Investigating the use of generative AI policies among ASPPH member schools and programs of public health

Background: The integration of generative artificial intelligence (AI) into the operational and curricular foundations of higher education is a dynamically evolving issue, particularly in public health programs looking to ethically embrace the ever-growing potential of AI in health research and education. Despite a collective priority across the United States to effectively adopt generative AI, few works have characterized the scope and rigor of existing guidance. Methods: This study employs a mixed-method design to characterize AI use guidance offered by member schools of the American Schools and Programs of Public Health. Guidance documents are screened for relevance and accessibility, with the remaining documents being classified as guidelines or policies. The determined policies are analyzed for content via descriptive analysis, content analysis, TF-IDF analysis, and thematic analysis. Results: Out of a possible 155 schools, 18 schools were determined to have a generative AI use policy. TF-IDF analysis identified recurring terms of importance throughout the corpus of policy documents. Content and Thematic Analysis identified areas of focus and themes shared throughout the corpus of documents. The resulting triangulated data are used to identify strengths and shortcomings in the extant policies with suggestions for further development. Conclusion: The results of the mixed-method design indicate that extant generative AI guidance in schools and programs of public health in the United States is in its relative infancy. Shared themes and areas of focus show a predominant focus on the intersection of academic integrity and AI use among students and faculty. Additionally, data privacy and security, research ethics, and other areas are currently under consideration. This study demonstrates that AI use governance is growing but narrow in scope. Further development in the areas of pedagogy, legal guidance, and ethical use frameworks are needed in order to meet the growing demand to integrate generative AI into US higher education.