Estimating Item Difficulty with Large Language Models as Experts
Accurate estimates of item difficulty are essential for valid assessment and effective adaptive learning. However, for newly created tasks, response data are typically unavailable. Pretesting and expert judgement can be costly and slow, while machine learning methods often require large labelled training datasets. Recent work suggests that large language models (LLMs) may help. However, there is limited evidence on the elicitation procedures and prompt configurations used to emulate experts for difficulty estimation. This study addresses this gap by evaluating three off-the-shelf LLMs as difficulty raters for newly created items without access to response data. Using an item bank from an ...
| Authors | Diana Kolesnikova, Kirill Fedyanin, Abe D. Hofman et al. |
|---|---|
| Primary category | stat.ME |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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