Multimodal Item Parameter Estimation using Simulated Response Probabilitie

Researchers at an unnamed institution used a fine-tuned multimodal large language model based on Qwen3.5 to reconstruct multiple-choice model (MCM) and three-parameter logistic (3PL) item response curves, accurately approximating item difficulty on a held-out test set by learning to replicate student choice probabilities across ability levels.

arXiv:2608.10154v1 Announce Type: new 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.