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ChatGPT can make coding practice less effective when it writes the solution before you have to reason through the problem. A small 2026 randomized trial found that developers learning an unfamiliar Python library scored lower on an immediate comprehension quiz after AI-assisted coding than after hand-coding. That does not prove ChatGPT universally makes programmers worse: workplace experiments found higher task completion with coding assistants, and the studies measured different outcomes.
Why using ChatGPT can interfere with learning to code
Learning requires more than ending up with working code. You also need to practice identifying a problem, choosing an approach, interpreting errors, and explaining why a solution works. If an assistant performs those steps for you, you may complete the task without building the same understanding.
The clearest warning comes from a 2026 randomized 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 involving asynchronous programming. After participants completed two coding features, they took an immediate quiz on concepts used in the task. The AI-assisted group averaged 50%, compared with 67% for the group that coded by hand; 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 the time difference was not statistically significant. Anthropic’s study summary describes the quiz result as “17% lower” for the AI group.
This is evidence of a possible learning trade-off in a specific setting—not proof of lasting damage. The study measured immediate comprehension after one task involving one unfamiliar library. Its authors do not establish whether quiz performance predicts long-term skill development, or whether the result would hold for other tools, programming topics, or ways of using AI.
Why productivity results do not contradict the learning result
Producing more work and learning more from a task are different outcomes. An AI assistant can help someone complete familiar work faster without showing whether that person could independently reproduce or adapt the solution later.
A June 2025 Microsoft Research summary of three randomized workplace experiments at Microsoft, Accenture, and an unnamed Fortune 100 company reported a combined estimated 26.08% increase in completed tasks among 4,867 developers offered an AI coding assistant. The estimate had a standard error of 10.3%, and the researchers noted that results in the individual experiments were noisy. Less experienced developers had higher adoption and greater productivity gains in these experiments. The outcome was workplace task completion—not independent learning or long-term retention. Microsoft Research’s summary therefore offers counterevidence to the broad claim that AI makes coding work worse, but it does not answer whether AI-assisted practice teaches a new skill as well as working unaided.
What a student study adds—and what it cannot establish
A 2024 quasi-experimental study by Sun and colleagues compared two college programming classes: 43 students in a ChatGPT-facilitated group and 39 in a self-directed group. The researchers observed more copying and pasting of code from ChatGPT and more debugging behavior in the assisted group. Although the article reported performance improvement in that group, the difference between groups was not statistically significant. The course used GPT-3.5-turbo, so its results are specific to that context and do not establish long-term effects on coding skill. The peer-reviewed study shows that assistance changed some programming behaviors; it does not settle whether it causes lasting harm or benefit.
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These are practical ways to keep practicing, not a routine proven to eliminate any learning trade-off.
- Make a first attempt. Before asking for code, write down the expected input and output and sketch the steps you think the program needs. Even an incomplete attempt gives you something to reason about and debug.
- Ask for a hint before a solution. Request the relevant concept, one next step, or an explanation of an error without a full rewrite. If the response gives away too much, ask for a question that checks your understanding instead.
- Use Study Mode as a prompt to think, not as an authority. OpenAI says ChatGPT’s Study Mode can ask questions, explain material step by step, and check understanding. The same documentation warns that it can make mistakes and sometimes provide a direct answer. Treat its explanation as something to verify, not proof that you understand the topic. OpenAI’s Study Mode documentation describes its capabilities and limitations.
- Inspect generated code line by line. Ask what unfamiliar lines do, what assumptions they make, and which edge cases might break them. Then close the response and explain the code in your own words. If you cannot explain a line, investigate it before relying on it.
- Keep debugging in the loop. Run the program, read the error or unexpected output, form a hypothesis, and try a fix before asking for another answer. If you do ask for help, share what you observed and what you tried so the response can support your diagnosis rather than replace it.
- Check recall without assistance. After an AI-assisted exercise, solve a related small problem without AI or explain the original solution from memory. This is a practical check on your understanding; the studies discussed above did not test this specific routine.
- Choose delegation based on your goal. Directly asking for code can make sense for familiar, repetitive work when your priority is completing the task and you can review the output. When you are learning a new language, library, or concept, use the assistant to support your reasoning rather than bypass it. The evidence supports distinguishing learning from task completion, but does not test every kind of task or risk.
What the evidence measures
| Evidence | Setting and participants | Measured result | What the result does not establish |
|---|---|---|---|
| Anthropic randomized trial, 2026 | 52 mostly junior software engineers learning the unfamiliar Python library Trio | Immediate quiz average: 50% for AI-assisted coding and 67% for hand-coding | Whether the difference persists or predicts long-term skill development |
| Microsoft Research field experiments, June 2025 | Three company experiments; 4,867 developers in the combined analysis | Combined estimate of 26.08% more completed tasks with access to an AI coding assistant | Whether developers learned or retained the skills needed to complete those tasks |
| Sun and colleagues, 2024 | Quasi-experimental college programming study; 43 students in the ChatGPT group and 39 in the self-directed group | More copying and pasting and debugging behavior in the assisted group; no statistically significant performance difference between groups | Long-term effects or outcomes beyond the course’s GPT-3.5-turbo setting |
These figures are not competing measurements of the same thing. One concerns immediate comprehension while learning a new library, one concerns workplace output, and one concerns student behavior and course performance. The available evidence does not resolve long-term skill development, transfer to other programming tasks, or how outcomes vary across AI products and interaction styles.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A structured option for learning AI-assisted Python
If you want a guided resource rather than relying on chat responses alone, Learn AI-Assisted Python Programming, Second Edition by Leo Porter and Daniel Zingaro is listed on the Simon & Schuster publisher page under Manning, dated October 2024. The publisher describes it as covering Python programming with tools including ChatGPT and Copilot. It is one possible structured-learning resource, not a requirement or a proven remedy for the learning trade-off described above.
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