Faculty everywhere are navigating the same tension: AI is now part of students’ study habits and future workplaces, yet our courses still need to cultivate original thinking, disciplinary methods, and academic integrity. A blanket ban won’t get us there. What works is being explicit, designing for process, and choosing when AI is a helpful scaffold and when it’s a distraction. This guide offers concrete practices you can adopt today, drawn from our workshops and field-tested tools.
Guiding principles
- Start with learning outcomes. Clarify what knowledge, skills, and habits you are assessing, then design your AI policy and assignment scaffolds around those goals. If AI is part of the work, add a measurable AI-related outcome and map it to tasks and grading.
- Align with authentic practice. Students’ future work will include AI; sustainable assessment prepares them to use it well, not hide from it.
- Be transparent and consistent. Spell out where AI is allowed, how to acknowledge it, and how accountability works. Treat this as a dialogue with students, not a trap.
- Emphasize process and metacognition. Require notes, drafts, reflections, peer review, and revision logs. These make learning visible and discourage shortcutting.
Build a course AI policy that fits your goals
Effective policies live in two places: the syllabus (course-level stance) and the assignment sheet (task-level rules). Use these six questions to draft yours:
- When is AI use allowed or prohibited?
- How should students acknowledge AI assistance?
- What cautions apply (hallucinations, fabricated citations, biased outputs)?
- How will students be responsible for any AI-assisted content?
- How will you encourage ethical, responsible use?
- How will students give input on the policy and reflect at the end of the course?
These prompts anchor clear, humane policies and reduce confusion on day one.
Three common policy stances (use what fits)
- Restrictive (mitigate): No AI for idea generation or drafting; permitted for mechanics (grammar, formatting) with disclosure.
- Balanced (support): AI allowed for brainstorming, summarizing sources, citation help, and language feedback; not allowed to “write for you.” Acknowledge any use.
- Permissive (elevate): Broad AI use allowed across the workflow with explicit acknowledgement and full student accountability for accuracy and ethics.
Design assignments that make learning visible
Use our AI Assignment Audit to align tasks with your goals, anticipate where AI might help or hinder, and write the rules students need. The audit walks you through: naming goals, mapping possible AI assists, connecting those to your policy, and planning scaffolds like notes, outlines, drafts, and reflections.
Example from a first-year writing assignment: allow AI for language polish and transition ideas; require students to do their own reading, note-taking, and argument analysis; and teach citation tools explicitly. This preserves the intended learning while acknowledging useful supports.
“How much is too much?” Decide per task, not per course
Rather than a single rule, specify the level of AI support by activity. For presentations, you might allow slide structuring but require students to generate the claims. For papers, you might allow topic brainstorming and language feedback while prohibiting AI-generated paragraph drafts. For peer review, consider AI-assisted tone editing or translation to increase inclusion. Always require students to cite and describe any AI use.
Use the How Much Is Too Much? guide to walk through scenarios and set your thresholds clearly on the assignment sheet.
Proactive prevention beats after-the-fact policing
- Scaffold milestones with required artifacts and revision history (e.g., OneDrive versioning, Track Changes).
- Share or co-create citation libraries (Zotero, shared folders) to validate sources and reduce fabricated citations.
- Offer authentic alternatives like podcasts, case studies, infographics, and short video analyses when those better assess your outcomes.
- Publish expectations early in your syllabus and each assignment; require acknowledgement of any AI assistance.
If you suspect misuse, respond with clarity and care
Return to the assignment directions and your AI policy. Confirm what you allowed and how students were asked to cite AI. Compare the submission to prior work for voice consistency, spot-check sources, and only treat detectors as advisory signals. Then meet with the student using your standard academic integrity process.
Model responsible faculty use of AI
Using AI to draft activities, generate practice questions, adapt tone for accessibility, or brainstorm alternative assessments can save time and improve clarity. If you use AI in ways students will encounter, say so. It normalizes disclosure and demonstrates critical evaluation.
Syllabus-ready policy snippets
Balanced syllabus statement (edit to fit): “In this course, AI tools may support early-stage work like brainstorming, summarizing sources, formatting citations, and language refinement. AI may not generate ideas or write sections of your assignments. You are responsible for the accuracy and ethics of any work you submit. Clearly acknowledge any AI assistance in your submission.”
Permissive syllabus statement (edit to fit): “You may use AI tools throughout your workflow. Acknowledge any assistance, verify accuracy, and ensure sources are legitimate. You are accountable for the quality and ethics of your final work.”
Quick workflow: from policy to practice
- Use the Course Impact Checklist to locate where AI touches your course and what to update first.
- Draft your syllabus and assignment policies with the six key questions.
- Run your major assignments through the AI Assignment Audit and set “how much is too much” thresholds by task.
- Build in prevention: milestones, source checks, and reflection.
Why this approach works
It keeps the focus on disciplinary learning, reduces ambiguity, and prepares students for the realities of knowledge work. Research on assessment in a digital world argues for authentic, future-oriented tasks and developing students’ evaluative judgment; your course-level stance plus assignment-level rules honor both.
Resources
- Course Impact Checklist (scope your course, update outcomes and policies).
- AI Assignment Audit Worksheet (goals → policy → scaffolds).
- How Much Is Too Much? (set task-specific allowances).
- Proactive Prevention (milestones, source checks, alternatives).
- Suspection Detection (what to do if you suspect misuse).
- Presentation: AI, Wooster, and You (policy options and examples).
- Re-imagining University Assessment in a Digital World (theoretical backbone for authentic, sustainable assessment).