Use AI to Learn Without Getting Worse at It

Answer-giving AI left learners worse off once it was gone. Three setups built to keep the thinking with you, and a way to check what stuck.

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If you have used ChatGPT this month to get through something you are learning (a spreadsheet formula, a new codebase, the rules of a new job), it probably felt like progress. The answer was always one message away.

A team of researchers tested it. Nearly 1,000 Turkish high-school students practised maths, some of them with a ChatGPT-style assistant beside them. That group's practice scores rose 48%. Then the assistant was taken away for the exam, and they scored 17% lower than students who had never had it. The analysis the authors registered in advance puts the drop about a third smaller, but it is still a drop. In the chat logs, students had often asked for the answer and copied it.

Easier practice is not evidence that you learned the thing. The effort you skip is often the part that was teaching you.

When you only need the answer, take it. This is about the times you need the skill, because you will have to do it again with nobody to ask.

Experienced programmers too

The Turkish students were teenagers. In January, Anthropic published a trial with 52 Python programmers, recruited through a crowd-work platform, learning a Python library that was new to them. More than half had seven or more years of coding experience. Half of them worked with an AI assistant; afterwards everyone took a quiz with no AI allowed.

The AI group scored 4.15 points lower on the 27-point quiz, roughly 15 percentage points, and they were not significantly faster. They learned less and got little or no time back.

Who did the thinking

Some of those programmers scored well anyway, and they had used the assistant differently. Those who handed it the work, leaned on it more as the task went on, or had it debug for them averaged 24% to 39% on the quiz. Those who asked it conceptual questions averaged 65%, about the same as the group with no AI. The best result, 86%, came from two people who let it write the code and then questioned it until they understood what it had written. The groups were small and sorted after the fact, so treat them as a clue.

A separate team found the same split. In pre-registered experiments with German university students learning to code, the researchers found no effect of a chatbot on learning overall. Inside that average, students who used it to generate solutions covered more topics but understood less, and students who used it to ask for explanations understood more. That split was exploratory. In the same study, 42% of requests for a solution came before the student had tried at all.

Only the Turkish trial tested the design directly. Its second AI group had an assistant told to give hints, with the correct solutions loaded. Their practice scores rose 127% and the exam damage disappeared, though they did no better than students with no AI. Two other trials show well-designed tutors teaching well without isolating who did the thinking. At Harvard, 194 physics students each learned one lesson from a tutor told to encourage a first try and release the solution one step at a time, and another in an active-learning class; median gains with the tutor were more than double the class's, though it answered whenever a student demanded it. In Nigeria, teacher-guided sessions with a chatbot raised English scores 0.238 standard deviations against students who got no programme at all.

Not every result fits. Google's trial of Gemini's Guided Learning raised maths scores overall, but its younger year group did slightly worse. The German study found no overall effect either way.

The consistent part is narrow and useful. In two randomised trials, an AI assistant used freely during practice left people worse off once it was gone. In both studies that looked at how people used it, those who asked for explanations did better than those who asked for answers. So keep the thinking on your side of the chat: try before you ask, and ask it to explain. Three ways to set that up follow.

Level 1: switch on the learning mode you already have

As of September 2026, according to each company's help pages and apps:

1. ChatGPT: type @study, or press + and choose Study. It is on every plan, though it does not work inside Projects.
2. Gemini: Add files, then More tools, then Guided Learning.
3. Claude: first switch on Code execution and file creation under Settings, then Capabilities. Then add Anthropic's learn skill from Customize, then Skills, then Discover. It stays out of coding tasks by design; for code, see Level 2.
4. Copilot: open Quick response under the prompt and choose Study and learn.
5. Claude Code: type /output-style learning.

Then one habit: before you send a question, write your own answer, even a bad one.

Treat the modes as a start. OpenAI's own help page says Study mode may still give a direct answer, and the companies' own trials are early. In OpenAI's one-session study, students using Study mode scored roughly 15% higher than with ordinary online resources in microeconomics, and no differently in neuroscience.

Level 2: paste a tutor prompt

The modes are not everywhere, and they slip. A prompt goes anywhere. Paste this at the start of any chat where you are learning, and fill in the last two lines:

Be my tutor for this. I'm trying to learn it, not just get it done, so help me work it out myself.

1. Don't give me the answer or write the solution for me. Give me one hint or one step at a time, then wait for my reply.
2. If I ask you to just tell me, ask me to try first. Count every wrong answer or guess as a try. After my second wrong try, give me the answer and explain the step I missed.
3. When I get something right, confirm it and ask me why it works.
4. Keep every reply to a few sentences.
5. When we finish, give me a similar problem to try on my own.
6. If you're not sure something is correct, say so.

What I already know: [e.g. basic percentages, nothing about probability]
The problem: [paste it here]

It borrows Harvard's try-first, one-step shape and the Nigeria exercises' hint-then-answer rule, and it is stricter than Harvard's tutor, which answered on demand.

I ran it eight times with a scripted learner: seven across Claude, Gemini and GPT, one in the ChatGPT app. Each time the learner demanded the answer, the tutor asked for an attempt, and after the second wrong try it gave the answer and named the missed step. Once, asked for fixed code, GPT held back the code but described the fix in words, which gave most of it away.

On a long problem some models count tries per step, so the answer arrives a step at a time; that is fine. If a hint gives the answer away, reply "smaller hint".

This is the same move as asking for the step before the answer, kept on for a whole session.

If you are learning code, use /output-style learning in Claude Code, or paste the prompt with the function or error as "the problem". When it does write code, do what Anthropic's top-scoring pair did: ask why each part is there until you could write it yourself. Then, my suggestion: close the chat and write it.

Level 3: give it your material, then test yourself cold

Levels 1 and 2 set up the tool. This level adds your own material, and it measures you.

1. Create a project for the thing you are learning. In ChatGPT, New project in the sidebar. In Claude, Projects, then New Project (the free plan allows five). In Gemini, a Gem with your files added.
2. Paste these instructions into the project's instructions:

You are my tutor for the material in this project. I'm here to learn it, not to look things up, so make me work for every answer.

1. Before you answer anything about the material, check the files in this project. Use them as the source of truth. If they don't cover it, say so before you answer from general knowledge.
2. Whenever I ask a question about the material, don't answer it yet, even if I say "just tell me". First ask me what I think, or give me one hint.
3. Count every wrong answer or guess as a try. After my second wrong try, give me the answer from the files and explain the step I missed.
4. When I get something right, confirm it and ask me why it works.
5. Keep every reply to a few sentences.
6. If you're not sure something is correct, say so.

1. Upload your material: course notes, documentation, your team's handbook if you are allowed to put it into that service, and an answer key if you have one. The Harvard team loaded every question with its worked answer; without your material, a tutor falls back on what it already knows.
2. Test the setup before you trust it. Ask a question about the material and add "just tell me". If it answers straight away, the setup is not holding. My first version failed exactly this test in the ChatGPT app, answering at once from general knowledge. The version above passed with a hint drawn from the file.
3. Learn in chats inside the project.
4. A few days later, run the cold check. Open a new chat in the project, paste this, and fill in the topics you studied:

Cold check. Quiz me on what I've been learning from the files in this project.
Only ask about these topics: [list what you studied]
Ask 6 questions, one at a time. Mix "what is" questions with "what would you do if" questions.
Don't give hints and don't tell me whether I'm right until all 6 are done.
Then score me out of 6, show the correct answer from the files for each one I missed, and list what I should review.

Answer from memory, with no notes. In the ChatGPT app, with a planted mix of right and wrong answers, every question stayed on topic, it gave no feedback until the sixth, and it scored 3 out of 6, marking exactly my wrong answers and correcting each from the file. I did not test the Claude or Gemini apps themselves, so run it once with answers you know are right and wrong before relying on the score.

The German study found students felt they had learned more than they had. A score checked against your files is a better guide than that feeling, though I tested the marking only on short factual questions, so check each marked miss against the source. Put the check in your calendar and paste the prompt yourself. Whatever you missed goes back into a tutoring chat.

What the research can and cannot tell you

None of the seven studies tested people again weeks or months later, so none shows the learning lasted. Only two went through journal peer review. The evidence on how people used the AI comes from patterns inside two studies, not from assigning them. Three trials were run by AI companies, and in Nigeria the comparison group got no programme at all.

Your own cold check a few days later is the only follow-up test in this piece.

The checklist

Next time you open an AI to learn something:

• Decide whether you need the answer or the skill. For the skill, keep the thinking on your side.
• Switch on your app's learning mode, or paste the tutor prompt.
• Write your own answer before you ask, then ask it to explain.
• If a hint gives it away, ask for a smaller one.
• For anything you need to know properly, put your material in a project and test the setup with "just tell me".
• A few days later, run the cold check on the topics you studied, with no notes, and send what you missed back into a tutoring chat.

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The Nigeria exercises drew in part on tutor prompts published by Ethan and Lilach Mollick in 2023.