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    <abstract>While the Question Generation (QG) task has been increasingly adopted in educational assessments, its evaluation remains limited by approaches that lack a clear connection to the educational values of test items. In this work, we introduce test item analysis, a method frequently used by educators to assess test question quality, into QG evaluation. Specifically, we construct pairs of candidate questions that differ in quality across dimensions such as topic coverage, item difficulty, item discrimination, and distractor efficiency. We then examine whether existing QG evaluation approaches can effectively distinguish these differences. Our findings reveal significant shortcomings in these approaches with respect to accurately assessing test item quality in relation to student performance. To address this gap, we propose a novel QG evaluation framework, QG-SMS, which leverages Large Language Model for Student Modeling and Simulation to perform test item analysis. As demonstrated in our extensive experiments and human evaluation study, the additional perspectives introduced by the simulated student profiles lead to a more effective and robust assessment of test items.</abstract>
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%0 Conference Proceedings
%T QG-SMS: Enhancing Test Item Analysis via Student Modeling and Simulation
%A Nguyen, Bang
%A Du, Tingting
%A Yu, Mengxia
%A Angrave, Lawrence
%A Jiang, Meng
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F nguyen-etal-2025-qg
%X While the Question Generation (QG) task has been increasingly adopted in educational assessments, its evaluation remains limited by approaches that lack a clear connection to the educational values of test items. In this work, we introduce test item analysis, a method frequently used by educators to assess test question quality, into QG evaluation. Specifically, we construct pairs of candidate questions that differ in quality across dimensions such as topic coverage, item difficulty, item discrimination, and distractor efficiency. We then examine whether existing QG evaluation approaches can effectively distinguish these differences. Our findings reveal significant shortcomings in these approaches with respect to accurately assessing test item quality in relation to student performance. To address this gap, we propose a novel QG evaluation framework, QG-SMS, which leverages Large Language Model for Student Modeling and Simulation to perform test item analysis. As demonstrated in our extensive experiments and human evaluation study, the additional perspectives introduced by the simulated student profiles lead to a more effective and robust assessment of test items.
%R 10.18653/v1/2025.acl-long.1268
%U https://aclanthology.org/2025.acl-long.1268/
%U https://doi.org/10.18653/v1/2025.acl-long.1268
%P 26152-26168

Markdown (Informal)

[QG-SMS: Enhancing Test Item Analysis via Student Modeling and Simulation](https://aclanthology.org/2025.acl-long.1268/) (Nguyen et al., ACL 2025)

• QG-SMS: Enhancing Test Item Analysis via Student Modeling and Simulation (Nguyen et al., ACL 2025)

ACL

• Bang Nguyen, Tingting Du, Mengxia Yu, Lawrence Angrave, and Meng Jiang. 2025. QG-SMS: Enhancing Test Item Analysis via Student Modeling and Simulation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 26152–26168, Vienna, Austria. Association for Computational Linguistics.