Institutional approaches to artificial intelligence policy and guidance in health informatics and information management education: emerging trends and inconsistencies.

Abstract

To examine the characteristics, scope, and thematic content of institutional and program-level academic artificial intelligence (AI) policies and guidance documents used by CAHIIM-accredited Health Informatics (HI) and Health Information Management (HIM) master's programs in the United States. This convergent mixed‑methods study examined publicly available AI‑related documents (eg, student handbooks, integrity policies, program manuals) from all 48 CAHIIM‑accredited HI and HIM master's programs listed in November 2025. Forty programs (83%) had at least 1 qualifying document, 8 programs had no qualifying documents. Quantitative analyses summarized policy characteristics and examined associations with delivery modality using Fisher's exact tests. Qualitative analysis applied Latent Dirichlet Allocation topic modeling to identify recurring semantic themes across policy texts. Most documents provided guidance rather than formal policies; neither policy type nor audience differed by delivery mode. Content emphasized academic integrity, responsible AI use, and expectations for student conduct. References to privacy, intellectual property, and regulatory concepts were present but less common than academic integrity-related themes. Topic modeling identified 4 dominant themes: academic integrity and appropriate AI use; generative AI in teaching; AI and data‑tool use in research; and student engagement with conversational AI systems. AI guidance and policy are available across most accredited HI and HIM master's programs; however, they vary in structure and scope. The majority of documents emphasize academic integrity and responsible AI use. Future research could examine how academic program AI policies relate to distinct but related domains such as curriculum development and organizational AI governance.