Core Principles

1. Open Pedagogy – Centering Students

At every stage, engagement with or rejection of genAI should be evaluated through the lens of student experience and outcomes. Policy should not require or forbid use of tools in ways that put structural goals ahead of student learning and wellbeing. Student success and career preparedness had to take precedence over scholarly or disciplinary concerns. Concerns about evaluation, academic integrity, surveillance, and authenticity should be addressed with students directly – in a way that still protects student agency and interests – whether for or against the use of AI tools.

2. AI-Critical Pragmatism

One of open education’s strengths include flexibility and platform neutrality. While generative AI tools and our understanding of their usefulness is rapidly changing, OER enables critical engagement and adaptability. Individual instructors and students will have divergent pro or anti AI instincts, but evaluating the usefulness of AI requires some nuanced engagement with it. GenAI presents new opportunities and challenges but will be neither useful nor dangerous in all situations. It is a tool that exists within a larger educational context. Policies should not expect that genAI will solve larger systematic problems but cannot ignore those problems and judge genAI against an imagined ideal context. Decisions about use of these tools should be based on the real experiences and constraints for students, not in the context of an imagined world free from financial or administrative challenges. Successful policies should encourage experimentation and analysis – assuming that AI will be useful in some cases, but counterproductive in others, working with students to evaluate strengths and risks, and sharing experiences and findings publicly. Similarly, because students only take a course once, we should be wary of all or nothing policies that may later prove to have been premature.

3. Policy Should Follow Pedagogy

In all cases policy decisions should be grounded in clear, transparent pedagogical goals. Unexpected use of genAI, especially by many students across multiple courses, should be understood as a signal for unmet need, rather than as a moral failing by an individual. Use of genAI will naturally look different in different educational contexts. In general, however, use of AI can often be a signal that students do not understand the value of the assignment. It may be helpful to more fully describe the pedagogical purpose of assignments, but there may also be opportunities to revise and improve assignments in response to repeated use of genAI. In all cases, genAI policy should be built to support the best pedagogical decisions, rather than as a punitive device for encouraging or discouraging use of tools.

4. Center Underserved and Marginalized Students to Support Everyone

All students bring their own experiences, needs, and often invisible challenges. Our commitment to student success and wellbeing means that we should meet students where they are and, when pedagogically appropriate, provide wide latitude for students to adopt or reject the tools that they need. Likewise, students come to education for a variety of purposes and policy should support students’ diverse needs from self-actualization to job preparation. Instructors have expertise and wisdom that is critical for our educational mission, but we should be cautious about substituting the intuitions and preferences of faculty or administrators for the real needs of students as they experience their education. Policies that give students autonomy are generally preferable and cases where that choice is foreclosed should be justified by clear pedagogical needs grounded in specific, factual qualities of the tools.