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

However, navigating and synthesizing information across these disparate sources can be a timeintensive Efficient online learning requires seamless access to diverse resources and inefficient process, creating barriers to efficient online such as videos, code repositories, documentation, and general learning [8]. The challenges associated with multi-source learning web content. This poster paper introduces early-stage work are especially evident in technical domains, where the need to on a Multi-Agent Retrieval-Augmented Generation (RAG) System quickly find accurate and relevant information is critical. For instance, designed to enhance learning efficiency by integrating these heterogeneous a developer exploring a new framework might consult a resources. Using specialized agents tailored for specific YouTube tutorial for an overview, reference a GitHub repository resource types (e.g., YouTube tutorials, GitHub repositories, documentation for implementation details, examine the official documentation for websites, and search engines), the system automates deeper insights, and conduct general web searches for troubleshooting.