Generative AI Policies at U.S. Public Health Schools

Eighteen U.S. public health schools have generative AI use policies, according to a 2026 analysis by Ashish Joshi. The study characterizes these policies through content, TF-IDF, and thematic analysis of official documents. It identifies common themes and areas for development in AI governance.

Use Cases

• Identify recurring policy terms like 'academic integrity' or 'data privacy' via TF-IDF analysis of the document corpus.
• Classify AI guidance documents as 'guidelines' or 'policies' based on their defined content and structure.
• Analyze shared themes across policy documents, such as research ethics or pedagogical integration of AI.
• Benchmark the scope of AI governance by comparing the 18 identified policies against the total 155 member schools.

Strengths

• Analysis based on 18 identified generative AI use policies from a defined population of 155 schools.
• Employs a mixed-method design including descriptive, content, TF-IDF, and thematic analysis for triangulation.

Limitations

• Very small sample size of only 18 policies, limiting generalizability.
• Dataset is a single 14.2 KB document, offering no raw data or structured records for computational analysis.
• Findings are specific to U.S. public health schools and may not reflect other disciplines or geographies.

Provenance

Source

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

Collection Method

Documents were screened for relevance and accessibility from member schools, then analyzed via mixed-methods.

Time Range

Policies were collected and analyzed circa 2026.

Freshness

Last updated in April 2026.

Geography

United States.