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    <abstract>As LLMs increasingly streamline item generation, ensuring the quality of their outputs remains a critical challenge. This study examines whether a multi-agent system can serve as an automated judge to detect and revise flaws in AI-generated educational assessment items, such as ambiguity, bias, or content misalignment.</abstract>
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%0 Conference Proceedings
%T Can LLMs detect item flaws in the questions they have generated?
%A Gorgun, Guher
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting-Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Coordinated Session Papers
%D 2026
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-9983004-2-4
%F gorgun-2026-llms
%X As LLMs increasingly streamline item generation, ensuring the quality of their outputs remains a critical challenge. This study examines whether a multi-agent system can serve as an automated judge to detect and revise flaws in AI-generated educational assessment items, such as ambiguity, bias, or content misalignment.
%U https://aclanthology.org/2026.aimecon-sessions.31/
%P 284-290

Markdown (Informal)

[Can LLMs detect item flaws in the questions they have generated?](https://aclanthology.org/2026.aimecon-sessions.31/) (Gorgun, AIME-Con 2026)

• Can LLMs detect item flaws in the questions they have generated? (Gorgun, AIME-Con 2026)

ACL

• Guher Gorgun. 2026. Can LLMs detect item flaws in the questions they have generated?. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Coordinated Session Papers, pages 284–290, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).