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            <namePart type="given">Joshua</namePart>
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    <abstract>We built a near-comprehensive automated item evaluation model, predicting historical item acceptance or rejection from item text and Qwen3-generated critiques using 52,759 items from a large-scale testing program. Two DeBERTaV3 classifiers fused reached AUC .80 overall and .86 for math. Fairness-related rejections remained difficult, underscoring the need for human review.</abstract>
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%0 Conference Proceedings
%T Automated Item Evaluation: Predicting Item Acceptance and Rejection using LLM-Generated Critiques
%A Maeda, Hotaka
%A Lu, Yikai
%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 2026
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-9983004-1-7
%F maeda-lu-2026-automated
%X We built a near-comprehensive automated item evaluation model, predicting historical item acceptance or rejection from item text and Qwen3-generated critiques using 52,759 items from a large-scale testing program. Two DeBERTaV3 classifiers fused reached AUC .80 overall and .86 for math. Fairness-related rejections remained difficult, underscoring the need for human review.
%U https://aclanthology.org/2026.aimecon-wip.16/
%P 113-129

Markdown (Informal)

[Automated Item Evaluation: Predicting Item Acceptance and Rejection using LLM-Generated Critiques](https://aclanthology.org/2026.aimecon-wip.16/) (Maeda & Lu, AIME-Con 2026)

• Automated Item Evaluation: Predicting Item Acceptance and Rejection using LLM-Generated Critiques (Maeda & Lu, AIME-Con 2026)

ACL

• Hotaka Maeda and Yikai Lu. 2026. Automated Item Evaluation: Predicting Item Acceptance and Rejection using LLM-Generated Critiques. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 113–129, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).