Faculty increasingly face legal and due-process risk when academic misconduct decisions hinge on AI-detection tools or AI-generated similarity comparisons. A prominent example is the University of Minnesota case involving Ph.D. student Haishan Yang, where the student was expelled after faculty compared exam answers with ChatGPT-derived output; appellate courts upheld the expulsion. A separate case, Matter of Newby v. Adelphi University, led a New York court to annul an AI-related violation finding and order expungement, citing a determination “without valid basis” where the appeal process was effectively compromised. The emerging takeaway for universities is that misconduct policy—what evidence is required, how notices are issued, and who adjudicates appeals—can determine whether institutions survive litigation when sanctions are severe.