Frank Rijmen

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2025

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Leveraging Fine-tuned Large Language Models in Item Parameter Prediction
Suhwa Han | Frank Rijmen | Allison Ames Boykin | Susan Lottridge
Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers

The study introduces novel approaches for fine-tuning pre-trained LLMs to predict item response theory parameters directly from item texts and structured item attribute variables. The proposed methods were evaluated on a dataset over 1,000 English Language Art items that are currently in the operational pool for a large scale assessment.
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