I Fear That The AI Adoption Gap In Schools Is Compounding

The Questions Get Hard

The surface-level read is that kids can build things with AI now. Fine. But the deeper issue is that our whole notion of what counts as a skill is starting to wobble. If AI can generate production-grade code from a plain English prompt, then the skill isn’t writing the code. The skill is knowing what to ask for, understanding the problem well enough to describe it precisely, and being able to evaluate whether what comes back actually solves it or just looks like it does. That is a completely different kind of literacy, and I'll be blunt, most curricula haven't started to wrestle with it. We're still drilling syntax when we should be drilling judgment. And this isn't just about computing. The same principle applies right across subjects.

There’s a line I keep returning to when I talk with educators. Outsource your doing, not your thinking. An educator collaborating with AI to create a lesson plan on a Tuesday evening is outsourcing the doing. The professional judgment, the part where you decide what your class actually needs, how to sequence the learning, which misconceptions to head off, that stays with the teacher. A student pasting an essay question into ChatGPT and handing in whatever comes back is outsourcing the thinking. The entire purpose of the exercise disappears. Getting that distinction right is, I think, the central challenge for education right now. I was with a group of educators in Canada last week and we spent a good chunk of our session on exactly this question. Where’s the line? Do we even know yet?

A Private Equity Firm’s Failure

Let me share a story that puts some meat on this.

Permanent Equity, a private equity firm, spent the whole of 2025 trying to build AI agents. Dozens of experiments, each one consuming over a hundred hours of work. Led by a PhD technologist they’d hired specifically for the job. In their 2025 annual letter, the firm’s leadership admitted that "the results have been shockingly poor compared to expectations" and said they were pulling back on agent ambitions entirely.

Then January rolled around. New tools came out. Two people, neither of them coders, finished in twenty minutes what months of professional work hadn’t been able to crack.

Now picture that in a school. A head of department who has never written a line of code could build a bespoke student tracking system during a free period. A classroom teacher could throw together an interactive resource tailored to what their students need on Thursday morning. The barrier to making things isn’t just lowering; it is falling through the floor. And when building stops being hard, the question changes. It stops being "can we make this?" and becomes "should we?" That's not a technical question. That's a question about educational purpose, about values, and it's the sort of judgment call no algorithm will ever make for you.

The Agent Shift Is Real

The other big story from January was agents. I want to pick this apart a bit because "agent" is about to become the most overused word in education this year, and most people will use it wrong.

An agent isn’t a chatbot. It’s an AI system that goes out and does things on your behalf. Sends emails, navigates apps, and takes actions on your computer. OpenClaw, an open-source agent built by developer Peter Steinberger, went viral in late January. Scientific American described it as “AI with hands”. CNBC reported it picked up over 60,000 GitHub stars in just 72 hours. People were buying separate Mac mini computers just to give these agents room to work around the clock.

Then someone built a social network for the agents. Called it Moltbook. NBC News reported that within days, more than 37,000 AI agents were active on the site, with over a million humans visiting to watch. Andrej Karpathy, the AI researcher and OpenAI co-founder, called it “genuinely the most incredible sci-fi takeoff-adjacent thing I have seen recently”. That particular experiment may well burn out fast, security researchers have already spotted real vulnerabilities, but the broader direction of AI that acts rather than just chats is not going away.

Why should schools care? Because right now, pretty much everything schools do with AI is conversational. A student types a question. An educator bounces ideas in a chat window. Agents are different. Agents actually do things. An agent could track student progress across five or six platforms and ping a teacher when someone is quietly falling behind. It could handle the grunt work of differentiation, pulling resources, adjusting difficulty, managing the kind of logistics that currently eat into educators’ evenings. The teacher gets freed up to do what they actually got into the job for. The conversations, the relationships, those messy unpredictable moments where a kid suddenly gets something and you can see it land.

But this is where it gets dangerous. There is a very thin line between an agent that says "heads up, these three students might need extra support this week" and one that quietly rewrites a child’s learning pathway without the teacher ever knowing it happened. The first one makes teaching better. The second one could replace it. Schools need to think carefully about which side of that line they want to stay on.

The Line Between Efficiency And Autonomy

Kevin Roose, tech columnist at the New York Times, posted something on X back on January 25 that maps directly onto this problem. He described a "yawning inside/outside gap" in AI adoption. On one side, you have people running multi-agent setups and consulting AI before every major decision. On the other hand, people who still cannot get a basic sign-off to use a chatbot at work. That describes education perfectly right now. Some schools have educators weaving AI into their practice every day. Others are still running on a blanket ban, and the only conversation happening is how to stop students cheating. Which is a bit like standing in front of the internet in 1998 and deciding the biggest issue is kids copying from websites.

The Gap Compounds Every Day

This is the part that should worry school leaders most. The gap between schools that have started working with AI and schools that haven’t is compounding. Not because the technology is getting harder to adopt, it is actually getting simpler all the time, but because the institutional muscle memory of working alongside AI takes time to develop. You cannot install culture like you install software. Culture shifts at the speed of trust, and trust comes from experience. From trying things, getting some of them wrong, talking about what went wrong, adjusting, and trying again. There is no shortcut for that process. You just have to start.

The schools that are getting this right didn’t attempt some massive overhaul in September. They went small. An AI sandbox where educators could experiment without worrying about getting things wrong. A working group of willing volunteers, some schools call them AI Mavericks, which I quite like as a name, testing tools and feeding back to colleagues. Informal conversations, sometimes literally at the coffee cart in the staffroom, surfaced what educators actually needed rather than what senior leadership thought they needed.

I have worked with and interviewed a lot of these schools. Alpha Schools, who are using AI to remotely teach core knowledge. Calvert Hall School in Baltimore, who have set up an AI working group that tests out new AI tools and evaluates them against their needs and strategy. What those places have built over the past year, the institutional knowledge, the shared vocabulary, the comfort with the tools, no latecomer can buy that off the shelf. It took months of quiet, unglamorous work, and the advantage it gives them grows every day.

What To Do Now

The agent era is not on the horizon. We are already in it. The tools for building, creating, and automating are within reach of practically everyone now, including your students. The question for schools is not whether to engage with this stuff. The question is how to engage without losing the things that actually matter. Human judgment. Human creativity. The ability to care about a young person in a way that no machine ever will.

Outsource The Doing. Guard The Thinking.

If you are a school leader looking at all of this and not sure where to start, start with your people. Not your tech budget. Not a shiny platform purchase. Give your educators time and genuine permission to explore, with guardrails but also with trust. Because here is what I have seen play out in school after school: educators who get the room to experiment do not dig their heels in. They become the ones driving the change.