No, AI won't deskill students

On UChicago and Claude for everyone

I’ve been travelling a great deal lately, which has kept me from the blog, and, if I’m honest, from finishing much of anything. I’ve started many blogposts and completed none, begun drafting several papers and closed out none of those either. I am once again in that recurring phase of life in which I try to work out where to go next while waiting, with rather less patience than I’d like to admit, to hear back from a handful of submissions whose statuses I check on the various editorial managers far more often than checking could possibly help.

This morning, though, prompted partly by an exchange with the always excellent Eric Schliesser, I found myself wanting to join the now longstanding debate on AI and deskilling, which I have found, for the most part, misleading. And misleading in a fairly specific way, which is what I want to set out here.

The thing that finally made me sit down and write was a piece of news. On 2 June the University of Chicago announced that it is giving all of its students, and all of its staff and faculty, full access to Claude. Staff get it from July, and students will have it before the autumn term begins. Chicago is not the first university to do this. Dartmouth has had much the same arrangement for about a year, and Berkeley and Duke have made their own deals with other AI companies. But Chicago is a serious place, not much given to chasing fashions, and the way its president explained the decision has stuck with me. The university’s job, he said, is to teach students three things: how to think with these machines, how to think without them, and how to think about them. I think that is exactly right, and I want to come back to it at the end.

The reaction, though, was the one you would expect, and a good part of it was alarm. Give students a machine that will write their essays for them, the worry goes, and they will never learn to write, or to argue, or to think for themselves. They will hand in work they did not do and cannot understand, and a whole generation will come out the other side hollowed out. I understand the worry, and I do not think it is silly. But I think it rests on a muddle about what a skill actually is, and once that muddle is cleared up, most of the alarm has nowhere left to stand.

Start with the word everyone is leaning on: deskilling. To be deskilled is to lose a skill. So what is a skill? We value skills for all sorts of reasons, and I do not want to wave any of them away: for the independence they give us, for the plain pleasure of being good at something, for the way they tie us to a craft or a tradition. But the deskilling worry is not really about those reasons. It is a worry about losing a capacity we still need, one that still does some real work for us, and that is the kind of skill I want to take on here. Once you fix on that meaning, something strange about the worry comes into view. Handing a task to a machine is not, by itself, evidence that the underlying human capacity has lost its point. It raises the question whether that capacity is still needed, and if so where.

Think about the calculator. Nobody says, or should say, that the calculator deskilled us, even though hardly any of us could now do long division on paper with much confidence. The reason we should not say it is that the need to do long division by hand simply went away. The ability went away along with it, and we lost nothing we still had any use for. Calling that deskilling would be odd. It would mean mourning the loss of something we were rather glad to be rid of. And the same thing is true far more widely than we like to admit. When a tool takes over a job we genuinely no longer need to do ourselves, the disappearance of the old ability is not a loss at all. It is just the need leaving, and the ability quietly following it out the door.

The dilemma

There is a cleaner way to see all this, and it sits right at the centre of the argument. Take any task you might be tempted to hand to an AI: writing an essay, solving an equation, drafting some code, summarising a paper. Now ask one simple question about it. Is the AI good at this task, or is it not? There are two clean cases, and then one messy case where the real educational problem lies.

Suppose, first, that the AI is not good at the task. Then you still need to be able to do it yourself, or at least to tell when the machine has got it wrong. And here is the part that matters: because you need that ability, the system that assesses you has a natural way of making you build it. Picture a student who leans on a shaky AI and never bothers to learn enough to catch its mistakes. That student hands in weak, error-filled work, and is marked down for it. The wish to avoid that is what pushes the student to learn the skill properly. As long as bad work is recognised as bad work, the ordinary desire to do well keeps the skill alive, machine or no machine. So where the AI is unreliable, the skill does not vanish. It is kept safe by the plain fact that you still need it.

Now suppose, instead, that the AI is good at the task. Genuinely good, reliably good. Then, if we are honest, you probably do not need the skill anymore, in just the way that you no longer need to work out a square root with pencil and paper. The ability has become optional. You can still learn it if you enjoy it, or because it has some independent value, but nothing is lost by letting it go simply because it is no longer required. Nothing was really demanding it of you in the first place.

Put the two cases side by side and most of the worry drains away. If you still need the skill, the system that judges your work has a way of making you build it. If you no longer need it, then losing it costs you nothing. There is a third case, though, and it is the one people really have in mind when they worry about AI. A student might still need a skill and yet go through an education that lets them put off finding out they need it until it is too late to learn it. The need is there, but nothing makes them feel it in time.

Now look at what is actually causing the harm there. It is not the machine. It is a way of marking work that lets fluent, empty answers pass and never makes the student find out what they cannot yet do. And this is an old failure. Students have always copied, crammed, leaned on templates, nodded along to feedback they did not understand, and learned to give a marking scheme what it wants without ever learning the thing behind it. None of that needed AI. So when people warn that students will use AI and pass without learning, the real work in that sentence is done by two small words: and pass. If a student can do shallow work and still pass, the university already had a problem, long before any machine turned up. A lot of what we call worry about AI is really worry about how we mark and assess. AI has not made it newly possible to get through a degree without understanding. It has only made it much harder to ignore.

Judging is its own skill

Behind the worry about assessment lies a deeper one, which is likely the real engine of the alarm. When the machine is good, I said, the skill shifts from producing the work to judging it. This deeper worry says that this shift is a trick, because you cannot really judge work you could not have produced. To tell whether an essay is any good, the thought runs, you have to be able to write one; to catch a broken proof, you have to be able to build one. If that were right, a student who lets the machine produce would lose the power to judge along with it, and end up able to do neither.

This runs three different things together. There is the skill itself: telling a good argument from a bad one, a sound proof from a broken one. There is producing the work: coming up with the argument or the proof yourself. And there is the way we usually test the skill: the essay handed in and marked. We treat the three as one thing, and they are not.

Take producing and judging first, because that is the centre of it. They are different skills, and they come apart all the time. A football coach is often a mediocre player. A good editor can say exactly what is wrong with a novel and could never write one. A critic’s taste routinely outruns anything they could make themselves. The power to judge does not follow automatically from the power to produce, and a person can have a great deal of the one with very little of the other.

What is true is narrower. Producing is one of the ways we learn to judge. When you work out a proof of your own, you are forced to weigh each step as you take it, and that steady pressure trains the judgment. But producing is a way of training the skill, not the skill itself, and it is not the only way. You can learn to read and judge proofs without ever being made to write one, so long as something else does the forcing, so long as you are made to judge and corrected when you judge badly. Producing is one road to judgment. It is not judgment, and it is not the only road.

Even in philosophy, the essay was never the skill itself. It was a way of making thought visible, so that it could be tested, corrected, and judged. Dialogue, oral examination, seminar exchange, and written argument are different vehicles for that same underlying discipline. We forget this constantly. We watch a student stop writing essays by hand and conclude that something vital has gone, when all that may have changed is the vehicle.

So this deeper worry falls apart. When the machine takes over producing, the skill that now matters, judging what it gives back, is not pulled down with it. Judging was always a separate skill. Producing was only ever one way of teaching it, and students can be taught another way: by being made to judge the machine’s work, and corrected when they judge it badly. The task was never to keep students producing for its own sake. It is to make sure that something still makes them judge.

The exceptions worth keeping

Everything so far has pointed one way: offloading is, on the whole, safe, and the skills that matter look after themselves. I do not want to oversell that, though, because there are two plausible exceptions, and because they work in different ways, let me take them one at a time.

The first is about timing. Some things are simply learned better early, before a student has a machine to lean on. The worry is not that you could never pick them up later, because often you could. It is that the early years are when certain habits form most easily, and a student who hands everything to a machine from the start may never lay that groundwork down. So some early stages are worth protecting on purpose. That is not a reason to keep AI out of education. It is a reason to guard a few early stages while letting the rest become openly machine-assisted.

The second is the emergency, and its logic is different. Here the trouble is that the need for a skill can arrive faster than it could ever be learned. For a small number of jobs, the day comes when the machine fails and there is no time to catch up, so the skill has to be there already, built in the years when the machine was handling things and the person did not seem to need it. We do not deal with this by asking everyone to keep every skill, just in case, which would be both impossible and pointless. We deal with it the way we always have, by deliberately keeping certain abilities alive in the particular people who will need them. We train a few surgeons to operate when the equipment dies and pilots to fly when the automation quits, and we carry the cost because the stakes are high and the warning is short.

Different as they are, both exceptions ask for the same narrow thing: protect a few specific stages, and train a few specific people. Neither is a reason to be wary of offloading in general. They are the carve-outs we make on purpose, in the few places they plainly earn their keep, precisely so that everyone else can offload without a second thought.

Offloading is how we build new skills

I want to put this more strongly, because so far I have mainly argued that offloading does no harm, and, as I have argued elsewhere, I think it does positive good. Offloading is not the enemy of skill. It is the way skill keeps moving to wherever it has become useful, and that is a reason to want it, not merely to tolerate it.

When a capacity stops being needed, the effort that used to go into it does not simply disappear. It moves. The calculator did not leave the world with less mathematical ability in it. It freed people from grinding through arithmetic by hand and let them put that freed attention into harder and more interesting mathematics, and into a hundred things that were not mathematics at all. This is the ordinary pattern of every useful tool. You hand the old task to the machine precisely so that you can pick up a new one. A student who offloads the things AI now does well is not being emptied out. They are being freed to learn the things that sit on top: how to ask the right questions, how to weigh the answers, how to put strange materials together into something of their own.

So the case for handing students these machines is not just that it will not hurt them. It is that it clears the ground for them to build whatever comes next, and the more they let go of what no longer needs doing, the more room they have to do it.

Back to Chicago

Which brings me back to the Chicago president and his three things. Thinking with machines is the skill the world now mostly rewards, and we should teach it openly and without apology. Thinking without them is the narrow business I have just described: the few early stages worth learning the hard way, and the few jobs where someone must be able to carry on when the machine fails. And thinking about them is knowing, for any task in front of you, which of the two you are dealing with.

All of this lets us say precisely what AI does and does not do to a university, because the single word “deskilling” hides three very different claims. The first is that AI changes which skills are worth having. That is true, and it is no loss. It is just the need moving, as it always has. The second is that AI exposes assessments that were already weak measures of understanding. That is a real problem, but an old one in new clothes, and it was always going to need fixing. The third is the one that genuinely earns its worry. AI can take a weakness a university was quietly tolerating and make it acute, because it drops the cost of evasive work almost to nothing.

So the honest thing to tell Chicago, and everyone watching it, is not that the machine is safe, nor that it is dangerous. It is that the machine is a stress test. Where teaching already tracks understanding, handing students these tools pushes the work upward, toward judging, revising, and owning what the machine gives back, and that is simply education doing its job. Where teaching only ever rewarded a fluent surface, the machine will make that impossible to hide.

The sensible default is more offloading rather than less, with the burden on whoever wants to hold a skill back to show that it is one of the few genuinely worth protecting. Skill is not being destroyed here. It is moving to wherever it is still needed. Our job is to follow it there, and to make sure that our teaching and assessment can still tell whether anyone actually has it. Where a course cannot survive its students being handed a good machine, the machine was probably never the deepest problem.