AI Code Detection for Teachers: Grading Programming Assignments Fairly

A practical guide for teachers and TAs on handling possible AI-generated code in programming assignments: clear policies, fair checks, and conversations instead of score-based punishment.

AI coding assistants have changed what a programming assignment measures. A student can paste a prompt into ChatGPT, Claude or Gemini and receive working, commented code in seconds. For instructors, that raises two questions at once: how do you tell when that has happened, and what should you do about it? This guide focuses on the second question as much as the first, because a fair process matters more than any detection tool.

Start with a clear policy

Before thinking about detection, make sure students know the rules. "No AI allowed" and "use AI however you like" are both legitimate policies, but most courses land somewhere in between. Spell it out for each assignment:

• Is AI allowed for explaining concepts, debugging error messages, or writing boilerplate?
• Is it allowed for writing the core solution?
• If AI is used, does it need to be disclosed, and how? A short note in the submission or a comment at the top of the file is easy to ask for.
• What happens if a student cannot explain code they submitted?

A clear policy turns many difficult cases into simple ones. A student who disclosed using AI for a helper function under a policy that allows it has done nothing wrong, whatever a detector says.

What AI-generated student code often looks like

Student code generated by an assistant often has a recognizable texture, especially in introductory courses:

• Polished docstrings and line-by-line comments from a student whose earlier work had none.
• Language features, libraries or patterns that have not been covered in the course yet.
• Extra input validation, error handling or a demo main block that the assignment never asked for.
• Perfectly uniform naming and formatting with no leftover debugging code or abandoned attempts.
• A solution that solves a slightly different, more generic version of the problem than the one you set.

Every one of these can also come from a hard-working student who read ahead, used a good tutorial or just writes neatly. They are reasons to look closer, not conclusions.

Where a detector fits

An AI code detector is useful for triage. When you have dozens of submissions, it can point you to the few that deserve a second read, and its reasons can help you put your own impressions into words. It is not useful as a judge. Detectors can be wrong in both directions: clean human code can score high, and AI code that a student has edited can score low. Short assignments, such as a ten-line function, are especially hard to classify because there is so little style to analyze.

If you use a detector, follow a few simple rules. Paste only the code, never the student's name or ID. Read the reasons it gives, and check whether they match what you see. Never treat a single score as a finding, and never put a score in a grade report as if it were evidence.

Talk with the student first

The most reliable way to find out whether a student understands their code is to ask them about it. Frame it as a normal part of the course rather than an accusation; many instructors hold short code walkthroughs for everyone, or for a random sample, so that being asked is not a signal of suspicion. Useful questions include:

• "Walk me through what this function does, line by line."
• "Why did you choose this approach instead of a simpler loop?"
• "What happens if the input list is empty? Show me where that is handled."
• "Can you change this so it also handles negative numbers?"

A student who wrote the code, or who used AI and genuinely worked through the result, will usually manage these comfortably, even if nervously. A student who pasted code they do not understand will typically struggle with the follow-up change in particular. That conversation tells you far more than any score, and it gives the student a fair chance to explain.

Look at the process, not just the product

Evidence about how the work was produced is usually stronger than evidence from the final file. If your course uses Git, ask for regular commits and look at the history: real work tends to grow in steps, with fixes and dead ends, while pasted work often arrives complete in one commit. Comparing a submission with the same student's earlier assignments is also valuable, because a sudden jump in style or sophistication is more meaningful than any single feature of the code.

Designing assignments for the AI era

Detection will always be imperfect, so it helps to design assignments where understanding is visible:

• Ask for reasoning. A short written explanation of key design decisions, or a reflection on what went wrong along the way.
• Use checkpoints. Small in-class or oral milestones that show progress over time.
• Add local context. Build on code, data or constraints from your own course materials rather than textbook problems that assistants have seen many times.
• Grade the explanation. Give part of the marks for a walkthrough or a short follow-up task.
• Allow AI explicitly in some tasks. Teaching students to use assistants well, and to check their output critically, is a useful skill in itself.

If you still have concerns

If a conversation leaves you with real doubts, follow your institution's academic integrity process rather than acting alone. Write down what you observed, what the student said and what other evidence you have. Keep the detector result in perspective: at most, it is one supporting observation. The aim is a decision that you would be comfortable defending to the student, to a colleague and to yourself.

Frequently asked questions

Try the detector

Have a snippet you are unsure about? Paste it into the free AI Code Detector to get a verdict, an AI probability and the reasons behind it. No sign-up, and your code is not stored.