From leadership research to district action: Investigating the use of generative AI policies among ASPPH member schools and programs of public health

AI-assisted editorial implementation guide. Proposed district actions are separate from the original authors’ findings and recommendations. Independent peer review of this guide is not asserted.

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Evidence and transfer

Education-related source: verify which participants, institutions and outcomes were actually studied before district adoption. Source publication type: systematic-review. License and identity checks do not establish study quality or causality.

Original authors’ abstract, reproduced under the source license:

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.

Study methods

ASPPH’s “Academic Program Finder” was used to create our sample. Three research team members extracted data from October 2024 to January 2025 from each ASPPH member’s website. Documents containing AI policies/guidelines were then downloaded from 153 members’ respective websites. One of the member schools, the Colorado School of Public Health, is a collective of three universities, this resulted in an initial collection of 155 documents. Each ASPPH member website was reviewed to determine if there were any references to the AI Policies/Guidelines at the school or university level.

Limitations

There are several limitations in this study. First and foremost, the results and discussion reflect available AI use guidance until 3/1/2025. It is likely that some ASPPH member schools have since developed or enhanced their policies or guidelines to the point that they would have warranted inclusion into our final corpus of policies. As such, the results should be taken as a reflection of the state of AI guidance in ASPPH member schools during the document screening process, which lasted from approximately 10/2024 to 3/2025. Furthermore, it is important to note that many schools circulate AI policy internally and do not formally publish AI guidelines on their websites. Additionally, given the results of our interrater reliability analysis, it is possible that stakeholders outside of the research team might have different opinions on whether a certain school’s guidance should be considered a guideline or policy and whether it should have been considered for the subsequent analyses.

A 90-day district inquiry

Days 1–15 · Appraise. Read the source methods, recruitment, comparator, outcomes and limitations. Record how the study setting differs from your district and which claim the design can support.
Days 16–30 · Design. Select one recurring district decision. Publish the decision owner, evidence requirements, consultation process and appeal route; test the process on a bounded low-risk decision. Agree participation, privacy protections, baseline and stopping rules before starting.
Days 31–60 · Observe. Collect the pre-agreed measures, keep a log of implementation changes and invite confidential voluntary feedback. Preserve existing support for staff and learners.
Days 61–90 · Decide. Track decision time, unresolved objections, documented reasons, appeals and access differences. Review whether the process changes who can participate and who carries the burden. Make a dated decision to stop, revise or extend the inquiry.

Board decision brief

Present the local problem, source setting, evidence limits, intervention, responsible owner, total cost, measured changes and uncertainty. Separate staff perceptions, implementation measures and student learning outcomes.

Attribution and reuse

Ashish Joshi; Brian McGoldrick; Nidhi Mittal; Shongkour Roy; Zebunnesa Zeba; Michael Arthur Ofori; Stella Dockery; Niharika Jha; John Auerbach; Jean Cadet; Jessica Kruger; Robert Leeman; Kerry Mitchell; Guillaume Molter; Andrew Naidech; Elahe Nezami; Sylvester Orimaye; Courtney Queen; Umair Shah; Abdul Shaikh; Daniel Smith; Melissa Tracy; Edward Trapido; Jorg Westermann; Andrew Wiss; Jonathan Zaccarini; Tim E. Leshan; Linda A. Alexander; Jamie M. Atchison; Eduardo Ruiz; Laura Magana. Investigating the use of generative AI policies among ASPPH member schools and programs of public health. Frontiers in Public Health (2026-04-08). https://doi.org/10.3389/fpubh.2026.1796810

Prepared 2026-11-07. Source authorship and DOI are preserved. The abstract and source passages are attributed extracts; this editorial guide is offered under the same CC BY 4.0 license.