Academic integrity and AI

Generative AI is already being used by both students and faculty. The use of AI tools has fundamentally complicated traditional notions of academic integrity. With easy and free access to AI tools, and with AI integrated into various platforms we use (e.g., Microsoft 365, Canvas LMS, etc.), there have been concerns about academic integrity as the use of AI can undermine the authenticity of student work. It is essential that we establish clear guidelines in our classes about when and how AI use is appropriate.

Faculty and students should also be familiar with the University’s Academic Standards policy (UAM 6,502). Any sanctions for violations of the academic standards policy that are stated in a course syllabus must align with those specified in UAM 6,502. As such, faculty may not create their own sanctions that differ from the policy. Adhering to the official policy ensures consistency, fairness, and clarity in upholding academic standards across the university.

It is also important to note that AI detection tools are not fully reliable and should not be used as the sole determining factor in deciding whether a violation of the academic standards policy related to AI usage has occurred. These tools may produce false positives or false negatives, and their results should be considered alongside other evidence and context.

Many discussions on AI and academic integrity have taken place to help faculty navigate this challenge. In addition to clear guidelines and transparency, we recommend shifting our focus from the product to an emphasis on the learning process. Below are some resources you may find helpful.

• James Lang’s podcast, Designed for Learning. 7. AI, cheating, and trusting students to be human.
• Tricia Gallant & David Rettinger (2025). The opposite of cheating: teaching for integrity in the age of AI. University of Oklahoma Press. (The authors also host a podcast The Opposite of Cheating when they talk with experts about teaching for integrity in the age of AI.)