EduRAG: Improving AI Teaching Assistants with Retrieval-Augmented Generation

Publication Date

1-1-2026

Document Type

Conference Proceeding

Publication Title

Communications in Computer and Information Science

Volume

2939 CCIS

DOI

10.1007/978-3-032-22202-2_22

First Page

278

Last Page

300

Abstract

This paper addresses the emerging need for AI-driven teaching assistants to deliver personalized, effective and responsive educational assistance. Although large language models (LLMs) like GPT-3, GPT-4 generate human-responsive text, these models suffer from domain accuracy. One potential solution was identified as the Retrieval-Augmented Generation (RAG). These problems can be addressed by fusing the generative capability of LLMs with modern retrievals that allow it to refer to well-curated knowledge bases in that domain, thus making the responses more accurate and appropriate. This study develops RAG as an AI teaching assistant. This reduces the gap between general knowledge and subject-specific expertise, thus providing information that is not only relevant to the context but also customized to the unique requirements of students.

Keywords

AI teaching assistants, domain-specific learning, large language models (LLM), personalized education, retrieval-augmented generation (RAG)

Department

Computer Science

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