Enhancing Online Learning Efficiency Through Heterogeneous Resource Integration with a Multi-Agent RAG System
This work addresses the challenge of manual effort in online learning for learners, but it is incremental as it builds on existing RAG and multi-agent methods.
The paper tackled the problem of inefficient access to diverse online learning resources by developing a Multi-Agent RAG System, which automates retrieval and synthesis, resulting in strong usability and moderate-high utility in a preliminary user study.
Efficient online learning requires seamless access to diverse resources such as videos, code repositories, documentation, and general web content. This poster paper introduces early-stage work on a Multi-Agent Retrieval-Augmented Generation (RAG) System designed to enhance learning efficiency by integrating these heterogeneous resources. Using specialized agents tailored for specific resource types (e.g., YouTube tutorials, GitHub repositories, documentation websites, and search engines), the system automates the retrieval and synthesis of relevant information. By streamlining the process of finding and combining knowledge, this approach reduces manual effort and enhances the learning experience. A preliminary user study confirmed the system's strong usability and moderate-high utility, demonstrating its potential to improve the efficiency of knowledge acquisition.