Be honest with yourself for a second.
You've opened ChatGPT mid-assignment and just... asked it to do the thing. Maybe it was a coding assignment you didn't fully understand. Maybe it was an essay where you kind of knew the argument but just needed it to sound better. Maybe it was literally just a homework question you didn't want to spend 20 minutes on.
And it worked. You submitted it. You got a decent grade. You moved on.
I'm not here to lecture you about academic integrity. I want to point out something almost nobody in your school is saying out loud: the way most students use AI today is quietly building a trap, and most won't notice until it's too late.
The AI Student Playbook (Tell Me If This Sounds Familiar)
There's a pretty standard set of moves that most students have figured out by now. If you're in a computer science class, a coding bootcamp, or just trying to get through a tech elective, this probably looks familiar:
The Copy-Paste Loop
Professor posts an assignment. You kind of read it. You paste the prompt into ChatGPT. It gives you code. You paste the code into your file. It doesn't work. You paste the error back into ChatGPT. Repeat until it runs. Submit.
The Explain-It-To-Me-But-I'm-Not-Really-Listening
You ask AI to explain a concept. It gives you a beautiful, clear explanation. You skim it. You feel like you understand it now. You close the tab. Two days later, on an exam, you draw a blank.
The Vibe Coder
You've heard that "vibe coding" is a thing, where you describe what you want, let AI write it, and tweak until it works. It can be useful sometimes, but when an interviewer asks you to write a simple function from scratch, you might freeze.
The Essay Cleaner
You write a rough draft, paste it into Claude or ChatGPT, ask it to "make this better," and submit the polished version. You're not technically lying; the ideas were yours. But when your professor asks you to defend your argument in class, you fumble because you didn't fully form it yourself.
None of this makes you a bad student. It makes you a student in 2026. Everyone around you is doing some version of this. The problem isn't that you're using AI, it's how you're using it, and what it's slowly doing to your actual skills.
Why It Feels Like It's Working (But Isn't)
Here's what makes this so tricky: grades are not the feedback system you think they are anymore.
Grades used to tell you whether you actually understood something. In a world where AI can generate a passing answer for almost any assignment in 30 seconds, a B+ doesn't mean what it used to. It means the AI understood it. And since the AI understood it, you felt like you did too. That feeling is called the illusion of understanding, and it's absolutely everywhere right now.
“The grade told you that you passed. It didn't tell you that you learned anything.”
Think about the last time you actually struggled with a coding problem for more than 10 minutes before going to AI. Do you remember the last time you genuinely reasoned through a bug yourself, even imperfectly?
The muscle that gets built in those 10 minutes of struggle, the debugging instinct, the pattern recognition, and the tolerance for ambiguity, is what employers actually interview for. That muscle won't develop if you never use it.
The Moment It All Falls Apart
There are a few specific situations where the AI crutch stops working, and they tend to be the ones that actually matter:
The Technical Interview
Even at companies that have loosened their coding interview standards, they still expect you to talk through your thinking. They want to know why you chose an approach, what the tradeoffs are, and what you'd do if it doesn't scale. You can't paste that into ChatGPT. That has to come from you, live, out loud, under pressure.
The In-Person Exam
A lot of professors have wised up. Closed-book exams, in-class coding tests, verbal defenses of your work. When the laptop goes away, so does the safety net. Students who used AI as a replacement for understanding scramble. Students who used AI as a supplement to understanding do fine.
Your First Real Project
This is the big one. When you're building something real, such as an internship project, a freelance gig, or a portfolio app, the AI can make confident mistakes. It generates code that looks right but breaks in edge cases or suggests approaches that don't fit your system. If you don't understand the code well enough to catch those mistakes, you're stuck and there's no professor to ask.
Real stories from my students
Horror Stories I Saw Last Semester of Students Getting Completely Burned by AI
I'm not going to use names. But every single one of these happened. I either tutored these students after the damage was done, heard about it from other students, or watched it happen in real time. Read carefully, because you might recognize yourself in one of these.
Story #1
The Student Who Passed the Whole Semester and Failed the Final
This one hurt to watch. A second-year computer science student had figured out the formula: every programming assignment, paste the spec into ChatGPT, clean up the output slightly, submit. He was pulling A's on every assignment. He genuinely felt like he was doing well.
Then came the final exam. Paper-based. No computer. No phone. The professor, who'd been watching AI submission patterns all semester, had specifically designed it to be handwritten.
The student sat down, looked at the first question, a basic Java method that any student who'd actually done the assignments would have written a dozen times, and froze. He left half the exam blank, failed, and had to retake the entire course.
He came to me the week after. The first thing he said was “I thought I understood it because the code worked.” That sentence is the entire problem.
Story #2
The Internship That Ended After Three Weeks
A student I had worked with landed a summer internship at a small tech company, which is a huge deal, especially for a second-year. He was excited, went in confident, and felt like he knew what he was doing.
The problem showed up fast. Every time he got a task, he'd immediately go to ChatGPT. He wasn't reading the existing codebase. He wasn't asking his supervisor questions. He wasn't building any mental model of the system. He was just generating code and dropping it in.
The AI-generated code looked plausible but didn't match the patterns of the existing system at all. It introduced a subtle bug that broke a feature nobody caught for a few days. When the senior developer sat down with him to do a code review and asked him to walk through his logic, he couldn't explain a single architectural decision he'd made.
They let him go after three weeks. He called me right after and said the worst part wasn't losing the job; it was realizing he had no idea what he actually knew versus what he'd been outsourcing.
Story #3
The Office Hours Ambush
This one comes up more than you'd think. A student submits a data structures assignment that works perfectly. All test cases pass. The TA gives it a look and flags it for an oral follow-up, which is something professors increasingly do when they suspect AI involvement.
The TA asks a simple follow-up: “Walk me through why you chose this sorting algorithm.”
The student stares. Fumbles. Says something about it “just working.” The TA asks a follow-up: “What's the time complexity?” More silence. “What would happen if the input size was ten times larger?” Blank.
The assignment was the core concept for the entire unit. The student had submitted working code for a problem they fundamentally did not understand.
The grade got pulled and an academic integrity investigation opened. The student had to write a formal explanation of their submission process. It was a nightmare that lasted two months over a single assignment.
Story #4
The Group Project That Fell Apart at the Presentation
A group of four students split up a semester-long project. One student, let's call him the “backend guy”, took on the API and database layer. He used AI to generate most of it, got it working on his machine, and handed off the code to the group a week before the presentation.
The day before the presentation, his teammates tried to run the full project and it broke. They couldn't debug it because nobody understood what the backend guy's code was actually doing, not even him. The code had patterns that made sense in isolation but were incompatible with the rest of the system.
They pulled an all-nighter trying to fix it. Two people in the group had to present on two hours of sleep with a half-broken demo. During the Q&A, the professor asked about the database schema and why certain design decisions were made. Nobody could answer because nobody had actually built it; an AI had.
Three of the four students failed the project portion of the course. The fourth, the one who had done her frontend work herself and actually understood it, passed with a decent grade. Same group, very different outcomes.
Story #5
The Student Who Submitted Code That Didn't Even Run
This sounds impossible but it happens more than you'd think. A student asked ChatGPT to write a Python script for a data analysis assignment. The AI generated code used a library function that existed in an older version of the library, not the version installed on the school's grading server.
The student tested it locally and it ran (they happened to have the older version installed). They submitted, but the autograder failed every test case because the code threw an import error on the grading machine. Score: zero.
When they came to me to figure out what happened, we found the error in about 90 seconds, a one-line fix. But they'd already missed the deadline; the autograder had already locked the submission.
If they had understood the code they submitted, even at a surface level, they would have caught that the import looked unusual. Instead, “it ran on my machine” was the entire depth of their review process. Zero for an assignment they could have aced.
Story #6
The Student Who Told an Interviewer “ChatGPT Helped Me With This”
I want to be careful here because being honest about using AI tools in a professional setting is fine and increasingly expected. But there's a version of this that goes badly wrong.
A student had built a portfolio project, a full-stack web app, using almost entirely AI-generated code. It looked good, he put it on his resume, and he got an interview.
The interviewer pulled up his GitHub repo and started asking questions about it. “What does this middleware do?” “Why did you use this state management approach?” “What's happening in this useEffect?” He couldn't answer any of them with confidence. Then he said the quiet part out loud: “I used ChatGPT for a lot of this, I'm still learning it.”
The interviewer nodded, made a note, and wrapped up the interview five minutes early.
Using AI to build something isn't disqualifying; not understanding what you built is. The portfolio was supposed to demonstrate what he could do, but instead it demonstrated what AI could do. Those are different things.
Story #7
The Student Who Got Caught Because AI Has a Distinctive Writing Voice
This one isn't about coding; it's about the written portions of tech courses that students often forget matter.
A student in a software design course had to submit a written reflection on their project: what they learned, what challenges they faced, what they'd do differently. Standard assignment. She pasted a few notes into ChatGPT and asked it to write the reflection for her.
The professor had been teaching for 15 years. He flagged it immediately, not because of any AI detection tool, but because the writing was too polished and structured and used phrases that no student naturally uses (“it is worth noting that,” “this realization underscored the importance of”). It read like a LinkedIn post, not like a student talking about their project.
He asked her to come to office hours and just talk about what she'd learned. She couldn't replicate any of the specific examples or language in the reflection because she hadn't written it; she'd barely read it.
Academic integrity board. Written apology. Grade reduction. All for a reflection that, if she'd just written it herself in 30 minutes, would have been totally fine.
Here's the pattern across every single one of these stories: the AI handled the output, but the student didn't own the understanding. And the moment they needed the understanding, whether in an exam, an interview, a real job, or a live Q&A, there was nothing there.
By the way
This is exactly what I help students with.
I work with students who are already using AI and want to make sure they're building real skills underneath it, so they can walk into any interview, exam, or project with actual confidence.
Apply for 7-Day Free TrialAfter the trial, membership is $47/month for full support. Prefer a quick call? Message Joseph for a free consultation to pick the right plan.
The Students Who Are Actually Winning Right Now
I've been tutoring students for over 8 years. I've also worked as a software engineer in industry. I can tell you exactly what separates the students who land good internships and jobs from the ones who don't, and it has nothing to do with avoiding AI.
The students who win are using AI more than their peers. But they're using it differently:
They use AI to explain concepts after they've already tried to understand them first. They fight with the material, hit a wall, then use AI to unstick themselves, not to skip the fight entirely.
They read the code AI generates and ask "why does this work?" before submitting it. Sometimes they deliberately write a worse version themselves first, just to build the muscle.
They use AI to go deeper, to understand edge cases, see alternative approaches, and ask follow-up questions that their professor doesn't have time for.
They can talk about their code. In an interview. Without notes. Because they actually understand what it's doing.
The difference isn't discipline or intelligence. It's just awareness. They know that AI is doing part of the work, and they make sure they do the other part.
Here's the Part Nobody Tells You: AI Makes Learning to Code Better Now
Ten years ago, learning to code meant Googling for hours, reading dry documentation, and hoping your Stack Overflow question didn't get downvoted into oblivion.
Now? You have a tutor available 24/7 that will patiently explain the same concept seventeen different ways until one clicks. You can paste your code and say "explain every line like I'm 10" and it will. You can ask it to quiz you, to give you harder problems, to break down why your approach is inefficient.
“If you combine AI with genuine effort to understand, you can learn faster right now than at any point in history. That's not an exaggeration.”
The students who figure this out early are going to be genuinely scary good by the time they graduate. They'll have real coding skills and they'll be fluent with AI tools, which is exactly the combination every tech company is desperate to hire right now.
The students who kept using AI as a shortcut? They'll have decent grades and nothing to show for it. That gap is going to be very obvious very fast once they're competing for real opportunities.
What You Should Actually Start Doing Right Now
You don't have to overhaul how you work. Start small. These three changes will have an outsized impact:
Give yourself 15 minutes before going to AI
On every coding problem, set a timer. Try for 15 minutes. Get stuck, make guesses, look at the error message. Then go to AI. That struggle period is where the actual learning happens; AI just closes the gap after you've already made progress.
Read AI code like it's a quiz
Before you paste any AI-generated code into your project, read every single line and say out loud (or write down) what it does. If you can't explain a line, you don't own it yet. Ask AI to explain that specific line. Then try to rewrite that section from memory.
Build one small thing completely from scratch every week
Doesn't have to be impressive. A calculator. A to-do list. A script that renames files. Something where you're the one making all the decisions and AI is just a reference, not the driver. These projects add up fast, and they're what you point to in an interview.
The Bottom Line
AI isn't the enemy. It's not going away, and pretending otherwise is just cope. The students who learn to use it intentionally, as a learning accelerator and not a homework bypass, are going to look like geniuses compared to their peers in about two years.
The window to build real skills while everyone else is coasting on AI shortcuts is open right now. It won't be forever.
If any part of this article made you feel a little called out, good. That feeling is the starting point. The next move is up to you.
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Joseph Abate
Software Engineer · 6 years in industry · 8+ years tutoring students in programming
I started Coding With Joseph because I kept seeing students graduate with degrees and no confidence in their actual skills. I help students close that gap using the tools available today, including AI, while making sure they build real understanding underneath.
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