Teaching Future Teachers in the Year of AI Bans

 Last week I took some time for family and visited my dad and my sister. My sister is a music educator who uses AI extensively for non-music tasks. As we sat down for a cup of coffee, with me still trying to recover from jet lag, she asked, “So what can you tell me about how AI will change education?”

We talked about this challenge in our respective educational worlds, hers in music education and mine in teacher education. We both agreed that one of our advantages is that we have performance measures. Musicians have to play in front of an audience, and teachers have to teach in front of a classroom. Live performance creates a measurable moment that no amount of AI use will remove. Weighing AI use on the path to that performance is harder to figure out. When we tried to go deeper, I had a hard time articulating evergreen truths.

AI Bans

While we all grapple with AI, New York City Public Schools paused AI use for elementary and middle school students, and Los Angeles Unified restricted all students from using AI tools, both for the 2026-27 school year. I sympathize with these decisions, though I disagree with them.

There are three risks behind them. The first and most obvious is that we don't know what the impact on students will be, so “do no harm” comes to mind. A one-year ban will do fairly little harm while we get our facts straight. Second, public opinion about AI is negative and in flux. Districts that rely on public funding are therefore cautious, individually and collectively, and wait-and-see seems politically prudent. Finally, data breaches and privacy violations pose significant risks. From these perspectives, large districts banning AI seems a logical temporary (I hope) step.

Still, I think it is a short-sighted step. This post is about what we teach pre-service teachers at a time when educational technology is in flux.

What I Teach

This semester I am teaching Elementary Technology Integration, a class my students take fairly early in their program, as they begin professional practicum experiences in schools.

Teaching future teachers always involves a gap between what we teach them and what they see in schools. We have to be both present and future-oriented, and help them bridge what exists within current systems and imagine what the (best) future might be. That future is where their careers lie. They will graduate in two years and, hopefully, stay in the profession for 10 to 20 years.

This gap creates tension. Our preservice teachers are present-oriented for a good reason. That alignment makes sense as they want to get jobs in today’s schools. Our job is also to prepare them for the future, because they will teach and lead in future schools.

From my perspective, AI is one of those things we have to tackle head-on in a technology integration class, even if schools decide to ban it. My students point out they are not seeing AI use; I counter that we cannot know what will happen. These are one-year bans, and only in some places. Other districts are adopting AI fully, partially, only in high school, or for teachers only. There is a tapestry of options, but we do know AI seems here to stay, even if we don’t know the details.

One example is a task asking my students to create an assessment with and without AI, then compare the two. What did AI do well, and what did it not? How do effort and outcomes compare? They first experiment, compare, and finally reflect on the practical and the ethical boundaries of using AI in this way.

My preservice teachers need at least a sense of what AI is and what it can do right now, so they can start building mental structures for its ethical boundaries, its capacity boundaries, and how it might support their practice. At the teacher level, AI is a set of tools for producing instructional materials, building assessments, interpreting student work, and much more.

Once they understand AI, they can also start thinking about what they want to teach their own students about it, even in classrooms that do not use AI. We cannot ignore AI. It is a reality, and it is critically important that we engage with it as a topic.

We must do this because the gap between the real world (the world students experience outside the classroom) and the classroom is an unaccounted-for risk. The more students feel that the real world and the world of school are at odds, the less likely they are to take school seriously. They need to see alignment, alignment but not reflection.

A good analogy is civics education. We teach children about making democratic choices and how institutions work, even though they are not yet full participants in politics. They cannot vote. They cannot be elected. There is a whole host of things they cannot do, but they can still learn about them, so that as adults they are ready to participate.

I think about AI in much the same way. We have to teach students about AI because one day they will be full citizens in a society shaped by it. We want them to have the knowledge to participate in the discussions about AI, let alone use it in their everyday lives. For that to happen, their teachers have to be thinking about it now.

This is part of the challenge with bans. It is true that we worry about AI taking agency away from students, or making them think they know something they don’t because AI did their assignments. I accept that completely. It is a real risk, and we have to weigh it carefully.

At the same time, we have to teach preservice teachers how to use AI and start their thinking about how they might bring it into instruction “Do no harm” is a very real guide for that work. But harm can come from not acting as well. Bans just push the decision and action a year down line, but in wait of what? What clarity will we have next year?

The Challenge

The problem with bans is not that districts made a decision. It is that another year without AI in the classroom is another year when teachers don’t get to practice what is and isn’t appropriate, explore those boundaries, and find what works and what doesn’t.

This is where my world of research clashes with the world of classroom practice. This is one time when research cannot keep up with AI. AI is everywhere: it is in kids’ hands, in their homes, and on their phones. The faster we can get to best practices, the better.

Research is struggling to catch up because these realities shift so fast and the tools are already available to everyone. The research world, for better or worse, is not equipped to move at that pace. It is careful and deliberate, and it needs approvals from ethics committees, while the AI companies are running a major experiment on humanity. Research simply wasn’t designed for that.

I am not arguing that research should drop its ethical boundaries. It is just that these things are happening in parallel, and society, schools, and teachers are stuck in the middle, trying to figure out where these tools make sense and where they don’t, without the benefit of research. Performance, musical or otherwise, is part of the answer, part of the way we can evaluate impact.

What to Do Now?

When I return to my sister’s question about how to think about the future of education, whether in music, teacher education, or anything else, my first answer is that we, as teachers, have to keep exploring AI. What are the boundaries? For every aspect of our teaching, we have to ask whether AI is appropriate and how we might use it productively without risking the outcomes we want to see. Agency, cognitive growth, and grappling with hard problems have to be part of the picture, and no blanket yes-or-no policy can account for them.

This is my approach: look at each goal and each assignment, and ask whether this is a good place to use AI and what the boundaries should be.

They can do that only by experimenting. If teachers can’t use AI, they can’t experiment with it or try it out, and that is one more year lost. Even a one-year ban pushes that experimentation, and everything that comes of it, a year down the road. My sister and I will keep trying to figure it out, and if you are teaching in any capacity, formal or informal, so should you.

Running a Research Program With an AI Chief of Staff

 

I have always wanted project management software that could truly help me. Nick and I even recorded a couple of episodes of Not That Kind of Doctor about that search. I never found the holy grail. I always ended up back in my notes app or a notebook, and every few months I had to reboot the whole system. When vibe coding came around, I tried to build my own research tracker. I had a vision of different stages, and AI did produce a few products, but they worked like the older ones. They took quite a bit of work up front and were hard to keep up.

My new solution emerged from AI, but also from my own difficulty. AI pushed me to study it in a variety of ways, and ideas and collaborations multiplied quickly. What had been a minor problem became a major one.

Then I came across the idea of creating an AI Chief of Staff agent. Instead of vibe coding a structure, I simply asked Claude to create a project tracker. I built categories around the funnel from idea to publication. Then I set up a cycle with a Monday morning update on priorities for the week and a Friday prompt for updating what got done.

All of that information lives in a simple .md file written in plain language. Each project stays in a category until it moves forward to the next one, for example from research idea to IRB writing, or from analysis and writing to submitted. I usually work through the AI interface, but occasionally I open the file itself. It is completely readable because it is plain text.

Because the process gave me exactly the support I had been missing for years, I added things that were not strictly research projects in the funnel, at least not originally. The tracker now includes conference proposals, grant proposals, and even plans for the podcasts.

Each project also has a running notes file with next actions and progress updates, so the tracker can actually update itself and flag delays or, in the case of the podcasts, weeks without guests. Without much fanfare, working with this agent has made me considerably more productive while reducing my cognitive load.

What this week looked like

Here is a snapshot from this week. One book came out. Thirteen manuscripts are under review with no decisions. That sounds like progress until you look at the rest of the list.

• A paper I meant to submit on October 5 now has a new target of October 30.

• A pilot study has moved its start date three times. It will now run in my class in November because we did not have enough participants.

• One writing project had shown no movement through eleven review cycles. We set a reboot date of November 12.

• Another project is pushed to December 1.

• A journal just asked for another round of revision on a paper I thought was nearly done.

• One data collection site launched. The other is still waiting on a consent form.

Two new projects also showed up. One is a pilot tied to a grant proposal that was not funded this round. The other is in such an early design phase that my tracker had no category for it, so we created one.

Made with ChatGPT 5.6

None of this is unusual. This is what a wide research program looks like from the inside, and it rarely matches the CV, which is always a lagging indicator.

What my Chief of Staff can and cannot do

My agent sets reminders, helps me set priorities, and gets me ready for the next step. My thoughts are more organized and my actions more purposeful. What it cannot do is reduce the friction with the real world. It cannot recruit participants, hurry the IRB, or speed up my grant reviews.

Since I started my project tracker, I stopped using my notes app and notebooks, and I no longer feel the urge to find another productivity app. There is no special maintenance or buildup. I just sit down when I have a few minute and say- let upadte my tracker lets go one category at a time, that's it. Sometimes I do it with a voice interface and sometimes typing but the update is in plain language and with my running notes connected most of it is automatic with me just making sure it is accurate.

What I gain is visibility into both successes and delays. I can see the whole picture while still focusing on one assignment at a time during the work week. The other benefit, for someone like me, is seeing the full scope of the work. I understand what comes next, and I say yes to fewer things. My long list of articles under review gives me pause. Each one will likely come back for revisions at least twice and then become a priority, so I need to make sure I have the bandwidth to handle them. Call it self-regulation with AI.

The Order Matters More Than the Tool

 Over the past few weeks, I kept seeing the same move in places that have nothing to do with each other.

For the past year, our Design Futures workshop has started the same way. Participants take a piece of paper and sketch a bicycle built for a Nebraska winter. Then they snap a photo of the sketch and ask AI to reproduce it, sometimes from the drawing and sometimes from a written description. By the time AI enters the process, a lot of thinking has already happened. Sometimes the results are fantastic. Sometimes the AI hands back a bike that floats above the pavement, not by intent, but because that is what it perceived.

Last week we interviewed Nick Husbye on the podcast, and he described something curiously similar. Nick asks his preservice teachers to draft a lesson plan by hand. When they have a draft, they let the AI come in and critique the plan. What Nick teaches is that AI can be a very good critic, and that the teacher still has the agency to say, “I hear this critique, but it doesn’t apply here, and here’s why.”

The third example comes from our work with Southeast Community College, where we are asking Writing Center tutors how they work with AI. One tutor described a simple structure they give students.

1. Start with the rules. Know what your instructor allows so you don’t break them.

2. Use your own words.

3. Only then bring in AI to critique, improve, and suggest.

Finally, in Tina Shetabi’s work on using AI in the fashion design process som students reported that starting with AI gave them a strong sense that it is not their work. When they started with their own sketch, they felt more ownership.

Created with ChatGPT

The pattern

These are four examples from the last few weeks, and there are more. None of these people coordinated. Each one puts human thinking first and AI second, sometimes third. Each treats AI as a critic or collaborator that extends the existing work or points out what isn’t working. The ideas stay thoroughly human. As Nick puts it, AI should amplify your thinking, not export it.

Why the order matters

All four cases involve novices, people entering a domain they are becoming experts in but are at the beginning stages. That might mean designing a bike, writing a lesson plan, drafting a paper in a writing class, or, designing a garment. For novices, the priority is learning the domain and thinking it through.

A while ago I called this the novice problem. Novices are less likely to develop if they start with AI. We want them to start with their own thinking, let AI mirror back what is working and what isn’t, and then iterate. Some of that iteration can happen with AI, but in many cases I want them to go back and iterate with their own thinking.

The novice’s first draft does two things. It gives them something to compare the AI against, and it gives them a sense of ownership.

When students start with AI, they tell us they don’t feel ownership. Their sense of agency is taken away. They often go with whatever the AI made, and they become less and less critical. We want students critical enough to compare what they intended with what is on the page, and to learn from that comparison instead of being replaced by it.

The bikes show how this works. Students asked AI to add cup holders, which is a fine idea. Some kept pushing until the bike had 30 cup holders in ridiculous places. They knew immediately it was funny and implausible. Because AI will go as ridiculous as you ask, stretching it shows students quickly where it doesn’t understand the physics of a working bike or a wearable garment.

That signal leads to metacognition. Even as novices, they are the ones who can evaluate the result, because the work started with their intent.

What this isn’t

This is not an argument against AI. In each case, the AI step adds real value. It also isn’t a universal rule.

The sequence lines up with decisions universities are making about when to bring AI into the curriculum in a more organized way, including the debate in computer science education about when and how much to introduce it. In most of our interviews with professors and students, both groups say the same thing. In entry-level classes, AI makes little sense as the creator. It becomes a crutch because it is too good, and students build up cognitive debt. Once learners move past that stage, the roles can shift. They need to learn how to use AI when they’re stuck, when they lack ideas, or when they need to do research. There are times when AI should go first.

What schools can do

Most AI policies, in K-12 and higher ed, focus on which tools are allowed and what counts as acceptable use. That’s fine. Many policies also leave a lot of agency to instructors, which is great, but instructors still need a framework to work from.

The better question is about sequence. What do students do, and in what order? The answer depends on how novice they are and how much expertise they bring. This changes the policy conversation. A tool can be allowed at one point in the process and not another, with a different intent at each stage.

A human sketches the bike, drafts the paper, or designs the garment. Then AI comes into play, because we can iterate much faster with it. We can ask for small changes or big ones, extend the thinking, or get a critique. Everything is judged against the original intent. That keeps the learner in control, even as a novice, and keeps the product connected to the person who created it.

Three practices make this work.

Model it yourself. Nick models his own failures with AI, and I try to do the same. I show what I used and where it didn’t work. That gives students permission to critique AI without rejecting all of it. It isn’t all in or all out. Sometimes I dump what the AI produces. Sometimes I’m amazed. Usually I’m somewhere in the middle. And sometimes I say out loud, “This isn’t very good. I prefer what I made myself, and I’m not sure AI was worth using here.”

Make comparison the assignment. Ask students to compare their intent with the final product and explain what they learned from the critique. That metacognitive move is where much of the learning happens.

Grade the comparison. If your students respond to grades, put significant weight here. AI output quality varies, so grading only the product partly grades the AI. Grading the comparison grades the thinking.

Where have you seen it work?

Nick’s advice to his students is simple. Write first. Use AI, but use it a little later.

These are observations from all of us grappling with how to teach in the age of AI. So my question is if you seen this sequence work, and where have you seen it fail?