We’re about to have the same fight about AI in schools that we’ve been having about devices. And if we’re not careful, we’re going to get it wrong the same way.

The screens debate has settled into a pretty tired binary — devices good or devices bad, embrace or remove. The AI debate is shaping up identically: let kids use it, or block it, detect it, punish it. Both miss what we should actually be talking about, and they miss it the same way.

When schools started rolling out one-to-one device programs, the case for them was mostly made in language like modernizing the classroom and preparing kids for the twenty-first century — phrases that did a lot of rhetorical work without committing to much. The drivers were a mix of vendor pressure, federal funding tied to tech deployment, equity arguments about the digital divide, and a sense that schools couldn’t keep using chalkboards while everything else moved on. Personalized learning got named sometimes, but the devices usually arrived first and any theory of how they’d improve learning came later, if at all. L.A. Unified’s 2013 iPad rollout became the cautionary tale precisely because nobody could quite say what the devices were supposed to make better.

What was real underneath the modernization rhetoric was a broader sense that our schools weren’t performing as well as we wanted them to, paired with the hope that the technologies transforming every other industry could transform education too. The hope was big but the plan was vague — and most of it hasn’t panned out. But the underlying concern was real — and still is. Twenty-five kids in a room, different speeds, different starting points, different learning styles, one teacher trying to move them all through the same material at the same pace. That isn’t the most effective way to teach kids, and we’ve known it for decades. Fast learners get bored. Slow learners fall behind. The case for trying something better was obvious long before anyone put a tablet in a classroom, and it persists after we take the tablets away. The device-skeptics keep skipping past this: removing the laptops doesn’t address the learning challenges that made us reach for new tools in the first place. The kids the one-size-fits-all model already wasn’t serving are still not being served — now without the tools that might have helped, if anyone had been clear about how.

That’s the argument I want to make about AI too. And the same “modernize the classroom” rhetoric is already coming back, just with AI swapped in for tablets: kids are going to use AI in the real world, so schools have to teach them how. I don’t buy it. Kids learn the tools that matter to their lives without school teaching them — that’s how Google, YouTube, smartphones, and every consumer technology of the last twenty years got absorbed by anyone under twenty. They’ll figure out AI the same way. The reason to bring AI into classrooms is not exposure. It’s whether AI can make teaching and learning better, and what we have to do to keep the obvious downsides from swamping the gains. Those are the questions worth arguing about: what improvement are we aiming at, what risks are we taking on, and how will we know if we got the trade-off right?

Personalized instruction is what works

Bloom’s 2 sigma study from 1984 is one of the most-cited pieces of education research for a reason: in those experiments, one-on-one tutoring with mastery-based instruction produced outcomes roughly two standard deviations better than ordinary classroom teaching. The numbers have been argued about for forty years, but the broad direction holds — high-quality personalized instruction works dramatically better than the lecture-and-worksheet model most of us grew up with. The reason every kid doesn’t have a tutor isn’t that we don’t know it helps. It’s that high-quality tutors are expensive and there aren’t enough of them.

That’s the hypothesis behind what places like Alpha School are doing with AI — that the bottleneck on personalized, mastery-based instruction has always been supply and cost, and AI uniquely changes that math. I don’t know whether Alpha is right. They’re small, their student population is unusual, their published outcomes are limited. They could be wrong about the model, or about the implementation, or about whether AI tutors actually replicate what made human tutoring work in the first place.

What I like is that they’re asking the right kind of question. They have a clear theory of how AI can make learning better — give every kid the personalized, mastery-paced instruction that we have decades of evidence for but have never been able to scale — and they’re testing whether that theory holds. That’s the conversation I’d like to see more of. Not “AI yes or no,” but “where do we believe this can improve the learning experience, and is it doing that?”

What we actually got wrong with screens

I don’t think the lesson of one-to-one device programs is technology doesn’t belong in classrooms. It’s something more specific.

Some of the harms we’ve seen from devices in classrooms weren’t fully foreseeable at the time of deployment. When laptops first showed up in schools, social media barely existed. TikTok wasn’t a thing. YouTube was small. Recommendation algorithms hadn’t yet figured out how to pull a kid from an educational video into a four-hour rabbit hole. The balance of upsides versus downsides shifted as the surrounding technology evolved — and the deployment didn’t shift with it. The devices we put in kids’ hands also matter: a Chromebook locked to school apps is a very different proposition from an iPad with social media one tap away. The shape of the technology, not just its presence, drives the trade-off.

Two things I take from that.

First, the trajectory of new technology is hard to predict. With screens, the upsides looked clear and the harms emerged later — some unforeseen, some flagged early and dismissed because the upside was more exciting and the incentives all pointed at deployment. With AI the order is partly reversed: the cheating headlines and attention costs are arriving before we’ve seen any clear improvement in learning outcomes. The pattern doesn’t generalize, but the unpredictability does. Anyone telling you they already know how AI in schools lands is wrong.

Second — and this is the one I keep underweighting myself — it is much easier to add technology to a school than to remove it. Once one-to-one became universal, once homework lived on Blackboard, once digital was the default, the cost of pulling back was enormous. That asymmetry should make us more cautious about going all in, not because the tech is bad but because reversibility matters and we usually only learn what’s working a few years in. The version of “be adaptive and iterate” that I find most honest is: pilot small before you go big, measure outcomes you actually care about, and treat the initial deployment as a hypothesis you’re testing, not a future you’re committing to.

The question I want us to be asking

Stop asking whether AI belongs in classrooms. The yes/no framing is a trap, the same way the screens yes/no framing was a trap.

Ask instead: how do we believe this technology can make learning better? Where is it well-suited to deliver that improvement? What guardrails do we need to put around it? What outcomes are we measuring, and on what timeline are we willing to revise the deployment if the data tells us we got it wrong?

Those are slower questions. They don’t make for a good X argument. But they’re the questions where there’s real work to be done, and they’re the only ones that have a chance of producing schools that actually serve the kids in front of them.