Crafting AI Syllabus Policies

Guiding Principles as You Draft Your Policy

Align Policy with Pedagogy and Learning Outcomes

Your pedagogy must drive your policy. Your stance should be a reflection of the course's specific learning objectives, so the fundamental question behind any AI rule should be: "How does this policy support or hinder what I want my students to learn?" This alignment should provide a clear and defensible rationale for your students and not an arbitrary restriction. The policy communicates what is valued in the course.

Establish Student Accountability and Responsibility

AI policies should make clear that the student is the ultimate author and is fully responsible for the entirety of their submitted work, regardless of the tools used in its creation. This is especially important given the known propensity of AI tools to "hallucinate" or create false citations. Your policy should be clear that "the AI did it" is not a valid defense for submitting inaccurate or plagiarized work. Students should be taught that AI generated content is raw material that requires verification, evaluation, and revision.

Mandate Transparency Through Acknowledgement and Citation

The foundation of academic integrity is transparency. Rather than attempting to catch or police students using AI, a more effective approach centers on openness and honesty. Even if the policy is total prohibition, there must be clear instructions on how to cite and disclose their use of GenAI tools. This transparency can be anything from "I used ChatGPT to brainstorm ideas" to more detailed policies that detail process documentation throughout a scaffolded assignment. All of the major citation styles have developed formats for GenAI citations, and students should be directed to those resources.

Implement Ethical and Practical Guardrails

A good AI policy should include ethical and practical issues related to GenAI usage. Issues like data privacy, intellectual property, and bias and equity are all essential considerations. Students should be warned against inputting any personal, confidential, or proprietary information, and when possible, be encouraged to use institutionally vetted AI tools. Students should also be reminded that course materials should not be uploaded into public AI tools without the instructor's or publisher's explicit permission. Finally, GenAI models trained on vast datasets from the internet can perpetuate and amplify harmful stereotypes — students should be encouraged to critically analyze content for such biases.

Foster a Culture of Inquiry, not Accusation

Given the limitations on AI detection tools, policies should actively discourage their use as a primary basis for academic misconduct allegations. An approach centered on policing and detection can create a climate of fear and mistrust. It's better to build a policy that fosters a culture of dialogue and trust. The first step in any inquiry about inappropriate AI use should be a conversation framed with curiosity, not an accusation. Focus on inappropriate AI use as a teachable moment, especially for first time or unintentional violations.