Enhancing Online Learning Efficiency Through Heterogeneous Resource Integration with a Multi-Agent RAG System
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916600635981824 |
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| author | Srivastav, Devansh Alam, Hasan Md Tusfiqur Asaei, Afsaneh Fazeli, Mahmoud Sharma, Tanisha Sonntag, Daniel |
| author_facet | Srivastav, Devansh Alam, Hasan Md Tusfiqur Asaei, Afsaneh Fazeli, Mahmoud Sharma, Tanisha Sonntag, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_03948 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Online Learning Efficiency Through Heterogeneous Resource Integration with a Multi-Agent RAG System Srivastav, Devansh Alam, Hasan Md Tusfiqur Asaei, Afsaneh Fazeli, Mahmoud Sharma, Tanisha Sonntag, Daniel Artificial Intelligence Computation and Language Multiagent Systems 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. |
| title | Enhancing Online Learning Efficiency Through Heterogeneous Resource Integration with a Multi-Agent RAG System |
| topic | Artificial Intelligence Computation and Language Multiagent Systems |
| url | https://arxiv.org/abs/2502.03948 |