Research on a Vertical-Domain Personalized Learning System Based on RAG and Multi-Agent Collaboration

DOI: https://doi.org/10.62517/jhet.202615423

Author(s)

Chao Geng1, Xiaojun Fan1, Yonghui Zheng1, Ruiheng Zhang1, Yawen Zhao2, 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

Specialized courses frequently encounter fragmented knowledge resources, insufficient adaptation to individual learning needs, and delayed feedback. This work presents a personalized learning system for vertical domains that combines Retrieval-Augmented Generation (RAG) with a multi-agent framework. Learner profiles incorporate prior knowledge, learning goals, weak knowledge points, and preferred difficulty levels, while a domain-specific knowledge base supplies external retrieval content. During resource generation, retrieved knowledge and learner profile data jointly inform the production of reading materials, quizzes, practical tasks, and animation scripts. A review agent evaluates generated resources prior to learner delivery. Learning records—including quiz results, coding performance, task completion, and learning time—update learner profiles and guide subsequent resource and path adjustments. Experimental results show that the proposed system achieves better resource adaptability than direct LLM generation and supports personalized learning in vertical domains.

Keywords

Vertical Domain; RAG; Multi-Agent Collaboration; Personalized Learning; Learner Profile; Intelligent Tutoring

References

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