AI RESEARCH
Compounding Disadvantage: Auditing Intersectional Bias in LLM-Generated Explanations Across Indian and American STEM Education
arXiv CS.CL
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ArXi:2601.14506v2 Announce Type: replace-cross Large Language Models (LLMs) are rapidly being adopted by STEM-focused educational institutions and students worldwide. They generate personalized instructions, explanations, and provide feedback on demand. However, these systems tailor instruction to graphic signals rather than nstrated ability. In such cases, personalization becomes a mechanism of inequality. We conduct one of the first large-scale intersectional audits of LLM-generated STEM educational content, constructing synthetic student profiles.