Policy Sketches
Syllabus language, assignment rules, and course-level frameworks for thinking through how AI belongs in learning.
These are not model university policies, and they are not meant to settle the question of AI in higher education. They are decision tools for making local choices visible: what AI is allowed to do, what students are being asked to demonstrate, and where a policy might fail.
How to read these
Treat each policy sketch as a starting point. Adapt the language to your course, your students, your campus rules, and the expertise of people already working on AI literacy, research support, accessibility, academic integrity, and student success.
Differentiate Yourself From AI
Key questionWhat if the focus is on the product, not the process?
What it clarifies- AI use does not remove responsibility for the final work
- generic AI-like work may not provide enough evidence of learning
- students are expected to go beyond what AI can produce for free
AI Integration Ladder
Key questionHow can a course distinguish between different levels of acceptable AI use and make those levels usable on assignments?
What it clarifies- AI use is not binary, but depends on the task
- different levels of AI use require different forms of accountability
- assignment labels are clearest when they say what AI may do, what it may not replace, and what students should make visible
AI Use Notes
Key questionHow can students disclose AI use in a way that supports learning?
What it clarifies- AI use can be documented as part of process
- disclosure should distinguish assistance from substitution
- students are accountable for accepting, rejecting, and revising AI output