QG-SMS: Enhancing Test Item Analysis via Student Modeling and Simulation
Published in Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL), 2025
This paper presents a novel student modeling approach for question generation, enabling enhanced test item analysis through realistic student response simulation. The work was conducted in collaboration with researchers at the University of Notre Dame.
Key Contributions:
- Developed a student modeling framework for question generation
- Enhanced test item analysis through student simulation
- Published at ACL 2025, a top-tier NLP conference
Research Area: Natural Language Processing, Educational Technology, Student Modeling
Recommended citation: B. Nguyen, T. Du, M. Yu, L. Angrave, and M. Jiang. (2025). "QG-SMS: Enhancing Test Item Analysis via Student Modeling and Simulation." Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL).