material

GenAI Policy Material Collection

Publications on AI Policies in Higher Education

1. C. Orfanidis, “Moral diversity in institutional policies governing the student usage of generative AI: An international comparison,” Higher Education Quarterly, vol. 79, no. 4, 2025, doi: 10.1111/hequ.70051
   • Analyzed 36 GenAI policies of HEIs in Canada, UK, and USA, and identified three dominant moral framings that represent different perspectives on GenAI:
     1. The Moral Consistency perspective understands GenAI as disruption, as a danger to the integrity of higher education. Such policies restrict or even reject GenAI use. Practically, course instructors have the ultimate moral authority, deciding what is wrong and right, which in turn requires students to figure out whether AI is permitted for each course.
     2. Responsible Futureproofing, in contrast, embraces GenAI and sees a moral obligation of having students use GenAI to prepare them for their future worklife. Naturally, policies with such a framing positively reinforce GenAI use by students while also highlighting ‘responsible use’. The teacher’s role in such policies is often not clearly defined.
     3. Constructive Skepticism is a middle ground between the other two. It considers GenAI to be “experimental”, always requiring “ethical guardrails”. Such hybrid policies address both students and faculty to some degree, mostly leaving it to faculty to decide how much is permissi ble in their courses. A practical consequence are, again, decentralized policies and “questionable adherence by their staff, students, and faculty”.
2. L. Mack and A. Byanjankar, “Understanding policy responses to generative AI in private higher education: Insights from japan and the united states,” Journal of Academic Ethics, vol. 23, 2025, doi: 10.1007/s10805-025-09655-7
   • Analyzed GenAI policies of 22 HEIs: These primarily detail how not to use GenAI rather than providing explicit guidance on how to maintain academic integrity when using GenAI.
   • Argue in favor of institution-wide instead of course-level policies to limit students’ confusion which is the natural consequence of asking the students to follow whatever rules apply in the course.
3. Y. Jiang, L. Xie, and X. Cao, “Exploring the effectiveness of institutional policies and regulations for generative AI usage in higher education,” Higher Education Quarterly, vol. 79, no. 4, 2025, doi: 10.1111/hequ.70054
   • Uses the Policy Enactment Framework to explain how ‘policy actors’ (here: teachers) are necessary to interpret the policies.
   • Shows that even if GenAI policies are vague, students do not see this as cause to be extra careful: The ease of use and perceived usefulness of AI systems override any concerns regarding policy compliance. Even banning tools does not effectively deter them.
4. M. Smit, R. F. Wagner, and T. J. Bond-Barnard, “Ambiguous regulations for dealing with AI in higher education can lead to moral hazards among students,” Project Leadership and Society, vol. 6, 2025, doi: 10.1016/j.plas.2025.100187
   • Explain student behavior in response to ambiguous GenAI policies with Moral Hazard theory: If individuals do not bear the full consequences of their actions (e.g., because they do not consider long-term impacts on their learning), they are more likely to engage in risky behavior (e.g., misusing AI).

AI Literacy Frameworks

UNESCO AI competency framework for students (2024)	UNESCO AI competency framework for teachers (2024)	OECD AI literacy framework (2026)