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AI can make it easier to produce code, but current evidence does not show that it generally makes people worse at learning to code. A 2026 meta-analysis found a moderate average productivity benefit across programming studies and no statistically significant average effect on measured learning. Results varied by setting, and one trial with experienced open-source developers found that using early-2025 AI tools made their tasks take longer.
The useful question is not simply whether AI makes coding faster. It is what the tool does for you—and what you still have to work out, write, debug, and explain yourself.
Why coding output and learning are different questions
A developer can finish a task sooner without gaining a skill, just as a learner can make progress while taking longer to solve a problem. Productivity measures—such as task time, commits, or lines of code—describe work completed. They do not, by themselves, show whether someone can later solve a similar problem without help.
Learning is harder to reduce to a single measure. The 2026 meta-analysis described below used exam performance as its learning measure. That is useful evidence, but it does not settle questions about long-term retention or transferring a skill to a new project.
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What studies say about AI and coding productivity
The pooled result: a moderate average gain, with variation
A 2026 meta-analysis by Sebastian Maier, Moritz Gunzenhäuser, Jonas Schweisthal, Manuel Schneider, and Stefan Feuerriegel searched ACM, arXiv, Scopus, and Web of Science for studies published from 2019 through 2025. It included 23 studies and 27 effect sizes comparing generative-AI-assisted programming with programming without AI. Across those studies, the authors found a moderate positive average productivity effect (Hedges’ g = 0.33; 95% confidence interval [0.09, 0.58]). The gains were not consistent across settings: they tended to be larger in controlled experiments and smaller in open-source and enterprise contexts. Read the meta-analysis.
This is a pooled result from the studies that met the authors’ criteria, not a promise that AI will speed up a particular task or person. The studies differed in their participants, work, tools, and measures.
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A counterexample from experienced open-source developers
In a randomized field study, METR enrolled 16 experienced contributors to large open-source repositories they had worked in for years. Participants proposed 246 real issues, with tasks averaging about two hours. When allowed to use AI, developers could choose their tools; they primarily used Cursor Pro with Claude 3.5 or 3.7 Sonnet, tools available at the time of the early-2025 study. AI-allowed tasks took 19% longer on average in this trial. Beforehand, participants expected a 24% speed-up; afterward, they still estimated that AI had sped them up by 20%.
The result is a reminder that perceived speed and measured completion time can diverge. It applies to this study’s participants, repositories, tasks, and tool era; it does not establish that AI slows down novice learners, other kinds of work, or current tools. Read METR’s study account.
Does AI make learning to code harder?
The meta-analysis found no statistically significant average effect on measured learning: Hedges’ g = 0.14, with a 95% confidence interval from -0.18 to 0.47. That result is inconclusive. It is not evidence that AI harms learning, nor proof that every learner benefits. It means the studies in the analysis did not establish a clear average learning effect by the measure they used.
There is a plausible practical distinction: if a tool supplies the reasoning, implementation, and debugging that a learner would otherwise practice, the learner may get less practice doing those things. But the evidence described here does not establish a universal causal mechanism for skill loss. The effect depends in part on what the person delegates and what they continue to do themselves.
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What developer surveys can—and cannot—tell us
Surveys help show how people use AI and what they think of it. They do not substitute for controlled tests of what people retain or can do unaided.
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- Users also report accuracy concerns. In a separate 2024 pulse survey, 38% of developers said code assistants gave inaccurate information half the time or more. Respondents cited shortcomings involving context, complexity, and less-common tools. Satisfaction or a sense of productivity does not independently verify that generated code is correct. See the survey discussion.
- Reported skill benefits are perceptions, not measured outcomes. GitHub and Wakefield Research surveyed 500 non-student developers at U.S. companies with more than 1,000 employees from March 14 to March 29, 2023. In that sample, 57% said AI coding tools helped them develop coding-language skills. The survey did not test retained knowledge or unaided performance. The findings were published by GitHub, a company with a commercial interest in AI coding tools. Read GitHub’s survey report.
- A newer survey announcement describes use, not causation. Stack Overflow’s October 6, 2026 article said more than 30,000 people responded over seven weeks. It reported that 73% of respondents who use AI coding assistants or agents use them daily, while 52% of respondents are still learning new coding skills. It also said 70% ask an AI agent for answers and 83% use a search engine. The article said the full dataset would be published later, so these are figures from the announcement rather than a fully inspectable dataset or causal study. Read Stack Overflow’s announcement.
How to use AI without handing over the learning
A learning-oriented workflow keeps the decisions and practice that build skill with you. Use AI as a tutor or debugging partner, not as an automatic substitute for attempting the task.
- Try first. Before asking for code, write down what you expect the program to do, sketch the steps, or make a first attempt. This gives you something to compare an explanation against.
- Ask for a hint or explanation. Request a concept explanation, a debugging question, or one next step rather than a complete solution. If you do ask for code, ask the assistant to explain the relevant choices and assumptions.
- Make the change yourself. Type, adapt, and test the implementation instead of pasting an answer you cannot explain. When debugging, identify the failure and form a hypothesis before asking for possible causes.
- Verify independently. Run the code, inspect the relevant documentation, and test edge cases. AI output can be inaccurate, especially when the task depends on repository context, complex behavior, or less-common tools.
- Check what you learned. Close the answer and explain the solution in your own words, then try a small variation without assistance. Treat success as evidence about that exercise, not proof of long-term retention.
GitHub’s learner guidance offers one practical setup for Copilot: turn off inline suggestions and instruct it to explain concepts without supplying solutions. That is product guidance, not a comparative study showing that this configuration improves learning. See GitHub’s learning guide.
How to judge claims that an AI tool makes coding easier
Before applying a result to your own work, check what was actually measured and who took part. A claim about speed in a short, controlled exercise does not automatically predict outcomes in a mature codebase—or what a beginner will learn.
- Outcome: Was the study measuring time, code quantity, quality, exam results, or retained skill?
- Participant: Were the people novices, working professionals, or experienced contributors to a particular project?
- Setting: Was the work a classroom exercise, controlled experiment, enterprise task, or issue in a mature repository?
- Tool and date: Which tool and version were used, and when? Results from early-2025 tools do not automatically describe later versions.
- Evidence type: Was the result observed in task performance or reported by survey respondents?
- AI autonomy: Did the system offer hints and completions, write substantial code, or act as an agent that could run code?
These distinctions help explain why a positive average productivity result, a field-trial slowdown, and survey respondents’ perceived benefits can all be true without answering the same question.
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