State the purpose
Identify acceptable tools, required disclosure and how students must verify AI-assisted work.
Faculty Resources · Teaching Toolkit
Set clear expectations, protect student and institutional information, and keep human judgment responsible for consequential decisions.
Decide what is allowed, what must be disclosed and what remains independent student work.
↓Align the assignment with the learning you actually need students to demonstrate.
↓Review expectations, evidence and student process before reaching a conclusion.
↓Use AI for brainstorming and drafting while verifying the result and protecting data.
↓Identify acceptable tools, required disclosure and how students must verify AI-assisted work.
Specify the stages or tasks where assistance is allowed and what independent work must remain.
Explain why independent performance matters for the learning outcome and make the expectation visible in the assignment.
Start with the course and assignment rules the student was given.
Look at drafts, sources, revision history, notes and other relevant evidence.
Use a non-accusatory conversation to understand how the work was produced.
If the evidence supports an academic-integrity concern, follow the college’s established process.
Do not treat an AI-detector score as proof. Detector results can be wrong and should not replace evidence, conversation or faculty judgment.
Generate examples, discussion prompts, alternative explanations or starting points for lesson planning.
Use AI to generate a first draft of instructions, rubrics or practice material, then revise it with your disciplinary judgment.
Ask students to critique AI output, verify claims, examine sources and discuss appropriate use.
Do not put protected student or institutional information into an unapproved AI service.
Verify facts, citations, calculations and policy claims rather than treating generated output as authoritative.
Keep a qualified person responsible for grading, accommodations and other consequential decisions.
Be transparent with students when AI materially affects course content or expectations.