Quick Answer: Is AI making it harder to learn programming?
AI is not making programming harder on its own, but it removes the mental struggle, generating a solution from memory, that builds lasting skill. Coding assistants hand you a correct-looking answer before you attempt one yourself. Stack Overflow's 2025 survey found the group learning to code trusts that output most, which is the riskiest combination of all.
LearnPath's own numbers show the shape of the problem, though this is a small sample from one product, not a controlled study. Across more than 300 programming learning paths in our database, roughly 60 first-attempt quizzes taken by about 30 learners, each quiz generated from the transcript of the video that learner had just watched, produced an average first-attempt score of around 65%, with about half scoring below 70%. These were not people being examined on someone else's syllabus; they were quizzed on a topic they picked, from a video they had watched minutes earlier. What the question of AI and learning actually turns on is whether you replace that immediate retrieval step with a shortcut, or add one on top of it.
The trust gap: beginners believe AI most, experienced developers trust it least
Stack Overflow's 2025 Developer Survey asked developers how much they trust the accuracy of AI-generated code, broken out by experience level, and the results run backward from what you would want. Among respondents in the survey's own "Learning to Code" group, 6.1% said they highly trust AI output. Among developers with ten or more years of experience, only 2.5% said the same. The group with the least experience is roughly 2.4 times more likely to highly trust the output than the group with the most. Across all respondents, more people said they actively distrust the accuracy of AI tools (46%) than said they trust it (33%).
The most common frustration, cited by 66% of respondents, was AI output that is "almost right, but not quite," the kind of answer a beginner has the least experience to catch. A further 45% said debugging AI-generated code is more time-consuming. Overall favorability toward AI tools fell across the survey, from more than 70% in 2023 and 2024 down to 60% in 2025, with people currently learning to code reporting 53% favorable versus 61% among working professionals.
None of this is a rejection of the tools. 84% of respondents use or plan to use AI in their development work, up from 76% the year before, and among people learning to code, 39.5% already use AI assistants daily. The tools aren't going away. The problem is narrower: the group with the least experience to spot a wrong answer is also the group most inclined to accept one without checking.
Even experienced developers get slower, not faster
If trust were the whole story, you might assume experienced developers who trust AI less are simply being cautious, and that using it still makes them faster. A randomized controlled trial run by METR in 2025 found the opposite in one specific setting. Sixteen experienced open source developers worked on 246 real issues in codebases they knew well, each issue randomly assigned to allow or disallow the frontier AI coding assistants available at the time. When allowed to use AI tools, developers took 19% longer to finish, not less. METR's 2026 follow-up post puts a wide confidence interval around that figure, from 2% to 39% longer, and says it is changing the experiment design, both worth knowing before you treat 19% as a precise number.
The stranger part is the perception gap. Before starting, these developers expected AI to speed them up by 24%. After finishing, and after the screen recordings showed they had actually been slower, they still believed AI had sped them up by 20%. If experienced developers who can review their own recorded work get the direction of the effect wrong, a beginner with no history to check against has even less to go on.
Why the struggle is the point
None of this means AI assistance is bad for learning, or that struggle is inherently good. It means a specific kind of struggle, generating an answer from memory rather than recognizing one already on screen, is what produces durable retention. Roediger and Karpicke's classic 2006 study found that students who took a recall test on material they had just studied, with no feedback, remembered substantially more of it after a delay than students who spent the same time restudying the passage. At the five-minute mark, restudying briefly looked better. Days later, testing won by a wide margin, even though the group that kept restudying felt more confident about what they knew.
That mismatch between confidence and actual retention is the mechanism behind the LearnPath numbers above. Watching a tutorial or reading an AI assistant's explanation and nodding along produces confidence. Generating the answer yourself, with no completion suggestion to lean on, produces retention. We have written before about why tutorials create this same illusion even without AI in the picture; AI coding assistants speed up the identical mechanism, just faster and more convincingly.
Your realistic options, compared
None of these options is free of trade-offs, and the honest comparison depends on whether you are optimizing to ship something now or to actually retain the skill.
| Option | Cost | Structure | Accountability | Best for |
|---|---|---|---|---|
| YouTube alone | Free | None, the algorithm decides what's next | None | Someone who already knows exactly what gap they're filling |
| freeCodeCamp | 100% free | Fixed curriculum, projects, certifications | Self-paced, no external check | Beginners who want a full free curriculum with no cost decision to make |
| Coursera Plus | $59/mo list, shown at $35/mo promotional when checked, or $399/year | High, structured courses with deadlines | Graded assignments, certificates | Learners who want university-style structure and can pay for it |
| AI chat assistants, for a plan or explanations | Free tiers exist; paid tiers vary by vendor | Whatever you ask for, generated on demand | None, nothing checks whether it stuck | Getting unstuck fast or generating a starting plan, not testing retention |
| LearnPath | Free tier; Pro $12.99/mo, or $8.99/mo billed annually | AI-built path from YouTube videos plus transcript quizzes | Quiz gates after each video | Learners who want free YouTube content organized with a forced recall step, though our own quiz scores show that step is still genuinely hard, not a guarantee |
freeCodeCamp is the cleanest deal on this list: its own site states plainly that every aspect of it is free, the courses, the projects, and the certifications alike. Coursera Plus sits at the other end, a real fixed price for real structure, worth it if graded deadlines are what keep you going. AI chat assistants are useful for the first five minutes of any topic, generating a plan or explaining a concept, but nothing in that interaction checks whether the explanation stuck.
LearnPath sits in between: free to start, and its transcript quizzes use the same recall mechanism the research above points to. But our own data on roughly 60 first-attempt quizzes shows an average score of around 65%, so a path that includes quizzes does not by itself guarantee the quiz was easy, or that the material stuck the first time through. For a deeper look at where quiz-based structure fits against building projects with no guide at all, see our piece on whether projects alone are enough.
What actually works: step by step
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Watch or read the explanation once, then close it before you write anything. Do not keep the AI chat window or the tutorial open next to your editor while you work. The moment you can see the answer while typing, you are back to recognizing it instead of recalling it.
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Retype the function from memory the next morning, not the same hour. The testing-effect research shows the retention gap between testing and restudying widens over days, not minutes; recalling something immediately after seeing it barely beats restudying. Give it until the next day before you attempt the rebuild unaided.
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Write out your own guess about a bug before you ask an assistant about it. Type what you think is wrong, and why, in a comment or scratch file before pasting the error into a chat assistant. That guess is a retrieval attempt whether or not it turns out to be right.
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Let AI handle boilerplate, not the one new concept in the lesson. Using an assistant for repetitive setup code or config files frees up attention for the actual new idea, but only if you write that specific part yourself, from a blank file.
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Quiz yourself, or take a generated quiz, before moving to the next video. Whatever produces the questions, the mechanism that matters is producing an answer with nothing to read from. Our related post on active recall versus rereading covers the wider evidence for why this step outperforms passive review.
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Track how often you can rebuild something with no assistant open, not how many features shipped with one. Shipping speed and retained skill are different measurements. The same tool that slows down learning speeds up shipping, and which one you actually need depends entirely on which one you are doing right now.
Frequently Asked Questions
Is AI making it harder to learn programming in 2026?
Not by itself. AI coding assistants remove the retrieval step, generating an answer from memory, that produces durable skill, and Stack Overflow's 2025 survey found people currently learning to code trust AI output more than experienced developers do. The tools aren't the problem; using them as a replacement for practice instead of a supplement to it is.
Why do beginners trust AI code more than experienced developers do?
Experienced developers have made enough mistakes to recognize when confident-sounding code is subtly wrong, so they read AI output skeptically. Beginners don't yet have that pattern library. Stack Overflow's 2025 survey found 6.1% of people learning to code highly trust AI accuracy, versus 2.5% of developers with ten or more years of experience, about 2.4 times higher.
Does using AI coding assistants actually slow down experienced developers?
In one randomized controlled trial by METR, yes: 16 experienced open source developers took 19% longer to finish real issues when allowed to use frontier AI coding assistants, not less. METR's own follow-up puts a wide confidence interval on that, from 2% to 39% longer. The developers expected and later still believed AI had sped them up, which it hadn't.
Should I stop using AI tools completely while I'm learning to code?
No, that overcorrects. The research points to a narrower fix: use AI for scaffolding and getting unstuck, but generate the core solution yourself first, then check it, rather than accepting AI's version first and rarely retyping it unaided. The tool that slows learning is the same one that speeds up shipping; the fix is knowing which you're doing.
What is the testing effect, and why does it matter for learning to code with AI?
The testing effect, shown by Roediger and Karpicke in 2006, is that recalling information from memory produces better long-term retention than restudying it, even though restudying feels more confident in the moment. AI assistants let you skip straight to a correct answer, which feels like restudying: confident, fluent, and, on its own, worse for retention days later.
How do I know if I'm using AI as a learning shortcut instead of a support tool?
Ask whether you could rebuild what you just did tomorrow with no assistant open. If the answer is no, you accepted an answer rather than generated one, the exact gap the LearnPath data above shows: an average first-attempt quiz score of around 65% on material learners had just watched, with about half scoring below 70%.