In a 2024 study, a student finishes their programming exercise. They succeeded: 95% of the task done, the code runs. In the interview right after, they admit: "Honestly, I really don't understand code at all." They had just produced it. With AI, they finished everything, and learned nothing.
That sentence captures the most insidious trap of our era. AI makes writing code so frictionless that you can sail through an entire learning experience without your brain encoding a single thing. You feel like you're learning, you produce, and nothing sticks. I built an entire course section ( and the applied projects: AI cranks out code fast, you read it slowly, you send back precise instructions. Each project shows the real prompts, the shaky first draft, then the review that hardens security, accessibility and contrast. We don't teach delegation, we teach critique. That's negative expertise turned into a habit.
But rereading the research, I saw my own blind spots. My quizzes fire right after the lesson: that tests working memory, not durable memory. My examples often show the solution before the effort. I had no real spaced repetition. So I'm adding: a "predict before you run" in the editor, free-recall questions where you re-explain in your own words, the return of older notions across later lessons, and a teach-back after each exchange with the AI. In short, I'm deliberately injecting the frictions my own courses, too smooth, had sanded away.
Conclusion
The irony of all this is that the tool meant to democratize learning to code can widen the gap between those who already know how to think and those still learning. AI won't turn you into a developer in your place, any more than a calculator makes you a mathematician. It amplifies a reasoning that must already exist.
The real question was never "can AI write this code?". Of course it can. The question is: "after reading it, could I rewrite it alone?". As long as the answer is no, you haven't learned anything, you've only borrowed. And debt, one day, comes due.
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