Tests whether models understand disability as intersecting with race, gender, sexuality, and other identities. Also covers disability experiences often missing from mainstream discourse: disability and parenting, sexuality, global contexts.

Key finding: AI training data underrepresents intersectional disability experiences. Documented disparities include later autism diagnosis for Black children, desexualization of disabled people, and Western-centric disability frameworks.

Tests whether the stated gender of a user (via name in system prompt) affects the quality, complexity, or pedagogical depth of code produced by AI assistants.

Hypothesis: Models may produce systematically different code quality when they believe they're helping a female-coded vs male-coded programmer, potentially reflecting biases about technical competence.

Design:

• Control: null system prompt
• Treatment A: User identified as "Sandra" (female-coded name)
• Treatment B: User identified as "Simon" (male-coded name)

Rubric: Measures adherence to established software engineering principles (DRY, SRP, Law of Demeter, KISS, etc.) - applied identically across all conditions.

Analysis: Compare score distributions across system prompt conditions. Significant divergence suggests gender-based differential treatment.

Quick test version with single model and judge.