If AI can do it for me, do I still need to know how?

In the last post, on piano practice, I found in the end that although AI clearly knows a lot about piano, it can’t necessarily teach someone who is actually playing.

The reason can’t be explained by “AI has no body” alone. Real teaching has to go through at least four layers:

Observe → Diagnose → Intervene → Verify

First see what really happened, then judge the cause, choose an intervention that suits this person, and finally keep adjusting based on the new results.

That’s the first kind of boundary I hit in this round of research: AI having knowledge doesn’t mean that knowledge can smoothly reach a real-world result.

Looking further, I started running into a very different kind of problem.

This time, AI may not have done anything wrong at all.

It can write my email well, organize my material, build the code, solve the problem, with results faster and more complete than if I’d done it myself.

So if AI can already do it for me, do I still need to know how?

I don’t think this question can be answered across the board.

Because in using tools, we’ve always been giving up some things we “must know how to do ourselves.”

I don’t need the ability to keep food fresh like a fridge, and I don’t need to work a sum out by hand every time before using a calculator just to keep up my mental arithmetic. Navigation means I memorize far fewer routes, and washing machines long ago replaced a lot of hand-washing skill.

AI is no different.

What really needs sorting out first is what I want left over once the thing is done.

I’d now roughly divide it into three kinds.

The first kind: I only need the result

These are the simplest.

Like tidying up meeting notes, converting file formats, extracting a batch of information, or writing a very ordinary admin email.

If what’s needed in the end is an accurate, usable result, and AI can reliably do it well, I’m happy to hand it over directly.

I won’t worry that because ChatGPT made an English email sound more natural, I didn’t “really learn to write this email.”

Because this email was never an English lesson.

One reason tools exist is to take away work that no longer needs to be done by people in person.

So I don’t really agree with a sweeping claim like:

the more you depend on AI, the more people are losing abilities.

Which abilities are lost, and why you still need them, is the more important question.

The second kind: I don’t have to do it myself, but I need to understand it well enough to judge

Some things I’m also willing to hand over the execution of, but I can’t completely lose the ability to understand the result.

Building products with coding agents is a very clear example for me now.

I don’t think I first have to become an engineer who can write production code independently before I’m entitled to have an agent build products for me.

That it can build them is real value.

But I still need to understand some things.

Like whether a technical approach will change product behavior, how much permission an agent should get, what trade-offs an architecture change brings, what passing tests actually proves, and which things haven’t actually been verified yet.

Otherwise what I’m handing over isn’t just coding execution.

The ability to judge “was this done right” goes along with it.

So what really needs keeping here isn’t necessarily execution ability, but enough judgment / oversight capability.

I don’t need to do every step myself.

But I at least need to know when I can relax, when I should keep asking, and when something is actually beyond what I can review.

The third kind: for some things, the ability itself is the result I want

Only here does the question really become different.

I practice spoken English because I want to be able to speak it myself in my next interview.

I practice piano because I want my own hands to really be able to play.

Students learn math not so that today’s page of exercises ends with all green check marks, but so that when they meet similar problems later, they can handle them themselves.

At this point, “AI helped me do the task better” and “I learned it myself” are no longer the same thing.

Very concrete evidence on this has started to appear in education research.

Doing better with AI’s help doesn’t mean the ability has stayed with the person

In 2025, PNAS published a randomized controlled trial in math with nearly 1,000 high school students.

The researchers designed two GPT-4-based AI assistants.

One was fairly close to how ordinary ChatGPT is used: when students got stuck on a problem, they could just ask it to help solve it.

The other was specifically constrained to fit teaching goals: it leaned toward giving hints rather than handing over answers, and used teacher-prepared correct solutions, common mistakes and feedback approaches to guide students.

The experiment also had a group of students without generative AI as a control.

In the AI-assisted practice phase, both AI groups performed clearly better.

The ordinary GPT group scored about 48% higher than the control group, and the pedagogically designed GPT Tutor group about 127% higher.

Looking only at this, it’s of course easy to feel AI helped learning.

But the experiment had a next step.

The researchers took the AI away and had students take an exam on their own.

This time, the students who had used ordinary GPT actually scored about 17% lower than the control group that hadn’t used AI.

The pedagogically designed GPT Tutor group basically eliminated this negative effect, but in the AI-free exam it didn’t significantly beat the control group either.

So within the same experiment, a very important distinction appeared:

with AI present, students’ task performance can rise clearly.

But once AI is removed, people’s independent ability doesn’t necessarily rise by the same amount.

When the researchers looked further at how students used ordinary GPT, they also found many were more inclined to ask it directly for answers.

That isn’t hard to understand.

If what students really need to practice is reading the problem themselves, recalling knowledge, trying, making mistakes, then revising based on feedback, AI skipping straight past these processes and handing over the right answer can of course boost immediate performance.

But it may also do part of the work that was meant to happen in the person.

This doesn’t mean you should use less AI when learning

Another study filled in the other half.

Also in 2025, a Harvard research team ran a randomized experiment with 194 participants in an undergraduate physics course.

Students learned the same content either through active learning in a traditional class, or with a specially designed AI Tutor.

This AI Tutor wasn’t an ordinary chatbot simply put in front of students.

The team designed the interaction around some principles from education and cognitive science, having students participate actively, receive information gradually, get hints and feedback when needed, and learn at their own pace.

Across the two lessons covered by the experiment, the AI Tutor group’s immediate learning gains were higher than in-class active learning, took less time, and students reported higher engagement and motivation.

This study of course has its limits.

It took place in one Harvard undergraduate physics course, tested only specific content, mainly observed short-term learning outcomes, and didn’t show that students still remembered more months later.

But put alongside the earlier math experiment, I think it’s already enough to show something very important:

the key isn’t “whether there’s AI” while learning.

It’s:

what AI does for the student, and what it leaves for the student to do themselves.

AI can skip straight past the thinking process and hand over the answer.

It can also watch students try on their own, and give a bit of help when they’re really stuck.

On the surface both are “AI tutoring,” but the actual product goals are completely different.

If “building ability” is the goal, some seemingly inefficient steps can’t all be removed

When building ordinary productivity products, we naturally aim to reduce friction.

If it can be done in one step, don’t make users take three.

If it can be autofilled, don’t make them type it.

AI pushes this direction much further: many things can now jump straight from a question to an answer that looks complete.

But if the goal of the task is learning, some friction may itself be the learning process.

• Thinking first yourself.
• Retrieving things from memory.
• Trying an answer that may not be right.
• Discovering what you don’t know.
• Doing it again after getting feedback.
• Switching to a new scenario, and seeing whether you can transfer it.

These processes look less efficient than “directly generating the right answer.”

But if this ability is exactly what I want to train, removing all of them may optimize away precisely the part I’m meant to learn.

So I now think good learning AI sometimes needs to hold back a bit:

not finishing it for me as fast as possible, but letting me finish it as much as possible.

Thoughts on “expression atrophy”

At the end of the first 100 days of this log, I wrote about a very personal feeling.

I found that organizing a full passage of writing on my own seemed a bit harder than before.

Meanwhile, AI is getting better and better at catching my incomplete expression. A few scattered thoughts, some badly organized sentences, can all be thrown at it first and turned into a fully structured piece of writing.

I don’t know whether this change was caused by AI.

My own feeling of course can’t prove causation.

But now I ask myself a different question:

which kind is fully expressing myself, for me?

A lot of text production actually belongs only to the first kind.

An email, an explanation, a clearly formatted document — I only want the result, and can use AI heavily.

But for me expression isn’t only text production.

In real life, I still have interviews, still have to discuss complex problems with people, and still have to make another person understand me when there’s no AI in between.

That means expression itself still has independent practical value.

There’s another layer too, which I can only treat as my own observation for now, not a conclusion research has proven:

sometimes I don’t first fully form a view and then translate it into language.

Slowly putting something vague into clear words is itself part of my thinking.

Halfway through writing, I discover two things actually have no logical connection.

Trying to explain a view, I discover I don’t know why I believe it either.

Telling it to someone, I realize a piece of evidence is missing here.

So what I really worry about isn’t:

will my sentences be less beautiful than what AI writes in the future.

It’s:

if every idea that hasn’t taken shape yet gets handed to AI very early to complete for me, am I also going through less of the process of thinking things through myself?

For now I don’t have enough evidence to answer this.

But at least I know this is an ability I still want to keep.

So now I first ask: once this is done, what do I want left over?

This is the second fairly practical judgment this round of research gave me.

Facing something that’s getting easier and easier to hand to AI, I first put it in one of three places.

I only need the result

Then outsource it freely.

There’s no need to dress every task up as a “learning opportunity.”

I need to keep judgment

Execution can be handed over, but enough understanding, verification and decision-making ability need to stay.

I don’t necessarily need to write the code myself, but I need to know when I can’t just take the agent’s word that it’s “done.”

What I need is this ability itself

Then AI should take part in the practice, rather than replacing all the parts that really need training.

What finally matters isn’t only:

how well I do with AI beside me.

It’s also, once the help gradually decreases:

what has really stayed with me.

I think this is an easily overlooked boundary of AI in this second post.

It’s very good at lending us its abilities.

But:

AI’s abilities don’t automatically become my abilities just because I’ve used them.

If I only need the result, this isn’t a problem at all.

If I need to keep judgment, part of the understanding and verification has to stay.

If forming a person’s ability is the goal itself, the product may even need to deliberately keep steps that AI could do directly but that are still necessary for learning.

This also leads neatly to the last post in this research series.

In the second case above, I said:

execution can be handed to AI, but important judgments stay with people.

That’s already a very common design in many AI products today:

Human in the Loop.

AI does it first; a person checks at the end.

Sounds very reasonable.

But in the next post I want to chase a more troublesome question:

does having a person in the loop mean they can actually catch the problems?

About this round of research

This is the second post in the research series “What AI still does poorly.”

The first discussed how, when AI has to guide a real person through a skill, any layer — from observing reality and judging causes to intervening and feedback — can become a limit.

This post looks at another boundary: when forming a person’s ability is the goal itself, AI raising tool-assisted performance doesn’t automatically prove learning happened.

This post mainly uses two randomized controlled trials.

Bastani et al.’s high school math study covered nearly 1,000 students, comparing practice performance with AI present against an independent exam after AI was removed, so it can observe assisted performance and later learning outcomes separately.

Kestin et al.’s study tested, in a Harvard undergraduate physics course, an AI Tutor designed specifically around teaching principles, providing evidence pointing a different way: AI doesn’t inherently hinder learning; how the system is designed, and what it does for students, is one of the key variables.

The two experiments differ in participants, subjects and evaluation periods, so you can’t simply compare which is “more right.” Put together, what’s most valuable to me is actually this distinction:

AI making a task go better and AI helping a person form an ability need to be verified separately.