Research on the Construction and Application of Intelligent Teaching Scenarios Based on Multi-Agent Collaboration and Knowledge Base - Driven Approaches

Aiming at the problems of fragmented knowledge and data privacy risks in traditional intelligent teaching systems, this research proposes a technical framework of “knowledge base driven + multi-agent collaboration”. Through vector databases and Retrieval-Augmented Generation (RAG) technology, semantic-level retrieval of subject knowledge is achieved. Combined with Federated Learning and blockchain technology, a cross-school collaboration mechanism is constructed. Experiments show that the “AI Learning Companion” system based on this framework can effectively improve the average scores of experimental classes and increase students' active learning time. The innovation lies in: CD constructing a dynamically updated knowledge base system that supports multi-modal resource integration; ® designing a multi-agent collaboration mechanism to achieve distributed processing of teaching tasks; ® proposing a blockchain-based cross-school knowledge base alliance scheme. The research results provide a transferable technical path for the large-scale application of intelligent education systems.