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    <abstract>Studies that validate LLM-predicted item difficulty conventionally benchmark predictions against a single-population estimate. Using a multimodal LLM’s pairwise judgments on a 29-item mathematics exam, mixture Rasch modeling shows the LLM tracks difficulty ordering differentially across classes, meaningful subgroup variation that the aggregate benchmark conceals entirely.</abstract>
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%0 Conference Proceedings
%T When One Benchmark Hides Many Truths: Mixture-IRT and LLM Difficulty Prediction
%A Dogan, Enis
%A Ogut, Burhan
%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 dogan-ogut-2026-one
%X Studies that validate LLM-predicted item difficulty conventionally benchmark predictions against a single-population estimate. Using a multimodal LLM’s pairwise judgments on a 29-item mathematics exam, mixture Rasch modeling shows the LLM tracks difficulty ordering differentially across classes, meaningful subgroup variation that the aggregate benchmark conceals entirely.
%U https://aclanthology.org/2026.aimecon-wip.36/
%P 282-288

Markdown (Informal)

[When One Benchmark Hides Many Truths: Mixture-IRT and LLM Difficulty Prediction](https://aclanthology.org/2026.aimecon-wip.36/) (Dogan & Ogut, AIME-Con 2026)

• When One Benchmark Hides Many Truths: Mixture-IRT and LLM Difficulty Prediction (Dogan & Ogut, AIME-Con 2026)

ACL

• Enis Dogan and Burhan Ogut. 2026. When One Benchmark Hides Many Truths: Mixture-IRT and LLM Difficulty Prediction. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 282–288, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).