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    <abstract>The significant of Computerized Adaptive Testing (CAT) is self-evident in contemporary Intelligent Tutoring Systems (ITSs) which aims to recommend suitable questions for students based on their knowledge state. In recent years, Graph Neural Networks (GNNs) and Reinforcement Learning (RL) methods have been increasingly applied to CAT. While these approaches have achieved empirical success, they still face limitations, such as inadequate handling of concept relevance when multiple concepts are involved and incomplete evaluation metrics. To address these issues, we propose a Knowledge Graph Reasoning-Based Model for CAT (KGCAT), which leverages the reasoning power of knowledge graphs (KGs) to capture the semantic and relational information between concepts and questions while focusing on reducing the noise caused by concepts with low relevance by utilizing mutual information. Additionally, a multi-objective reinforcement learning framework is employed to incorporate multiple evaluation objectives, further refining question selection and improving the overall effectiveness of CAT. Empirical evaluations conducted on three authentic educational datasets demonstrate that the proposed model outperforms existing methods in both accuracy and interpretability.</abstract>
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%0 Conference Proceedings
%T A Knowledge Graph Reasoning-Based Model for Computerized Adaptive Testing
%A Qiu, Xinyi
%A Chen, Zhiyun
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F qiu-chen-2025-knowledge
%X The significant of Computerized Adaptive Testing (CAT) is self-evident in contemporary Intelligent Tutoring Systems (ITSs) which aims to recommend suitable questions for students based on their knowledge state. In recent years, Graph Neural Networks (GNNs) and Reinforcement Learning (RL) methods have been increasingly applied to CAT. While these approaches have achieved empirical success, they still face limitations, such as inadequate handling of concept relevance when multiple concepts are involved and incomplete evaluation metrics. To address these issues, we propose a Knowledge Graph Reasoning-Based Model for CAT (KGCAT), which leverages the reasoning power of knowledge graphs (KGs) to capture the semantic and relational information between concepts and questions while focusing on reducing the noise caused by concepts with low relevance by utilizing mutual information. Additionally, a multi-objective reinforcement learning framework is employed to incorporate multiple evaluation objectives, further refining question selection and improving the overall effectiveness of CAT. Empirical evaluations conducted on three authentic educational datasets demonstrate that the proposed model outperforms existing methods in both accuracy and interpretability.
%U https://aclanthology.org/2025.coling-main.354/
%P 5295-5304

Markdown (Informal)

[A Knowledge Graph Reasoning-Based Model for Computerized Adaptive Testing](https://aclanthology.org/2025.coling-main.354/) (Qiu & Chen, COLING 2025)

• A Knowledge Graph Reasoning-Based Model for Computerized Adaptive Testing (Qiu & Chen, COLING 2025)

ACL

• Xinyi Qiu and Zhiyun Chen. 2025. A Knowledge Graph Reasoning-Based Model for Computerized Adaptive Testing. In Proceedings of the 31st International Conference on Computational Linguistics, pages 5295–5304, Abu Dhabi, UAE. Association for Computational Linguistics.