Software engineering seems to be shifting from writing every line of code toward reviewing generated code, understanding requirements, making technical decisions, and judging trade-offs. Since AI can produce working code almost instantly, how should someone who is still learning develop those skills?
Traditionally, good judgment came from building many projects, making mistakes, and learning what worked through trial and error. It feels like beginners are now expected to make senior-level decisions immediately: spotting overengineering, identifying inefficient solutions, and knowing what should be refactored. How can we develop that understanding while AI is changing the way we work?
I'm currently at university, and many of my classmates and I rely heavily on AI for assignments, presentations, and homework because it seems necessary to meet deadlines and keep up. The problem is that we often aren't thinking through the work ourselves, and it feels like that dependence is weakening our ability to learn. At the same time, avoiding AI completely can make it difficult to keep pace.
I'm worried that this has taken much of the enjoyment out of programming. What should beginners focus on, and how can we use AI as a learning tool without letting it replace the thinking and practice needed to become competent developers?
5 Answers
Treat an AI coding assistant like a very fast junior developer: useful, but lacking context and common sense. Give it precise requirements, constraints, expected inputs and outputs, error cases, performance needs, and examples. Then test everything yourself. If you cannot explain why the generated solution works or what its trade-offs are, you are not ready to rely on it.
The fundamentals still matter. You learn to judge generated code by first learning how to write and reason about code yourself. Practice building small projects, debugging them, reading documentation, designing simple solutions, and explaining every line you submit. Use AI after making your own attempt, then compare its approach with yours instead of blindly accepting the output.
Develop judgment by getting feedback on real work. Build projects with clear requirements, write tests, review your own code after a few days, and ask why a simpler solution might be better than a complicated one. Learn data structures, algorithms, databases, networking, operating systems, security, and the language you use. AI changes the workflow, but it does not remove the need to understand those areas.
You probably do not need to abandon AI completely, but you should stop letting it do the first and most important thinking. Try solving the problem manually first, even if your attempt is incomplete. Use AI for hints, alternative approaches, explanations, test cases, or feedback on code you already wrote. For some assignments, set aside regular no-AI practice so you can measure whether you actually understand the material.
The idea that everyone must use AI to finish ordinary university work is worth questioning. Heavy workloads can exist in some programs, but completing tasks quickly is not the same as learning the material. Your classmates may appear ahead because they submit faster, but understanding the fundamentals will matter more in interviews and professional work than producing an answer with a tool. If the workload is genuinely unreasonable, discuss it with instructors or reconsider whether the course structure is sustainable.
That makes sense. I think writing down the requirements and edge cases before asking for code would force me to understand the problem instead of just reacting to whatever the tool produces.