked to adapt to - normalized the way everything else was.

The current contender is watching my sixth grader this past week work through a series of prompts designed to help her with a short essay using our school’s AI wrapper.

What was most remarkable about it was how unremarkable it all was.

For about thirty minutes I watched her iterate her essay about the Roman Empire using a thoughtfully scaffolded lesson that pushed her to incorporate evidence into her argument, clarified content questions when she was stuck, and provided useful examples and analogies to help illustrate the goals of the assignment. She wasn’t amazed and she wasn’t confused.

She wasn’t cheating. She was doing her homework.

I was watching with a teacher’s eye, not just a parent’s. The lesson design kept her in the driver’s seat – it asked questions rather than supplying answers, and when she tried to move past the evidence step, it pushed back. She had to do the thinking. She was so proud of her essay that she wanted to read it to me aloud. That was new.

As someone who has expressed real ambivalence about AI use in middle school, I was genuinely surprised. Observing her actually do the assignment made me much less worried than thinking about it in the abstract. When I asked her what she thought about it, she was matter-of-fact. “I liked the analogies. Those were helpful. It was my essay. It didn’t do it for me.”

She's eleven. She doesn't know what AGI stands for, she's never heard of scaling laws, and she couldn't care less whether AI is conscious. She was just trying to finish her homework. What I watched was the opposite of what I've read in virtually every comment section under any positive AI coverage of education in schools - that an entire generation of kids is going to be rendered incapable of thinking for themselves. What I saw was an eleven-year-old who did the work, finished her essay, and wanted to explain it to me. Any parent of a tween knows that’s a win.

Three years ago, when she was in the third grade and I was just beginning to grapple with ChatGPT, it was inconceivable to me that she would enter high school in a world where schools could still afford to ignore AI. Now, here we are in 2026 and it’s a homework assignment. Not a crisis. Not a revolution either.

After writing about AI in schools for a year – the costs of cognitive off-loading vs. the potential benefits of personalized learning, the heated debates, the existential hand-wringing – what I watched in our living room was the AI revolution taking place in real time. Not with a bang but a whimper. In this instance, the personalized tutoring carried the day. I’m sure that won’t always be the case. A less carefully designed lesson, or a student less willing to push through the friction, and it can go sideways fast.

This particular lesson worked because a colleague of mine didn’t use a generic pre-loaded template. The lesson was designed with the same care and attention she'd bring to any assignment - tested and fine-tuned to meet a specific learning goal: pushing students to use evidence for their claims. The tool didn’t make the lesson good. The teaching made the tool useful. That distinction tends to get lost.

My daughter is hardly alone in this experience. Thousands of students across the country are using AI wrappers like Flint, MagicSchool, and others in their classwork every day. It’s not in every class and it’s not in every assignment – not by a long shot – but it’s happening and we will not know the ramifications until more data is collected, analyzed, and sorted.

By then, my daughter will be headed for college.

Normal Adoption, Abnormal Pace

What I’ve learned over the past year, and should have been less surprised by, is that AI adoption in education is proceeding on the exact same trajectory as every previous technology – slow, shallow, uneven, and lumbering in all the ways that institutions generally are. In the spring of 2023, in the flush of seeing what AI was capable of, I thought the rate of adoption would go much faster. I was wrong. But I’m glad that I was.

Justin Reich’s Failure to Disrupt: Why Technology Alone Can’t Transform Education disabused me of this notion. The history of educational technology is a history of overpromising and underdelivering. AI has been no exception.

In some ways this is reassuring. It takes years for new technologies to get integrated into existing systems and longer still for the shape of the impacts to become clear. Despite almost weekly predictions of imminent and devastating job losses, the current numbers have not borne that out. AI, by this reading, and certainly while witnessing my daughter complete her homework assignment, is “normal technology” - significant, yes, but following the same basic adoption curve as everything that came before it.

But here’s what makes me less comfortable with that framing. Even incremental improvements are developing at a pace that no previous educational technology has approached, and the distance between what AI can do and what most educators know it can do is widening, not closing.1 The adoption rate is normal. The technology is not. And I’m resigned to the fact that these two realities are going to coexist uncomfortably for the rest of my career. The tools will keep outpacing the training, the policies will keep chasing the capabilities, and teachers will keep being asked to make decisions about technology they haven’t had time to fully understand.

Lead with the Pedagogy

I don't see any single decisive moment where teachers everywhere will simply need to wake up to the fact that generative AI tools are going to be with us for the duration. Many would say that moment has already passed, but that's not how institutional change works. Unlike COVID, where the crisis was imposed directly and schools had no choice but to shut down overnight, no degree of urgency around a question of technology will force that kind of speed. It will still be one class at a time, one week at a time, one semester at a time and, critically, one teacher at a time. Schools march on because they must.

The controversy right now revolves around the extent of what “navigating” AI should look like as we all continue to sort out boundaries we feel comfortable with, review imperfect data that will likely be out of date by the time it is published, and simply use our own two eyes to evaluate what’s working and what isn’t.

I maintain that if the conversations schools are having about AI revolve around whether the technology is good or bad, or just about whether AI can perform a particular task, those conversations are mostly a waste of time.

The only productive conversation starts with pedagogy: what are the skills, habits, and dispositions we want students to develop, how do we know they’ve developed them, and how does AI support or undermine those goals? I’ll bang that drum over and over because I think it’s the only path forward.

Educators have to lead with the learning. Not the technology. That’s what I wrote about last week with our department’s research paper redesign, and it’s what I’ll keep writing about – not because I’ve run out of things to say about the tools, but because it’s the only approach I’ve seen that doesn’t become obsolete the moment the next model drops.

I don’t know what the lifeboats look like. I don’t think anyone does. But I know they won’t be built by people arguing about whether the flood is real. They’ll be built by teachers. They always are.

Connect With Me

Beyond this newsletter, I work directly with schools, educators, and organizations navigating AI integration. Take a look at my website and reach out - I’d love to hear what you’re working on.

My father was a top litigator, member of the American College of Trial Lawyers, and feared opponent in the courtroom. I was too young to understand everything he was doing professionally, but my memories of his work habits are vivid. After a 10-12 hour day in Manhattan, he’d be home in his favorite chair with a brief in his lap, surrounded by memos, files, and accordion folders, preparing for a case.

He was born in 1925, a member of the Greatest Generation, and his career spanned the postwar years through the early 2000s. Despite representing cutting-edge technology clients, my father never owned a computer, a cell phone, or a laptop. He was probably the only attorney alive when he passed in 2011 who did not have an email address in Martindale-Hubbell1. He was strictly analog. Everything he did was by hand on legal notepads or markups of printed material.

His work ethic was legendary. As I got older - even attending law school before pivoting to teaching - my appreciation of his career deepened. I began to hear stories from former clients, older family members, and even opposing attorneys about his most famous cases. One of the traits that set my father apart was his ability to locate virtually any document at a moment’s notice during the pressure of a trial.

To appreciate how difficult this was in, say, 1980, imagine a case involving thousands of pages of documents, any one of which might become critical during a witness’s testimony. Before the age of digitization, lawyers brought in hand trucks of bankers boxes with folders bursting at the seams. My father was known for many things in the courtroom, but a signature skill that made him so formidable was a meticulous filing system he had built over decades that allowed him to find the precise document at exactly the right time.

“When Joe came into court and we saw how he organized the case with that level of detail,” one older attorney told me, “we knew we were in trouble.”

I miss my father for lots of reasons, but those work habits came back to me recently when I sat down with one of our strongest students to talk about how she actually does research - part of my preparation for our history department's effort to redesign the way we teach it.. Her approach mirrored much of how my father operated decades earlier, and it reminded me of a concept I first encountered at Harvard's Project Zero: "Visible Thinking."

The Question We Started With

The impulse for the assessment redesign was obvious.

AI can now generate an outline in seconds. It can produce a thesis statement, identify sources, summarize dense academic articles, and write polished prose that is, for most practical purposes, undetectable. I’ve spent the better part of two years teasing out every stage of the research process - from topic selection through final editing - and at virtually every step, AI has the potential to either augment or completely derail the cognitive work the assignment is supposed to teach.

AI itself is not the primary culprit. User intent, AI fluency, discernment, and domain expertise determine whether AI adds value or replaces thinking.

The problem is that mastering these skills requires the kind of discipline and judgment most teenagers haven’t developed yet.

And it’s not just a high school issue. My sixth-grade daughter recently came home and told me she and a friend are entering an “AI Live”2 competition in which middle schoolers create a piece of performance art conveying a message about their relationship with technology, documenting how they’ve used AI in the process. Students as young as fifth grade are using AI wrappers to help structure their essays. I’ve already documented the influence of AI in the middle school debate community, and research databases like JSTOR are integrating AI into their search tools.

As Hemingway once observed about bankruptcy, AI is infiltrating every corner of the school experience - gradually in 2024 and 2025, and now suddenly in early 2026.

This is the conversation every school is having right now, and it almost always starts in the same place.

So when our department sat down to talk about the research paper, the instinct was natural and understandable: how do we make this assignment AI-proof?

But that turns out to be the wrong question. Or at least, an incomplete one. Because once we started talking seriously about what AI could compromise, we found ourselves in a much more interesting conversation - one that had less to do with technology and more to do with what we actually believed a research paper was for. The question that proved more productive was deceptively straightforward: what are the non-negotiable thinking skills we want students to develop through this process, and how can we feel confident that they’ve developed them?

What One of Our Strongest Students Already Knew

In anticipation of our meeting, I sat down with one of our strongest research students - a senior who had written outstanding papers across multiple years of our history curriculum and had also distinguished herself in rigorous independent research in another discipline entirely. I wanted to understand how she approached research, not as a theoretical exercise but as someone who has repeatedly demonstrated it at a very high level.

What she described was reinforcement of everything I’ve been reading over the past two years. She talked about becoming genuinely invested in a question and doing more research than she needed as the material pulled her forward. She described a process of using sources to find other sources - following footnotes, tracing citations, clicking through to the next article to see where a particular claim originated. “Once you come upon one good source or one good piece of information,” she told me, “the best way to find the next best thing is to use that source to find other sources.” It sounds simple, but it’s actually a sophisticated research disposition that many students never fully embrace.

Her organizational system was equally revealing. She would start with a general sense of her argument, create categories for her body paragraphs, and then research into those categories - color-coding primary source evidence, leaving herself analytical notes in the margins so she wouldn’t forget why a particular passage mattered weeks later. “You kind of need to catalog your thoughts a little bit if you’re working on something more long-term,” she said. She wrote her topic sentences last, because the structure emerged from the thinking, not the other way around. When I pointed out that she seemed to be outlining and writing her paper as she researched it, she agreed. “It’s extremely iterative,” she said.

This is the conversation that got me thinking about my father - here was an 18 year old instinctively developing the kind of skills necessary for deep, lasting learning - and then methodically categorizing her work so she could come back to it days or weeks later. Sure, as someone growing up with the internet and online source material, her tools were digital, but the thinking and overall approach was eerily similar to how my father dug into his case preparation 50 years earlier.

The Virtues of an Analog Process

I asked her what she thought about moving more of the research process to paper-based, handwritten formats. Her answer surprised me with its nuance. She acknowledged that a student working digitally without AI could probably produce a better final product than a student working entirely on paper. But then she added: “In terms of building skills, the final product of that research paper might not be as good, but more skills would be built by the student by doing it in an analog manner.”

When I asked about AI’s role in research more broadly, she was remarkably specific. The worst use, she said, was at the very beginning - for brainstorming and generating ideas - because “it can become difficult at a point to differentiate what’s your own original idea and what you’ve read through AI.” This is utterly at odds with much of what you read about using AI to brainstorm - and she is not the first student I’ve heard this from.

She didn’t think it should be used for outlining either. And when I asked whether learning to use AI was itself an important skill to develop, she was direct: “I don’t think using AI is as much of a skill to build as other things, like learning how to research and write by yourself. I think that is something that students can learn to do later in life. What’s difficult is to build those fundamental skills.”

This from a top student who has figured it out on her own. I’ve no doubt that if and when she integrates AI tools into her work routine she will continue to be successful. She’s already developed more authentic research skills than most of her peers - and many adults for that matter - but the critical distinction is her mindset. My conversation with her aligns with the gut feeling I’ve had for the past few years that the strongest students understand instinctively where AI harms them. They are committed to doing the hard work first because it works.

The Ongoing Debate

Unfortunately, the picture is not going to be as clean as “keep AI away from students until they’ve built the skills.” In our own department, I teach an advanced Independent Research in History course, with students enrolled who are heading to college in a matter of months, where I’ve slowly introduced AI tools for discrete stages of the process. The Deep Research models allow students to pinpoint relevant sources faster and with more specificity than ever before. NotebookLM and other digital tools with AI integration allow for a level of organization and file scanning that has already become the norm across industries.

I think that’s appropriate for students at that level, and I’d make the case that exposing advanced students to these tools before they encounter them unsupervised in college is part of our responsibility. But I’m honestly torn, because the line between “ready” and “not ready” is never going to be as bright as we’d like it to be. And the fact that one of our most advanced research students does not rely on AI for any stage of her process speaks for itself. If and when AI enters the picture, it should add value and never replace the core skills.

Disciplinary Differences

The picture gets even more complicated when you look across disciplines. In the science field where she also excels, the organizers behind the major competitions have released explicit guidelines permitting students to use AI for improving the phrasing and clarity of their writing. The use of AI is becoming accepted practice in scientific research more broadly, where communicating findings clearly is essential while writing has never been the primary skill being assessed.

The trend is clear: different disciplines are arriving at different answers, and the humanities’ emphasis on writing-as-thinking puts us in a fundamentally different position than fields where writing may be seen more as a delivery mechanism for other kinds of expertise.

What holds all of this together, I think, is that the hardest skills to build are the most human ones. To perform advanced research, you need judgment, patience, perseverance, and skepticism - which are far more difficult to measure.3 These are also prerequisites for developing AI fluency. Not every student will be ready for open-ended AI use on the same timetable, and not every discipline will draw the line in the same place. But the sequence matters. Independent thinking always has to come first.

What We Built

Every humanities department in every school is going to have to work through some version of this conversation, and not everyone will land in the same place. Some will be more conservative about AI use, others less. The key is being honest about the trade-offs - and then designing accordingly.

What our department ultimately landed on was less an “AI-proof” assignment and more an attempt to make the entire research process visible and documentable. We were thinking practically about how to shift the burden away from surveillance and toward structured evidence of student thinking at every stage. The redesign moved almost all of the cognitively critical work into the classroom and onto paper - handwritten worksheets, annotated hard-copy sources, in-class quizzes that require students to explain their reasoning rather than reproduce information. The grading structure shifted accordingly, with the final product counting for significantly less than the process checkpoints that precede it.

I don’t want to paint this as a finished product or a triumphant case study. We’re still working through real questions, and I suspect every school attempting something similar is navigating its own version of the same tensions. But what interests me most is a connection to something I first encountered in a world where generative AI wasn’t remotely part of the conversation.

Making Thinking Visible

Twice in the past twenty-five years - most recently in 2009 - I attended Project Zero, a week-long summer institute at the Harvard Graduate School of Education. Project Zero has been around for decades, and one of its central preoccupations is what they call “Visible Thinking” - a research-backed framework built around the idea that if you want students to think well, you have to make the thinking itself visible, not just the products that thinking is supposed to produce.

The approach emphasizes three core practices: thinking routines that structure how students engage with content, documentation of student thinking as it happens, and reflective professional practice among teachers. Project Zero is still going strong, and its framework is arguably more relevant now than at any point in its history.

What struck me as our department’s redesigned research paper took shape is that we had independently converged on almost exactly this framework - without everyone in the room having heard of it. The timeline quiz that asks students to defend their periodization choices is a thinking routine. The annotation requirements that force students to analyze primary sources through multiple lenses are documentation of thinking. The revision log that tracks changes in argument and evidence is a record of cognitive development over time. The one-on-one conference is a moment where a teacher can observe thinking in real time, rather than simply evaluate a product after the fact. Even the final reflection, in which students assess the quality of their own evidence, is a metacognitive exercise straight out of the Visible Thinking playbook.

We Can’t Avoid These Questions Any Longer

The pressure of AI forces us to answer a question that schools have been able to avoid for a very long time: if we can’t trust the product, what evidence of thinking do we actually have? For most traditional assessments, the honest answer was: not much. The research paper, as most of us experienced it in school and as many of us have been assigning for years, was always a product-focused exercise in which process was assumed rather than observed. AI has exposed just how fragile that assumption was.

Visible Thinking is not an AI strategy. It was coined years before anyone imagined a world where a chatbot could write a competent five-paragraph essay in 15 seconds. But it may be the most useful framework schools have for responding to this moment - not because it “solves” AI, but because it redirects the conversation from surveillance to pedagogy, from policing products to designing for thinking. Interestingly, in Project Zero’s current materials for this summer’s institute, I found virtually no mention of AI. It’s always been about thinking.

An Analog Approach in a Digital World

My father's filing system may have been from another era, but the practice of externalizing one's thinking - organizing, categorizing, making connections visible - is as old as the written word itself. He simply adapted it for the courtroom. Reflecting on his practice now connects to the Visible Thinking concept I didn’t have a name for until I sat in a lecture hall at the Harvard Graduate School of Education more than twenty years ago.

Every document organized, every connection ingrained, every piece of evidence coded and accessible because the thinking behind it was explicit and externalized. When opposing counsel saw those tabs, they weren’t intimidated by the system itself. They were intimidated because they could see that someone had already done the cognitive work of integrating thousands of pages into a coherent argument, and that the thinking behind that preparation meant every document could be retrieved at the moment it mattered most.

I know my father would not have stood in the way of AI advancing and improving efficiency for routine tasks in his office. AI will automate many kinds of knowledge work, much of which we’re still figuring out. But it doesn’t change the importance of visible thinking for the truly essential cognitive efforts that require human judgment, synthesis, and review. I know he would have resisted using AI for his own thinking.

And I, for one, would not have bet against him with just his hard copies, legal pad, ballpoint pen, and a highlighter.

Connect With Me

Beyond this newsletter, I work directly with schools, educators, and other institutions about pedagogically questions raised by AI. Take a look at my website and reach out - I’d love to hear what you’re working on.