Who Gets to Do Physics? Occupational Stereotypes in AI-Generated Problem Sets

TL;DR

This study examines how AI-generated physics problems embed social stereotypes related to occupations, revealing patterns of occupational stratification and risk portrayal despite technical correctness.

Contribution

It uncovers systematic occupational biases in AI-generated physics problems and discusses implications for teaching and screening strategies.

Findings

Hazardous scenarios concentrated in Migrant Worker problems

Passive-accident framing appeared mainly in Migrant Worker problems

Ownership language was mostly used for CEOs

Abstract

As AI-generated problem sets gain traction in introductory physics courses, their technical correctness is well established - but the social assumptions embedded in their framing have gone largely unexamined. This study analyzes 600 introductory physics problems generated by four AI systems - Grok~4, GPT-5.2, Claude Sonnet 4.6, and Gemini 3 Flash - across structured prompts involving occupations (CEO, Physicist, High School Teacher, Nurse, Construction Worker, and Migrant Worker). Problems were coded on five dimensions: hazard presence, hazard type, agency role, cognitive role, and object ownership. While the physics content is technically sound across all platforms, our analysis reveals systematic occupational stratification in narrative framing. Hazardous scenarios were concentrated in Migrant Worker and Construction Worker problems, with exposure-related hazards (electrocution, burns, radiation, heat or chemical exposure) especially concentrated in Migrant Worker problems. Passive-accident framing - the persona as the recipient of an injury - appeared in one in eight Migrant Worker problems and never appeared for the Physicist, Teacher, or CEO. Possessive ownership language was reserved almost exclusively for the CEO. These patterns suggest that AI-generated physics problems can introduce surface-level diversity while reproducing occupational hierarchies in who acts, who owns, and who is placed at risk. We discuss implications for physics teaching and offer simple screening strategies for instructors using AI-generated problems.