Higher education is wrestling with two concurrent AI problems: inconsistent campus policies and the unintended consequences of AI-detection tools. Purdue’s institutional AI strategy—widely publicized for making AI competency a graduation requirement—sparked debate about whether a coherent campuswide vision is the right response. Critics say many institutions still default to policing and plagiarism detection rather than articulating curricular uses and guardrails. Faculty report that AI-detection tools are reshaping student behavior: some students only began using AI after learning detectors’ stylistic triggers, while nonnative speakers face disproportionate risk of false positives. The fragmented landscape—course-by-course rules, inconsistent departmental guidance and uneven detector accuracy—has fostered confusion and adversarial relationships between students and faculty. College leaders and academic technologists are now being urged to move from reactive enforcement to instructional design: clarify permitted uses, incorporate AI literacy into learning objectives, and invest in assessment models that evaluate student reasoning. The debate highlights the operational challenge of aligning pedagogy, academic integrity and technologically literate graduates.