LLMs are Biased Teachers: Evaluating LLM Bias in Personalized Education

This paper evaluates bias in LLMs acting as personalized tutors by measuring how models generate and select educational content differently for students with varying demographic characteristics (race, gender, disability, income, etc.). The study introduces two bias metrics (MAB and MDB) and applies them to 9 LLMs using over 17,000 educational explanations across multiple difficulty levels and subjects.

With the increasing adoption of large language models (LLMs) in education, concerns about inherent biases in these models have gained prominence. We evaluate LLMs for bias in the personalized educational setting, specifically focusing on the models' roles as"teachers."We reveal significant biases in how models generate and select educational content tailored to different demographic groups, including race, ethnicity, sex, gender, disability status, income, and national origin. We introduce and a

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