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arXiv AI By Liliana Santos-Deonizio, James Malamut, Ram\'on Mart\'inez, Dorottya Demszky

When Youth Enter The Chat: An Epistemic Shift in the Validation of LLM-Based Measures of Student Talk

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The article discusses the growing use of large language models (LLMs) to assess student discourse at scale, noting that current validation methods—such as expert annotations and F1 scores—often ignore the contextual and cultural nuances of student language. It argues that these practices inadequately capture the experiences of racially and linguistically marginalized youth, and proposes re‑contextualizing classroom conversations and involving students as epistemic authorities. A case study with multilingual 8th‑grade math students demonstrates misalignments between student self‑interpretations and LLM outputs, underscoring the need for youth participation in validating LLM‑based measures.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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