AgentCAT: Simulating Computerized Adaptive Testing via Multi-Agent Large Language Models
Computerized Adaptive Testing (CAT), as a key technology for personalized education, aims to accurately assess examinee proficiency by retrieving exercises dynamically matching current ability estimates. However, existing CAT research is constrained by limitations of static offline data and isolated component optimization. Restricted by partial labels in offline logs, researchers degrade the dynamic assessment process into static sequence prediction. Current research focuses on isolated perspectives, e.g., selection or diagnosis, neglecting the overall CAT interaction process. To address this, we propose AgentCAT, a Large Language Model-based multi-agent simulation system, to construct a ...
| Authors | Weiyuan Zhou, Haiping Ma, Xiaoshan Yu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 20 June 2026 |
| Last updated at source | 20 June 2026 |
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