A Fast-Adaptive Cognitive Diagnosis Framework for Computerized Adaptive Testing Systems

Liu, Yuanhao; You, Yiya; Liu, Shuo; Qian, Hong; Qian, Ying; Zhou, Aimin

doi:10.24963/ijcai.2025/648

A Fast-Adaptive Cognitive Diagnosis Framework for Computerized Adaptive Testing Systems

Yuanhao Liu, Yiya You, Shuo Liu, Hong Qian, Ying Qian, Aimin Zhou

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 5824-5832.

https://doi.org/10.24963/ijcai.2025/648

Keywords

Machine Learning: ML: Representation learning
Machine Learning: ML: Applications
Data Mining: DM: Applications
Multidisciplinary Topics and Applications: MTA: Education

Abstract

Computerized Adaptive Testing (CAT) measures student ability by iteratively selecting informative questions, with core components being the Cognitive Diagnosis Model (CDM) and selection strategy. Current research focuses on optimizing the selection strategy, assuming relatively accurate CDM results. However, existing static CDMs struggle with rapid and accurate diagnosis in the early stage of CAT. To this end, this paper proposes a Fast Adaptive Cognitive Diagnosis (FACD) framework, which incorporates dynamic collaborative and personalized diagnosis modules. Specifically, the collaborative module in FACD uses a dynamic response graph to quickly build student cognitive profiles, while the personalized module leverages each student's response sequence for robust and individualized diagnosis. Extensive experiments on real-world datasets show that, compared with existing static CDMs, FACD not only achieves superior prediction performance across various selection strategies with an improvement between roughly 5%-10% in the early stage of CAT, but also maintains a commendable inference speed.

Yuanhao Liu, Yiya You, Shuo Liu, Hong Qian, Ying Qian, and Aimin Zhou. 2025. A Fast-Adaptive Cognitive Diagnosis Framework for Computerized Adaptive Testing Systems. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 5824–5832. https://doi.org/10.24963/ijcai.2025/648

@inproceedings{ijcai2025p648,
  title     = {A Fast-Adaptive Cognitive Diagnosis Framework for Computerized Adaptive Testing Systems},
  author    = {Liu, Yuanhao and You, Yiya and Liu, Shuo and Qian, Hong and Qian, Ying and Zhou, Aimin},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {5824--5832},
  year      = {2025},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2025/648},
  url       = {https://doi.org/10.24963/ijcai.2025/648},
}

TY  - CPAPER
TI  - A Fast-Adaptive Cognitive Diagnosis Framework for Computerized Adaptive Testing Systems
AU  - Liu, Yuanhao
AU  - You, Yiya
AU  - Liu, Shuo
AU  - Qian, Hong
AU  - Qian, Ying
AU  - Zhou, Aimin
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 5824
EP  - 5832
DO  - 10.24963/ijcai.2025/648
UR  - https://doi.org/10.24963/ijcai.2025/648
ER  -