It is the last week of the semester and a faculty member is reading a paper that does not quite feel right. The citations are real, but two of them point to a textbook the class did not use. The middle of the paper reads in a different voice than the introduction. Running the paper through an AI detector, half out of curiosity, returns a high score. Now what?
The options are familiar to anyone who has been through this: Email the student and ask. Fail the assignment. Refer it to the dean of students. File an academic-integrity report. Drop a grade and say nothing. Each path carries different consequences for the student, procedural obligations for the institution and personal exposure for the faculty member. The syllabus does not say which path is which, and the institutional AI policy, if one exists, tends to say that AI use may be a violation and stops there. So, the faculty member improvises.
This is where most of the legal exposure in academia’s AI moment lives. The field has spent three years debating whether AI use constitutes cheating; the more pressing, largely unresolved question is what faculty and administrators are actually supposed to do when they suspect it. Most institutions have left that question unanswered. The result is faculty acting on instinct, students facing inconsistent processes and institutions absorbing risk that better procedure would prevent.
