Using AI Coding Tools in College Without Hollowing Out Your Skills
Students are shipping more impressive projects than ever and failing interviews on the fundamentals behind them. The problem isn't that they use AI — it's when they use it. Here is the line that keeps the tool and the skill.
A student sent me a genuinely impressive project last term — a working full-stack application, deployed, with authentication and a clean interface. Two weeks later he came back deflated from an interview. The interviewer had opened his own repository, pointed at a function, and asked why he had chosen that approach over the obvious alternative. He did not know. He had not chosen it; it had been generated, it had worked, and he had moved on. The interview ended politely and early.
I want to be careful here, because the lesson most people draw from this story is the wrong one. He did not fail because he used AI. He failed because he used it at the one moment where the struggle was the point. Students who refuse these tools out of principle are handicapping themselves for a market that has already moved; students who route every problem through them arrive at placements with a portfolio they cannot defend. Both are losing. The useful question is not whether to use AI, but where in your process it belongs.
The two failure modes
The first failure mode is refusal. A minority of sincere students avoid AI tools entirely, believing this preserves their purity as engineers. What it actually does is leave them slower than their peers at everything the industry now takes for granted, and unprepared for workplaces where these tools are standard issue. Refusing to learn the tool is not rigour; it is just a different way of arriving underprepared.
The second failure mode, far more common, is outsourcing the thinking. The student prompts before understanding the problem, accepts the first output that runs, and never forms the mental model the exercise was supposed to build. This produces a very specific kind of graduate: someone with an impressive-looking body of work and no ability to reason about any of it under questioning. It is invisible for two years and catastrophic in one interview.
The line between them is not about how much you use the tool. It is about the order of operations.
What interviewers test now that AI writes the boilerplate
Hiring has adjusted faster than most students realise. When anyone can produce syntactically correct code on demand, the ability to produce syntactically correct code stops being a signal. So interviewers have moved their questions to where the signal still is.
They ask why. Why this data structure, why this approach, what breaks if the input triples, what would you change if this ran on ten thousand users instead of ten. They open your own repository and ask you to walk through a specific decision. They give you a piece of working code and ask what is wrong with it. They describe a vague problem and watch whether you can turn it into a specification before any code exists.
Every one of those questions is unanswerable by someone who generated their way through college and answerable by someone who used AI as an accelerator on top of their own understanding. This is why the tool is not the problem — the sequence is. It is also the deeper version of a shift we wrote about in the death of generic coding: the commodity part of the job is commoditised, and what remains is judgement.
Will AI tools make me a worse engineer?
Only if you use them to skip the part where you get better. Skill is built by struggling productively with a problem slightly beyond your current ability — that is what forms the mental model. Any tool that removes the struggle also removes the formation. But the same tool used after you have formed the model removes drudgery instead, and drudgery builds nothing.
So the honest answer is: it depends entirely on whether the thing you are outsourcing is the learning or the labour. Generating a regex you understand but cannot be bothered to remember is labour. Generating a recursive solution you could not have derived is learning, handed away. The tool is identical in both cases. The outcome for your career is not.
There is a second, subtler risk worth naming: fluency without depth is very comfortable. You feel productive, your projects work, and nothing signals that anything is missing until an interviewer asks a question one layer down. The absence of friction is exactly what makes the erosion hard to notice from inside.
The rule I give students: generate after you can specify
The rule is simple enough to apply in the moment. Before you ask a model for code, you should be able to state, in plain language, what the code must do, what the inputs and outputs are, and roughly what approach you would take. If you can do that, generating is fine — you are delegating typing. If you cannot, generating is how you skip the learning, and you should sit with the problem yourself first.
In practice this means a fifteen-minute rule for anything you are supposed to be learning: attempt it unaided for fifteen minutes, write down where you got stuck, and only then bring the tool in — ideally to explain the concept rather than hand you the answer. "Explain why my approach doesn't handle duplicates" teaches you something. "Write the function" does not.
For coursework and fundamentals, be stricter still. Data structures, algorithms, operating systems, and databases are the subjects interviewers probe and the ones where a generated answer costs you the most. For a hackathon deadline at two in the morning, be looser — nobody is testing you on scaffolding, and shipping matters. Deciding this deliberately by context, rather than by default, is most of the discipline.
Where these tools genuinely accelerate learning
Used well, AI is a better study aid than anything students had before, and it would be silly to pretend otherwise. The uses I actively recommend:
- Explaining code you did not write — paste an unfamiliar function from a library or an open-source repository and ask what it does, line by line, until you can restate it yourself.
- Being the reviewer you don't have — ask what is wrong with code you wrote, what edge cases you missed, what a senior engineer would flag. This is the closest most students get to mentorship on their own work.
- Generating practice, not answers — ask for five problems on a concept you are weak at, then solve them yourself.
- Unblocking environment and tooling pain — build errors, dependency conflicts, deployment configuration. Nobody's engineering judgement was ever built by fighting a package manager.
- Rubber-ducking a design — describe your approach, ask what you have not considered, and argue back.
The pattern across all five is that you remain the one doing the reasoning. The tool supplies information, review, or labour; the model of the system still forms in your head.
The habits that keep the skill
Three habits reliably separate students who stay sharp from those who quietly hollow out.
The first is retyping. When you accept generated code, type it out yourself rather than pasting it. It sounds superstitious, and it is remarkably effective — typing forces you to read every line, and you will catch things you would otherwise have scrolled past.
The second is the explain-back test. After any generated block enters your project, close the window and explain out loud what it does and why it is written that way. If you cannot, you have a gap, and gaps found on your own sofa are free while gaps found in an interview are not. This is the same muscle that makes you good at explaining your work under pressure.
The third is deliberate breaking. Change something and predict what will fail before you run it. If your prediction is right, you understand the code. If you are surprised, you have just found exactly what to study. This one habit turns a passive codebase into an active teacher.
What do I say in an interview if I used AI?
Say so, plainly, and then demonstrate that you understand what you built. Interviewers in 2026 assume you used these tools; almost every professional does. What they are checking is whether you are the author of the decisions or merely of the prompt. Lying is both unnecessary and easy to catch — two follow-up questions will expose it, and dishonesty ends an interview much faster than tool use ever would.
The strong version of the answer sounds like: "I used an assistant for the boilerplate and the CSS, and I wrote the matching logic myself because I wanted to get the ranking right. I did use it to review my error handling and it caught a case I'd missed with empty results." That answer signals judgement, honesty, and self-awareness — all things being tested. The weak version is a vague "I built it all myself" that collapses under the first "why did you choose this?".
Building a portfolio that survives scrutiny
Since anyone can now generate a polished-looking application in a weekend, a polished-looking application no longer proves much. What still proves something is depth in one place: a project where you made real decisions, hit a real constraint, and can explain the trade-off you chose. One such project beats four generated clones, which is the same reason three deployed side projects can outweigh a high CGPA — the value was never in the artefact, it was in what building it forced you to understand.
Practically: pick one project you care about, keep a short written record of the decisions you made and why, and be able to name one thing that is wrong with it and one thing you would rebuild. Reviewers notice that immediately, because it is the one thing a prompt cannot produce on your behalf.
The balance, stated plainly
Use the tools. They are part of the job now, and the students who pretend otherwise are choosing a handicap for no benefit. But keep the sequence right: understand, then specify, then generate — never the reverse — and keep the fundamentals your own, because those are the ground everything else stands on.
My student rebuilt that project over six weeks, this time writing the core logic himself and using the assistant for scaffolding and review. The final version was less polished than the original. He could also defend every line of it, and he cleared the next interview comfortably. The tool had not been his problem. The order had.
Not sure whether you're accelerating or hollowing out? Talk to a Career Call counsellor. We'll go through your projects the way an interviewer would, find the gaps hiding under the polish, and build a plan that keeps the speed and the skill.