How to talk to a student you suspect used AI on an assignment

You've got a submission on your desk that reads too cleanly. Perfectly structured, confident vocabulary, no visible signs of struggle. Your instinct says something's off. What you do next matters more than most guidance acknowledges - both for the student and for your own position.

Don't lead with the accusation

The first mistake is framing the conversation as an investigation. Starting with 'Did you use ChatGPT for this?' gives the student two options: confess or deny. Neither is particularly useful. A denial locks both of you into an adversarial position before you have any real evidence. A confession still doesn't tell you how much AI was involved, or whether it was deliberate misuse or naive experimentation.

There's also a practical risk. Formal academic misconduct proceedings require clear evidence. If you raise a concern without it and the accusation falls apart, the institutional and relational fallout can be significant - especially if the student is a non-native English speaker whose fluency you may have misread.

Gather evidence before you open the door

Before any conversation, make sure you have something concrete to go on. A submission that 'feels' AI-generated is not sufficient grounds for a formal concern, even if your instinct turns out to be right.

Useful signals split into two categories: output signals (things about the text itself) and process signals (things about how it was created). Output signals - seamless structure, generic examples, oddly formal vocabulary - are what most teachers notice first, but they're the least defensible. Text-based AI detectors that work on the same principle flag ESL writers at disproportionately high rates, and students can always argue they were just writing carefully.

Process signals are more concrete. If the submission came through a tool that tracks writing behaviour, you may have access to whether the work was largely typed or pasted, how long the session lasted, whether there were natural pauses and revisions. Learnaway, for example, records the timing and type of events - a burst of typing, a 600-character paste, a gap of 20 minutes - without recording the actual text. A teacher can see that 85% of the essay arrived in a single paste event after two minutes of activity, without ever reading what the student wrote.

How to open the conversation

Lead with curiosity, not suspicion. The simplest frame is a standard process question - one you could reasonably ask any student about any piece of work:

'I want to check in on how you approached this. Can you walk me through your process - how did you start, where did you get stuck, what changed as you went?'

That question works whether or not AI was involved. A student who wrote the work will have a story. They'll remember the hard parts, the choices they made, the thing they looked up. A student who didn't write it often can't reconstruct the thinking behind it - not because they're a bad liar, but because there's no process to recall.

If you have specific process data, you can raise it without accusation: 'I noticed there was a point where a large block appeared quite quickly - can you tell me more about that?' This gives the student a chance to explain. Maybe they drafted it in a separate app and pasted it in, which is a completely legitimate workflow. The question opens a door; it doesn't close one.

What genuine engagement sounds like

Students who wrote their own work - even with some AI assistance - can usually describe their reasoning in specific terms. 'I wasn't sure about the second paragraph, so I moved the structure around.' 'I looked up what cognitive dissonance actually meant because I wanted to use it correctly.' That kind of memory is hard to fabricate.

What you're listening for is not a perfect process. Everyone cuts corners. You're listening for evidence that the student was present in the work: that they made choices, hit obstacles, and thought about the content rather than just submitting the output of a prompt.

When to escalate - and when not to

Most of these conversations end one of three ways: the student demonstrates clear engagement and you move on; they acknowledge some AI use that, on reflection, doesn't cross your policy line; or there's a real gap between the submission and what they can explain.

Only the third case warrants escalation to formal procedures, and even then, document what you have first. A timestamped process log from a tool is far stronger evidence than a prose score from a detector. 'The submission contained a single paste of 850 characters at minute three of a six-minute session' is a statement of fact. 'The detector gave it a 91% AI score' is a probabilistic claim with a documented false-positive problem.

The instinct to act fast on AI suspicion is understandable, but most of the harm in these situations comes from acting before there's enough to go on. A well-placed curiosity question almost always tells you more - and leaves you in a stronger position - than jumping straight to accusation.