Publication Date
Spring 2025
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Robert Chun
Second Advisor
Thomas Austin
Third Advisor
Samish Chandra Kolli
Keywords
AI teaching assistants, large language models (LLM), retrieval-augmented generation (RAG), domain-specific learning, personalized education
Abstract
This paper is based on the emerging need for AI-driven teaching assistants to deliver personalized, effective, and responsive educational assistance. Earlier proposals for educational technology such as rule-based systems and adaptive learning platforms have been limited by the lack of flexibility, domain accuracy, and slow, non-interactive support. With the launch of large language models (LLMs) like GPT-3, GPT-4, they have demonstrated that they can generate the kind of human-responsive text. When it comes to more substantive education matters, though, such models suffer from domain accuracy, in addition to whether accurate information can be imparted. And that is where the aspect of staying accurate comes into play. One such 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. The present study develops RAG further as an AI teaching assistant to enhance the value of education for the individual. Its objective is to remain abreast of the more current retrieval methodologies together with Large Language Models (LLMs) that attempt to reduce the divide between general knowledge and subject-specific expertise. This, therefore, empowers AI assistants by providing information that is not only relevant to the context but also customized to the unique requirements of students. The paper discloses how RAG brings benefits to a solution that conventional AI teaching assistants would have otherwise restricted. It further discusses about the development of dependable, precise, scalable, AI-driven solutions for educational settings.
Recommended Citation
Vadlamudi, Geethika, "EduRAG: Improving AI Teaching Assistants with Retrieval-Augmented Generation" (2025). Master's Projects. 1527.
DOI: https://doi.org/10.31979/etd.92ne-ym3n
https://scholarworks.sjsu.edu/etd_projects/1527
