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ChatGPT can make it easier to finish a coding task while making it harder to learn the ideas behind it. A small randomized study found lower immediate quiz scores among developers who used AI to work with an unfamiliar Python library; separate workplace experiments found that developers with an AI assistant completed more tasks. Those findings measure different things, so they do not show that ChatGPT universally makes people worse programmers.
Why using ChatGPT can interfere with learning to code
Learning requires more than producing working code. You also need to choose an approach, interpret errors, understand unfamiliar concepts, and remember how to apply them later. When an assistant supplies the solution before you do that work, you may finish without practicing those steps. The concern is most relevant when your goal is to learn a new language, library, or concept—not simply to complete a familiar task.
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The clearest direct evidence comes from a 2026 randomized controlled trial by Anthropic researchers Judy Hanwen Shen and Alex Tamkin. It involved 52 mostly junior software engineers who used Python regularly but were unfamiliar with Trio, a library that involves asynchronous programming. After completing two coding features, participants took an immediate quiz on concepts used in the task. The AI-assisted group averaged 50%; the hand-coding group averaged 67%. The reported difference was statistically significant (Cohen’s d = 0.738, p = 0.01). The AI group finished about two minutes sooner on average, but that time difference was not statistically significant. Anthropic’s study summary describes the quiz result as 17% lower for the AI group.
This is a warning signal about immediate comprehension in one learning setup, not proof of lasting damage. The study was small, focused on one unfamiliar library, and did not establish whether quiz performance predicts long-term skill development or transfer to other programming tasks.
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Why that does not mean ChatGPT makes coding work worse
Workplace productivity and learning are separate outcomes. Microsoft Research’s June 2025 summary of three randomized field experiments at Microsoft, Accenture, and an unnamed Fortune 100 company reported a 26.08% increase in completed tasks in a combined analysis of 4,867 developers who had access to an AI coding assistant. The individual experiments were noisy, and the estimate had a standard error of 10.3%. Less experienced developers showed higher adoption and greater productivity gains in those experiments.
Those results concern completed workplace tasks, not whether developers independently understood or retained the code. An assistant can help someone deliver more work while leaving open how much they learned. The experiments therefore provide relevant counterevidence to a blanket claim that AI makes coding work worse, but they do not cancel out the learning study. Microsoft Research’s summary explains the field experiments and their outcomes.
What a student study adds—and does not establish
A 2024 quasi-experimental study by Sun and colleagues examined 82 college students in programming classes: 43 in a ChatGPT-facilitated group and 39 in a self-directed group. The assisted group showed more copying and pasting of code from ChatGPT and more debugging behavior. The article reports no statistically significant difference in programming performance between the groups. It used GPT-3.5-turbo in a particular course context, so it cannot settle whether ChatGPT improves or harms long-term coding skill more broadly. The peer-reviewed article reports the study’s methods and findings.
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How to use ChatGPT without outsourcing the learning
The following habits are practical ways to keep yourself involved. They are not a proven protocol for preventing skill loss, and the studies discussed here did not test this exact routine.
- Try the problem first. Before asking for code, write down the expected input and output, then sketch the steps you think the program needs. A partial attempt gives you something specific to reason about.
- Ask for a hint, not the finished solution. Request the concept involved, one next step, or an explanation of an error without asking the assistant to rewrite the whole program. If it gives away too much, ask it to quiz you on the next step instead.
- Question unfamiliar code. For each line you do not understand, ask what it does, what assumptions it makes, and which edge cases could break it. Then put the answer aside and explain the code in your own words.
- Debug before requesting another fix. Run the code, inspect the error or unexpected result, form a hypothesis, and try a change. This gives you practice diagnosing problems instead of only receiving a patch.
- Check yourself without assistance. After a practice task, solve a related problem from memory or explain the approach without looking at the generated code. This is a useful independent check; the studies above did not test this particular step.
Anthropic’s qualitative analysis described varied ways participants used AI, including delegating code, asking for explanations, and asking conceptual questions. Some high-scoring participants asked follow-up or conceptual questions, but the researchers did not establish that those patterns caused better results. Treat them as sensible ways to stay engaged, not guaranteed fixes.
Use Study Mode as a prompt for practice, not a guarantee
OpenAI documents Study Mode as a ChatGPT feature that can ask questions, explain topics step by step, and check understanding. It may help structure a practice session, but OpenAI also warns that it can make mistakes and sometimes provide a direct answer. Keep checking the output and doing the reasoning yourself. OpenAI’s Study Mode documentation describes how the feature works.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose between learning and delegation deliberately
For a familiar, repetitive task, asking an assistant to draft code can be a reasonable productivity choice if you can review the result. For a new library or concept, slow down and use it to clarify ideas or diagnose a specific problem rather than letting it complete the whole exercise. The evidence supports distinguishing work output from learning; it does not establish a single best approach for every task or risk level.
If you prefer a structured resource alongside practice, the publisher’s page for Learn AI-Assisted Python Programming, Second Edition lists an October 2024 edition by Leo Porter and Daniel Zingaro covering Python programming with tools including ChatGPT and Copilot. It is an optional learning resource, not a required or proven remedy for the study’s quiz-score difference.
What the evidence can—and cannot—tell you
| Evidence | Setting and participants | Measured outcome | Main limitation |
|---|---|---|---|
| Anthropic randomized trial, 2026 | 52 mostly junior software engineers learning the unfamiliar Python library Trio | Immediate quiz average: 50% with AI assistance and 67% with hand-coding | Small, focused study; long-term learning was not measured |
| Microsoft Research field experiments, summarized June 2025 | Three company experiments; 4,867 developers in the combined analysis | Reported 26.08% increase in completed tasks with access to an AI coding assistant | Task completion does not measure durable learning |
| Sun and colleagues, 2024 | 82 college students in a course-specific comparison using GPT-3.5-turbo | More copying and pasting and debugging in the ChatGPT group; no statistically significant difference in programming performance | Quasi-experimental and course-specific; not a long-term skill test |
The studies use different participants, designs, tools, and outcomes, so their figures are not directly comparable. The direct evidence does not resolve long-term skill development, transfer to other programming tasks, or how results vary among AI products and interaction styles.
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