Best Practices on Applying RAG Based Chatbots in Learning Management System(LMS) for Software Developers

Conventional learning management systems (LMS) do not support knowledge-based needs for a framework-level knowledge base in software development educational environments. Future-oriented LMS platforms should be able to support such knowledge-based needs of learners. For this, we propose integrating a retrieval augmented generation system (RAG) based on the official NestJS documentation into the LMS. By utilizing Ollama and Dify, we enabled the rapid and easy setup and operation of the RAG system. Our system, based on the official NestJS documentation, provides responses for development-related questions to learners and junior developers. Through this, the RAG system can assist in solving knowledge-based problems that junior developers encounter while working on projects. Ultimately, our RAG system in the field of software development education enables providing personalized education to developers via AI tutoring.

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Publication Details

Journal
Korean Institute of Smart Media
Published
2025-07-30
DOI
https://doi.org/10.30693/smj.2025.14.7.37
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00
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article

Best Practices on Applying RAG Based Chatbots in Learning Management System(LMS) for Software Developers

Chaeyun Seo, Young‐Chul Kim, Jae-Ho Kim
Korean Institute of Smart Media
AI in Service Interactions
article

Best Practices on Applying RAG Based Chatbots in Learning Management System(LMS) for Software Developers

Chaeyun Seo, Young‐Chul Kim, Jae-Ho Kim
article en

Abstract

Conventional learning management systems (LMS) do not support knowledge-based needs for a framework-level knowledge base in software development educational environments. Future-oriented LMS platforms should be able to support such knowledge-based needs of learners. For this, we propose integrating a retrieval augmented generation system (RAG) based on the official NestJS documentation into the LMS. By utilizing Ollama and Dify, we enabled the rapid and easy setup and operation of the RAG system. Our system, based on the official NestJS documentation, provides responses for development-related questions to learners and junior developers. Through this, the RAG system can assist in solving knowledge-based problems that junior developers encounter while working on projects. Ultimately, our RAG system in the field of software development education enables providing personalized education to developers via AI tutoring.

Korean Institute of Smart MediaVol. 14(7)
Openalex Percentile: Top 90%
AI in Service Interactions
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