Research on a Closed-Loop Multi-Agent Collaborative Decision-Making and Personalized Knowledge Generation System for Vertical Domains

DOI: https://doi.org/10.62517/jbdc.202601323

Author(s)

Yonghui Zheng1, Ruiheng Zhang1, Chao Geng1, Xiaojun Fan1, Hanxiao Chang2, Mingming Gong3,*

Affiliation(s)

1College of Information Science and Engineering, Henan University of Technology, Zhengzhou, Henan, China 2College of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, Henan, China 3iFLYTEK Co., Ltd., Hefei, Anhui, China *Corresponding Author

Abstract

Traditional online learning systems still have several limitations. These limitations include inflexible learning paths, coarse-grained resource recommendations, delayed feedback, and limited intelligent interaction. This study develops a personalized learning system. The system combines multi-agent collaboration with dynamic learner profiles. The system first identifies learner requests through an intent recognition module. The intent recognition result determines which agent should handle the task. Different agents are responsible for different learning activities. These agents include the Question-Answering Agent, Path Planning Agent, Resource Recommendation Agent, Question Bank Generation Agent, Document Parsing Agent, Code Tutoring Agent, and Learning Evaluation Agent. The system updates the learner profile according to learning behaviors, knowledge mastery states, learning goals, and knowledge gaps. The learner profile is combined with the knowledge graph. This combination supports staged path planning and resource recommendation. The system uses error-feedback mechanisms, Feynman learning logs, and event-triggered mechanisms to build a closed learning loop. The loop connects diagnosis, path generation, resource recommendation, and adaptive feedback adjustment. Experiments and case studies show that the system can generate more reasonable learning paths and provide better resource recommendations. The proposed system provides a practical reference for personalized intelligent learning systems.

Keywords

Multi-Agent Systems; Personalized Learning; Learner Profile; Learning Path Planning; Resource Recommendation; Intelligent Education

References

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