Enhancing Online Learning Efficiency Through Heterogeneous Resource Integration with a Multi-Agent RAG System

TL;DR

This paper presents an early-stage multi-agent RAG system that integrates diverse online resources to improve learning efficiency by automating retrieval and synthesis, reducing manual effort, and enhancing user experience.

Contribution

It introduces a novel multi-agent RAG framework tailored for heterogeneous online resources, demonstrating its potential to streamline knowledge acquisition in online learning.

Findings

System shows strong usability in user study

Moderate-high utility for knowledge synthesis

Potential to significantly enhance learning efficiency

Abstract

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.