Leveraging Fine-tuned Large Language Models in Item Parameter Prediction
Suhwa Han, Frank Rijmen, Allison Ames Boykin, Susan Lottridge
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.- Anthology ID:
- 2025.aimecon-main.27
- Volume:
- Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
- Month:
- October
- Year:
- 2025
- Address:
- Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States
- Editors:
- Joshua Wilson, Christopher Ormerod, Magdalen Beiting Parrish
- Venue:
- AIME-Con
- SIG:
- Publisher:
- National Council on Measurement in Education (NCME)
- Note:
- Pages:
- 250–264
- Language:
- URL:
- https://preview.aclanthology.org/ingest-emnlp/2025.aimecon-main.27/
- DOI:
- Cite (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).
- Cite (Informal):
- Leveraging Fine-tuned Large Language Models in Item Parameter Prediction (Han et al., AIME-Con 2025)
- PDF:
- https://preview.aclanthology.org/ingest-emnlp/2025.aimecon-main.27.pdf