When to Use Generative AI
Generative AI is most useful when it expands options or saves drafting time without removing human responsibility. Use the following questions before each task—not just when selecting a tool.
If the task affects admission, grades, progression, misconduct, discipline, student support, or other important rights, stop and follow institutional review procedures. See Responsible AI Use in Higher Education.
What is the purpose?
State the learning or operational outcome first. AI is a reasonable candidate when it helps generate alternatives, transform material you understand, or produce a draft for expert review. It adds little when the activity itself is the learning outcome—for example, when students must demonstrate unaided writing, calculation, or clinical reasoning.
What happens if the output is wrong?
Match the review effort to the consequence. An awkward meeting-email draft is easy to correct. Incorrect feedback, a fabricated citation, or an unfair grade can materially affect a student.
Never treat model output as evidence by itself. For consequential tasks, the decision-maker must inspect the original work and relevant criteria and be able to justify the decision without referring to the model as the authority.
Can you verify it?
Use AI where you or another reviewer can recognise errors. Check factual claims against authoritative sources, recalculate numbers, open cited publications, and compare assessment-related suggestions with the approved learning outcomes and rubric. If reliable verification is impractical, change the workflow or do not use AI.
Are the tool and data appropriate?
Use institutionally approved tools and share only the minimum data needed. Remove direct and indirect identifiers and prefer synthetic examples. Do not enter student records, identifiable work, grades, health or support information, private messages, or confidential research into an unapproved service.
Consent is not a substitute for institutional approval or a complete data protection assessment. Local models may reduce third-party disclosure, but they still require secure operation and an appropriate legal and pedagogical basis.
Is the activity fair and accessible?
Check cost, account requirements, accessibility, language performance, and availability in students’ locations. If AI use is required, provide access and an equivalent alternative for anyone who cannot use the tool. Do not infer a student’s capability or intent from writing style, language variety, or an AI detector score.
How will you communicate the use?
Tell students what is permitted, why the tool is being used, what they should disclose, and who remains responsible. For student-facing or consequential outputs, explain the role AI played and provide a way to correct or challenge the result.
Quick examples
| Use | Examples | Minimum safeguard |
|---|---|---|
| Good starting point | Brainstorm lesson examples; reformat educator-authored material; draft low-stakes communications | Educator reviews before use |
| Context-dependent | Draft feedback; create practice questions; adapt reading level; summarise approved source material | Verify against originals, test accessibility and bias, disclose where relevant |
| Escalate or avoid | Admission, grading, misconduct detection, discipline, student-risk prediction, automated support allocation | Institutional assessment; never use model output as the sole basis |
If the task passes this checklist, start small, test it with representative examples, record failures, and define a condition that would make you stop using the workflow.