#984: Mapping Generative AI Policies in U.S. Universities through LLM-Assisted Content Analysis

This poster reports a content analysis of institutional generative AI policies across the top 100 U.S. universities. Using a locally hosted LLM-assisted coding workflow and human adjudication, we mapped policy orientation, integrity provisions, disclosure rules, pedagogy, ethics, and implementation authority. Findings show cautious maturity: policies are mostly balanced and integrity-driven, while guidance for permitted pedagogical use, disclosure practices, and tool specificity remains uneven.