Generative AI Policies at U.S. Public Health Schools

18 out of 155 American Schools and Programs of Public Health had a formal generative AI use policy at the time of this study. The dataset contains the analyzed policy documents and results from mixed-method analyses including TF-IDF and thematic coding. Researcher Ashish Joshi published this analysis in April 2026.

Use Cases

• Analyze TF-IDF results to identify key recurring terms across the corpus of 18 policy documents.
• Compare thematic analysis results on shared focuses like academic integrity, data privacy, and research ethics.
• Use the classification of documents as 'guidelines' or 'policies' to model factors influencing policy formalization.
• Benchmark the 18 identified policies against the total 155 member schools to assess adoption rates.

Strengths

• Analysis is based on a defined population of 155 U.S. public health schools and programs.
• Employs a mixed-method design including descriptive, content, TF-IDF, and thematic analysis for triangulation.
• Documents are screened for both relevance and public accessibility.

Limitations

• The dataset is very small, containing analysis from only 18 identified policy documents.
• The study's scope is limited to U.S.-based ASPPH member institutions, not a global sample.
• The underlying source documents are not included in the 15.2 KB file, only the analysis results.

Provenance

Source

American Schools and Programs of Public Health (ASPPH) member institutions.

Collection Method

Mixed-method design involving screening of publicly available guidance documents, followed by classification and content analysis.

Time Range

Study conducted circa 2026; temporal coverage of source documents is not specified.

Freshness

Data reflects a snapshot as of the study's publication in April 2026.

Geography

United States.