Use AI to support your thinking, not replace it: first work out the problem yourself, ask for hints or explanations before requesting a complete solution, and verify any code you accept. That approach keeps you involved in design, debugging, and comprehension—the work that helps you build and maintain programming skill.
What the evidence says—and what it does not
There is a meaningful difference between finishing a task faster and learning from it. In a randomized controlled trial summarized by Anthropic on January 29, 2026, 52 mostly junior software engineers who knew Python but were unfamiliar with the Trio asynchronous-programming library completed short coding tasks. On a near-term quiz, the AI group averaged 50%, compared with 67% for the hand-coding group. The largest score gap was on debugging questions. AI users finished about two minutes faster on average, but that difference was not statistically significant.
This is evidence of a difference in immediate comprehension in a specific learning task—not proof that routine AI use causes lasting skill loss. The sample was relatively small, the quiz followed the task soon afterward, and the relationship between quiz results and long-term development is unresolved. The researchers also caution that effects may differ when people use AI for familiar or repetitive work.
Productivity findings address a different question. In a controlled experiment reported by GitHub, 95 professional developers who already knew JavaScript built an HTTP server. Developers with Copilot completed the task in an average of 1 hour 11 minutes, versus 2 hours 41 minutes without it—a reported 55% faster result. That experiment measured task completion, not learning or retention, so it does not contradict the unfamiliar-library study.
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How people interact with an assistant may matter, but the evidence is still preliminary. In Anthropic’s analysis, higher-scoring groups tended to ask conceptual questions, request explanations alongside code, or check their understanding after generation. Lower-scoring groups leaned more heavily on delegated code generation or AI-led debugging. The authors explicitly note that this cluster analysis does not show that those habits caused the score differences.
A March 14, 2026 AAAI proceedings paper by Ba-Thinh Tran-Le, Patrick Thomas, Nicholas M. Stiffler, and Thuy Ngoc Nguyen studied LeetCode-style problems with novice and advanced college programmers. Its LeetCoach prototype encouraged reflection and incremental steps rather than giving full solutions. The abstract reports substantial post-test gains for novices and smaller gains for advanced learners, describing the work as early evidence and a proof of concept—not proof that every hint-based tool prevents skill loss.
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Use a learning-first workflow
These steps are a practical recommendation, not a scientifically validated schedule. Adjust how much independent practice you do to your goals; the cited studies do not establish an ideal number of minutes or days.
- State the problem before prompting. Write down what the code needs to do, what inputs and outputs matter, and what constraints you know. Sketch a possible approach, even if it is incomplete.
- Ask for a small assist first. Request a conceptual explanation, a hint, a test idea, or feedback on your reasoning. For example: “I think this async function can return before all tasks finish. What behavior should I check?” This leaves you responsible for making the next step.
- Attempt the solution yourself. Use the hint to write or modify the code. If you later ask for a complete implementation, treat it as a proposal rather than evidence that you understand the result.
- Trace and verify accepted code. Follow important branches and data flow. Predict likely failure cases, then write or run tests that check them. Do not stop at code that looks plausible or passes only the simplest case.
- Diagnose bugs before asking for a fix. Describe the symptom, form a likely explanation, and identify what evidence would confirm it. Then compare your diagnosis with the assistant’s response.
- Explain the result from memory. After the change works, describe what caused the problem, how the fix works, and why the tests cover the relevant behavior. If you cannot do that, revisit the code or ask for an explanation before moving on.
Choose the right kind of AI help
The useful distinction is not simply “AI” versus “no AI.” Consider who is doing the reasoning and whether the task is practice or familiar work.
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| Use of the assistant | Who starts the solution? | Who diagnoses errors? | Best fit |
|---|---|---|---|
| Hint or concept first | You sketch an approach; the assistant offers a clue or explanation. | You investigate and test; the assistant can review your reasoning. | Learning an unfamiliar concept or practicing problem-solving. |
| Review or test suggestions | You write the implementation and ask for critique or test cases. | You assess the suggestions and verify changes. | Building comprehension while getting targeted assistance. |
| Full code generation | The assistant produces most or all of the initial implementation. | Risk shifts toward accepting the assistant’s diagnosis unless you independently check it. | Potentially useful for delivery work, provided you still inspect, test, and explain the code. |
This is a way to think about your workflow, not a product ranking: the cited studies did not compare coding assistants as products. For repetitive work you already understand, delegation may be a reasonable productivity choice. When your goal is to learn an unfamiliar library, algorithm, or debugging technique, keep more of the reasoning and implementation in your own hands.
Keep independent practice in the loop
Periodically solve a small task or revisit a real bug without asking an assistant to generate the code or diagnosis. Focus on designing an approach, writing or modifying code, and debugging it independently. The purpose is to preserve opportunities to practice those skills, not to follow a fixed quota: the cited sources do not identify a universal schedule.
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Practice resources can supply exercises, but one study using LeetCode-style problems does not establish that a particular book or platform improves retention. Choose exercises that match the skills you want to practice, and judge progress by whether you can solve, explain, and debug the work—not simply by how quickly a tool produces code.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to interpret the results
The Anthropic summary describes the findings as preliminary. Its researchers, Judy Hanwen Shen and Alex Tamkin, conclude that “Cognitive effort—and even getting painfully stuck—is likely important for fostering mastery.” That is a qualified interpretation of a short study, not a rule that every task should be difficult or that AI should never write code.
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The AAAI paper’s authors similarly argue that “Such learning requires active participation rather than passive acceptance of AI-generated answers, which might be incorrect.” Together, these sources support staying intellectually involved and checking outputs. They do not settle what months or years of workplace AI use do to programming skill.
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