Most of the academic-integrity conversation around AI points at students. Did they write this? Did a chatbot? Should I run it through a detector? That’s a real question, and we’ll get to it. But there’s a quieter one that almost nobody is asking out loud, and it’s the one that should keep you honest: where is the line for my AI use?

Because you use it too. You ask it to generate a practice paper, draft a feedback comment, summarise a class’s performance, or polish a report. None of that is cheating — but not all of it is automatically fine either. If you’re an IGCSE or A-Level teacher quietly building AI teaching tools into your week around Cambridge and Edexcel content, this is an honest map of where that use is clearly defensible, where it gets murky, and how to coach students from a position you’ve actually thought through. No moral panic, no lecture. Just the lines worth being careful about.

Start with the right question

The wrong frame is “is AI allowed?” The useful frame is: who is accountable for this piece of work, and does the person reading it know what they’re reading?

Academic integrity has never really been about tools. A student copying from a textbook by hand in 1995 was committing the same offence as one pasting from a chatbot today — passing off someone else’s work as their own, or misrepresenting how it was produced. AI just makes that faster and harder to spot. The same logic applies to you. The question isn’t whether you touched an AI; it’s whether the output is honestly attributed and whether you remain the responsible professional behind it.

Hold onto that and most of the grey areas resolve themselves.

Where teacher AI use is clearly fine

Let’s name the green zone plainly, because teachers often feel a vague guilt about uses that are completely legitimate.

Generating practice material. Asking AI to draft topical questions, a mock in the style of a paper, or differentiated versions of a worksheet is no different from adapting a question bank or a colleague’s resource. You’re the one selecting, editing and deciding it’s fit to put in front of a class. The accountability stays with you.

Marking support against the mark scheme. Having a tool do a first pass on a class set — crediting the points an examiner would award, flagging what’s missing — is a workflow decision, not an integrity one, provided you review it. The mark a student receives is still your mark. (More on staying the accountable marker below.)

Drafting feedback you then shape. Using AI to produce a first draft of “why this answer lost marks and what a better one looks like,” which you read, correct and personalise, is fine. You’re using it the way you’d use a comment bank — a starting point, not the final word.

Saving yourself admin time. Reformatting, summarising your own notes, turning bullet points into a tidy email. Low stakes, nobody misled.

In all four, two things are true: the work is honestly what it appears to be, and you’d happily say out loud how you produced it. That’s the test.

The grey areas worth being careful about

Now the murkier ground — not forbidden, but where a thoughtful teacher slows down.

AI-written reports and comments presented as personal. This is the one I’d flag hardest. When a parent reads “Aisha has grown wonderfully in her confidence with rates of reaction this term,” they believe a teacher who knows Aisha wrote it. If an AI generated that sentence from a grade and a couple of prompts, and you pasted it unedited, you’ve quietly misrepresented the relationship. It’s not plagiarism in the exam sense, but it’s the same family: passing off generated text as the product of personal knowledge.

The fix isn’t to ban AI from reports — drafting structure or wording from your genuine observations is fine. The fix is that the substance has to be true and yours. If you can’t vouch for every claim in a comment because you don’t actually remember the student doing that, the AI has stopped helping and started inventing.

Feedback that you don’t actually read. A first-pass comment you review is support. A comment you forward sight-unseen is a risk — to the student, who may get something subtly wrong, and to you, because your name is on advice you never checked.

Anything you’d be uncomfortable disclosing. A reliable instinct: if you’d hesitate to tell a parent, a moderator, or the student themselves that AI produced this, that hesitation is information. Sit with it before you send.

These aren’t reasons to stop. They’re reasons to stay in the loop on the outputs that carry your professional judgement.

How to stay the accountable marker

The single principle that keeps teacher AI use clean: you are the final marker, always. AI can mark first; it cannot mark last.

In practice that means a few habits:

• Treat AI marks as a draft, not a verdict. Review the borderlines and the high-tariff answers before any mark is reported.
• Use the override, every time it’s warranted. A tool that won’t let you change a mark — and show the student your decision rather than the machine’s — isn’t one to trust with reported grades.
• Keep your sign-off explicit. For anything that goes on a report or into a moderation sample, the grade should carry your professional confirmation, not a silent machine output.

This isn’t bureaucratic box-ticking. It’s the thing that lets you answer “did a teacher mark this?” with an honest yes. If you want the longer version of the marking-specific judgement calls, I’ve written about what AI marking gets right and where it still needs your eyes.

Coaching students: set the expectation early

You can’t credibly police student AI use if your own is fuzzy — which is exactly why the sections above come first. Once you’re clear, the student conversation gets much easier, and it’s more about expectations than detection.

Be specific about what’s allowed. “Don’t use AI” is unenforceable and, frankly, dishonest about the world they’re entering. Far better: spell out the zones. Using AI to explain a concept you didn’t understand — fine. Using it to generate a paragraph you submit as your own — not fine. Using it to check your own draft for clarity — fine, and tell me you did. Specificity is what makes a rule fair.

Ask for transparency, not abstinence. A simple norm — “if you used AI, note where and how” — does more for integrity than any detector. It moves the behaviour into the open and turns it into something you can teach around rather than chase.

Be honest about detectors. AI-detection tools are unreliable. They produce false positives, they disadvantage second-language writers, and confident accusations based on a detector score have already caused real harm. Use them, if at all, as a prompt to have a conversation — never as evidence. The more durable defence is assessment design, not surveillance.

Design assessment AI can’t quietly do for them

If a task can be completed by pasting it into a chatbot, the task is the problem, not the student. The most reliable integrity strategy is to design assessment where AI use is either visible or genuinely unhelpful.

• Anchor to in-class, observed work for anything high-stakes — short writes under your eye, vivas, “explain your reasoning out loud.”
• Make process visible. Ask for plans, drafts, annotated working. AI produces tidy final answers; it’s far worse at faking a messy, evolving process.
• Ask for the personal and the specific — “use the experiment we ran,” “respond to this source we annotated together.” Generic prompts get generic AI answers; local, specific ones don’t.
• Use AI-assisted tasks deliberately. Sometimes the best move is to let students use AI and then mark their judgement — “the AI gave you this answer; what’s wrong with it?” That assesses exactly the skill that matters now.

This is less about catching cheating and more about building AI literacy as a teacher — and modelling it for students who’ll need it for the rest of their lives.

A word on Cambridge and Edexcel context

At the level of a self-serve teacher, you’re rarely the one filing a malpractice report — but it’s worth knowing the shape of the rules. Both Cambridge and Edexcel treat AI-generated work submitted as a candidate’s own as malpractice, the same category as plagiarism or collusion. For controlled and coursework components especially, the expectation is that work is the student’s own and that teachers can authenticate it — which is precisely why observed, process-visible assessment matters.

For your own use, the boards’ line is consistent with everything above: tools that help you teach and mark are fine; misrepresenting authorship or authenticating work you can’t actually vouch for is not. If your school has an AI policy, follow it; if it doesn’t, the honest-attribution test is a safe personal standard until it does.

Where Tutopiya fits

If you want marking support that keeps you firmly in the accountable seat, Tutopiya’s platform for teachers marks IGCSE and A-Level answers against the actual Cambridge and Edexcel mark schemes, returns examiner-style feedback you can edit, and builds in a review-and-override step — so the AI marks first and you stay the final marker. It’s free to start, which is the right way to test whether the workflow holds up to your own integrity standards before it touches a reported grade.

For the related question of using AI feedback well, see using AI feedback without dumbing down your teaching.

FAQ

Is it cheating for a teacher to use AI to mark or write reports? No — using AI to draft marks or feedback is a legitimate workflow, as long as you review and own the output. The line is misrepresentation: presenting AI-generated claims you can’t personally vouch for as your own knowledge, or reporting a machine mark you never checked. Keep your review and sign-off and you stay on the right side of it.

Should I tell students and parents when I use AI? You don’t need a disclaimer on every worksheet, but the honesty test is useful: if you’d be uncomfortable disclosing that AI produced something — a personal report comment, say — that discomfort means the AI has gone past support into misrepresentation. Anything you’d happily explain is fine.

Are AI detectors reliable enough to accuse a student? No. AI detectors produce false positives and disadvantage second-language writers, and accusations based on a detector score have caused real harm. Treat any flag as a reason to talk, never as evidence. Assessment design is a far more dependable defence than detection.

How do I design assessment that AI can’t just complete? Anchor high-stakes work to observed, in-class tasks; make the process visible by asking for plans and drafts; and ask for the specific and personal — your class’s experiment, a source you annotated together. Generic prompts get generic AI answers; local, specific ones resist it.

Does the exam board allow AI in teaching? Cambridge and Edexcel treat AI-generated work passed off as a candidate’s own as malpractice, and expect teachers to be able to authenticate student work. Your own use of AI to plan, mark and give feedback isn’t restricted in the same way — the duty is honest authorship and the ability to vouch for what you report.

The bottom line

You don’t need to be afraid of AI to use it with integrity — you need to keep two things true: the work is honestly what it appears to be, and you remain the accountable professional behind it. Get your own use clear first, stay the final marker, design assessment that rewards real thinking, and the student conversation stops being a hunt and becomes teaching. That’s not a compromise on standards. It’s how you raise them.

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