Generative AI Policies at U.S. Public Health Schools

18 U.S. public health schools provided generative AI use policies, analyzed via mixed-methods. The dataset contains the policy documents and analysis results from a study by Ashish Joshi, published in April 2026. It characterizes the scope and themes of early AI governance in this academic sector.

Use Cases

• Analyze TF-IDF results to identify key recurring terms like 'academic integrity' or 'data privacy' across policy documents.
• Compare thematic analysis findings on shared focus areas such as pedagogy, legal guidance, and ethical use frameworks.
• Use descriptive analysis data to benchmark policy prevalence, such as the 18 policies found among 155 surveyed schools.

Strengths

• Analysis is based on a defined corpus of 18 institutional policy documents.
• Employs a mixed-method design including content, thematic, and TF-IDF analysis for triangulation.

Limitations

• Limited scope with only 18 policies from a potential pool of 155 schools, indicating low adoption or public availability.
• Dataset size is 14.7 KB, containing analysis results and likely excerpts, not the full raw document corpus.
• Findings are specific to U.S. public health schools, limiting generalizability to other disciplines or regions.

Provenance

Source

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

Collection Method

Documents were screened for relevance and accessibility, then analyzed via descriptive, content, TF-IDF, and thematic analysis.

Time Range

Policies were collected and analyzed circa 2026.

Freshness

Data reflects policies analyzed and published in April 2026.

Geography

United States.