AI RESEARCH
Estimating Item Difficulty Using Large Language Models and Tree-Based Machine Learning Algorithms
arXiv CS.LG
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ArXi:2504.08804v2 Announce Type: replace-cross Estimating item difficulty through field-testing is often resource-intensive and time-consuming. As such, there is strong motivation to develop methods that can predict item difficulty at scale using only the item content. Large Language Models (LLMs) represent a new frontier for this goal. The present research examines the feasibility of using an LLM to predict item difficulty for K-5 mathematics and reading assessment items (N = 5170