@inproceedings{han-etal-2025-leveraging-fine,
title = "Leveraging Fine-tuned Large Language Models in Item Parameter Prediction",
author = "Han, Suhwa and
Rijmen, Frank and
Boykin, Allison Ames and
Lottridge, Susan",
editor = "Wilson, Joshua and
Ormerod, Christopher and
Beiting Parrish, Magdalen",
booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers",
month = oct,
year = "2025",
address = "Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States",
publisher = "National Council on Measurement in Education (NCME)",
url = "https://aclanthology.org/2025.aimecon-main.27/",
pages = "250--264",
ISBN = "979-8-218-84228-4",
abstract = "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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%0 Conference Proceedings
%T Leveraging Fine-tuned Large Language Models in Item Parameter Prediction
%A Han, Suhwa
%A Rijmen, Frank
%A Boykin, Allison Ames
%A Lottridge, Susan
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
%D 2025
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-218-84228-4
%F han-etal-2025-leveraging-fine
%X 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.
%U https://aclanthology.org/2025.aimecon-main.27/
%P 250-264
Markdown (Informal)
[Leveraging Fine-tuned Large Language Models in Item Parameter Prediction](https://aclanthology.org/2025.aimecon-main.27/) (Han et al., AIME-Con 2025)
ACL
- Suhwa Han, Frank Rijmen, Allison Ames Boykin, and Susan Lottridge. 2025. Leveraging Fine-tuned Large Language Models in Item Parameter Prediction. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 250–264, Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).