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    <abstract>This paper introduces custom Large Language Models using sentence-level embeddings to measure teaching quality. The models achieve human-level performance in analyzing classroom transcripts, outperforming average human rater correlation. Aggregate model scores align with student learning outcomes, establishing a powerful new methodology for scalable teacher feedback. Important limitations discussed.</abstract>
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%0 Conference Proceedings
%T Measuring Teaching with LLMs
%A Hardy, Michael
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
%D 2025
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-218-84228-4
%F hardy-2025-measuring
%X This paper introduces custom Large Language Models using sentence-level embeddings to measure teaching quality. The models achieve human-level performance in analyzing classroom transcripts, outperforming average human rater correlation. Aggregate model scores align with student learning outcomes, establishing a powerful new methodology for scalable teacher feedback. Important limitations discussed.
%U https://aclanthology.org/2025.aimecon-main.40/
%P 367-384

Markdown (Informal)

[Measuring Teaching with LLMs](https://aclanthology.org/2025.aimecon-main.40/) (Hardy, AIME-Con 2025)

• Measuring Teaching with LLMs (Hardy, AIME-Con 2025)

ACL

• Michael Hardy. 2025. Measuring Teaching with LLMs. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 367–384, Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).