AI-Based Classification of TIMSS Items for Framework Alignment

Ummugul Bezirhan, Matthias von Davier


Abstract
Large-scale assessments rely on expert panels to verify that test items align with prescribed frameworks, a labor-intensive process. This study evaluates the use of GPT-4o to classify TIMSS items to content domain, cognitive domain, and difficulty categories. Findings highlight the potential of language models to support scalable, framework-aligned item verification.
Anthology ID:
2025.aimecon-main.15
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:
134–141
Language:
URL:
https://aclanthology.org/2025.aimecon-main.15/
DOI:
Bibkey:
Cite (ACL):
Ummugul Bezirhan and Matthias von Davier. 2025. AI-Based Classification of TIMSS Items for Framework Alignment. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 134–141, Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):
AI-Based Classification of TIMSS Items for Framework Alignment (Bezirhan & von Davier, AIME-Con 2025)
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PDF:
https://aclanthology.org/2025.aimecon-main.15.pdf