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    <abstract>This study evaluates large language models (LLMs) for automated essay scoring (AES), comparing prompt strategies and fairness across student groups. We found that well-designed prompting helps LLMs approach traditional AES performance, but both differ from human scores for ELLs—the traditional model shows larger overrall gaps, while LLMs show subtler disparities.</abstract>
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%0 Conference Proceedings
%T Evaluating LLM-Based Automated Essay Scoring: Accuracy, Fairness, and Validity
%A Huang, Yue
%A Wilson, Joshua
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress
%D 2025
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-218-84229-1
%F huang-wilson-2025-evaluating
%X This study evaluates large language models (LLMs) for automated essay scoring (AES), comparing prompt strategies and fairness across student groups. We found that well-designed prompting helps LLMs approach traditional AES performance, but both differ from human scores for ELLs—the traditional model shows larger overrall gaps, while LLMs show subtler disparities.
%U https://aclanthology.org/2025.aimecon-wip.9/
%P 71-83

Markdown (Informal)

[Evaluating LLM-Based Automated Essay Scoring: Accuracy, Fairness, and Validity](https://aclanthology.org/2025.aimecon-wip.9/) (Huang & Wilson, AIME-Con 2025)

• Evaluating LLM-Based Automated Essay Scoring: Accuracy, Fairness, and Validity (Huang & Wilson, AIME-Con 2025)

ACL

• Yue Huang and Joshua Wilson. 2025. Evaluating LLM-Based Automated Essay Scoring: Accuracy, Fairness, and Validity. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 71–83, Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).