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: ways to name a course posture, clarify assignment expectations, and make local choices visible. The point is not to produce one rule for every class. The point is to help instructors, librarians, programs, departments, and other campus partners ask better questions about what AI is allowed to do, what students must still demonstrate, and where a policy might fail.
How to read these
Treat each policy sketch as a starting point. Some offer syllabus language, some offer assignment-level rules, and some offer a vocabulary for institutional conversation. Adapt the language to your course, your students, your campus rules, and the expertise of the people already working on AI literacy, research support, accessibility, academic integrity, and student success.
A Loose Map
The policy sketches mix levels on purpose:
- Course-level framing helps instructors name the role AI plays across a semester.
- Assignment-level language helps students know what is allowed for a particular task.
- Process documentation helps make student judgment visible without turning every case into a detection problem.
- Caveats and redo paths help policies stay pedagogical rather than purely punitive.
The sketches are written from classroom and humanities research contexts, but many are easier to use with partners. A citation exercise might become stronger with a librarian. A disclosure policy might benefit from a writing program or teaching center. A research workflow might need archival, metadata, accessibility, privacy, or social-scientific expertise.
AI Integration Ladder
Key questionHow can a course distinguish between different levels of acceptable AI use?
ActivityA course policy framework that names different levels of AI use, from prohibited to required, so expectations can vary by assignment.
What students learn- AI rules can change depending on the purpose of the task
- different levels of AI use require different forms of accountability
- permission is not the same thing as integration
Assignment-Level AI Permissions
Key questionWhat does an assignment need to say about AI beyond the syllabus policy?
ActivityA policy sketch for attaching a short AI permission statement to each assignment rather than relying only on the syllabus.
What students learn- AI permissions depend on what an assignment is meant to assess
- students should know what evidence of learning they must produce
- clear assignment language reduces accidental policy confusion
Course AI Postures
Key questionWhat overall posture should a course take toward AI?
ActivityA framework for deciding whether a course treats AI as a forbidden shortcut, limited aid, routine tool, or object of study.
What students learn- AI policies reflect course values and learning goals
- different courses can make different legitimate choices
- a policy should explain its reasons, not only its rules
AI Use Notes
Key questionHow can students disclose AI use in a way that supports learning?
ActivityA policy sketch for asking students to document AI use in a short reflective note rather than treating disclosure as a confession.
What students learn- 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
Differentiate Yourself From AI
Key questionWhat should an AI policy ask students to make visible?
ActivityStudents may use AI, but their submitted work must show intellectual moves, evidence, and judgment that clearly belong to them.
What students learn- AI use does not remove responsibility for the final work
- authorship can be demonstrated through judgment, evidence, and revision
- using AI well requires students to know what they can do that AI cannot