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        <title>Math Item Difficulty Prediction with Multimodal Input</title>
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        <namePart type="given">Kai</namePart>
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    <name type="personal">
        <namePart type="given">Suhwa</namePart>
        <namePart type="family">Han</namePart>
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    <abstract>Item difficulty prediction relies on the efficient utilization of high-leverage item characteristics. Many items in the domain of mathematics include figures, charts, or other visual stimuli that are challenging to incorporate into difficulty prediction models. Multimodal large language models (LLMs) offer a way to process these visual stimuli in combination with text input, potentially enhancing the success of item difficulty prediction. In this study, we employ several open-source multimodal LLMs to predict the difficulty of math items with visual stimuli from a publicly available data set. We find that multimodal LLMs are capable of predicting math item difficulty.</abstract>
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%0 Conference Proceedings
%T Math Item Difficulty Prediction with Multimodal Input
%A North, Kai
%A Ormerod, Christopher
%A Kwako, Alexander
%A Han, Suhwa
%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 2026
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-9983004-0-0
%F north-etal-2026-math
%X Item difficulty prediction relies on the efficient utilization of high-leverage item characteristics. Many items in the domain of mathematics include figures, charts, or other visual stimuli that are challenging to incorporate into difficulty prediction models. Multimodal large language models (LLMs) offer a way to process these visual stimuli in combination with text input, potentially enhancing the success of item difficulty prediction. In this study, we employ several open-source multimodal LLMs to predict the difficulty of math items with visual stimuli from a publicly available data set. We find that multimodal LLMs are capable of predicting math item difficulty.
%U https://aclanthology.org/2026.aimecon-main.42/
%P 380-386

Markdown (Informal)

[Math Item Difficulty Prediction with Multimodal Input](https://aclanthology.org/2026.aimecon-main.42/) (North et al., AIME-Con 2026)

• Math Item Difficulty Prediction with Multimodal Input (North et al., AIME-Con 2026)

ACL

• Kai North, Christopher Ormerod, Alexander Kwako, and Suhwa Han. 2026. Math Item Difficulty Prediction with Multimodal Input. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 380–386, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).