Exploring AI-driven Methods for Pre-calibrating Difficulty of Mathematics Items with Visual Content

Leah Walker, Jinnie Choi


Abstract
Use of AI is bringing efficiencies in replacing previously laborious field- testing with scaled item parameter pre- calibration, especially in language assessment. However, gaps in research exist in replicating this accomplishment for mathematics assessment items with visual content. This study explores the methodological options of using AI in pre-calibrating multimodal mathematics items.
Anthology ID:
2026.aimecon-wip.19
Volume:
Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress
Month:
October
Year:
2026
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:
145–151
Language:
URL:
https://aclanthology.org/2026.aimecon-wip.19/
DOI:
Bibkey:
Cite (ACL):
Leah Walker and Jinnie Choi. 2026. Exploring AI-driven Methods for Pre-calibrating Difficulty of Mathematics Items with Visual Content. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 145–151, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):
Exploring AI-driven Methods for Pre-calibrating Difficulty of Mathematics Items with Visual Content (Walker & Choi, AIME-Con 2026)
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PDF:
https://aclanthology.org/2026.aimecon-wip.19.pdf