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arXiv · 2608.10154

Multimodal Item Parameter Estimation using Simulated Response Probabilitie

Christopher Ormerod·YoungKoung Kim
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Abstract

We present results from reconstructing multiple-choice model (MCM) and three-parameter logistic (3PL) model curves using a fine-tuned multimodal large language model (LLM) based on Qwen3.5. The model is prompted and fine-tuned to replicate choice probabilities across a large training corpus of multiple-choice items containing both image and text stimuli, conditioned on a labeled set of student ability levels. By learning to reproduce the systematic error patterns of students across a discrete range of abilities, the LLM implicitly captures the underlying response probabilities encoded in the 3PL and MCM curves. This allows us to accurately approximate item difficulty on a held-out test set directly from the model's predicted option probabilities.

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Christopher Ormerod, YoungKoung Kim. 2026-08-10. Multimodal Item Parameter Estimation using Simulated Response Probabilitie. https://arxiv.org/abs/2608.10154

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