An Interactive Question-Answering System Using Large Language Model and Retrieval-Augmented Generation in an Intelligent Tutoring System on the Programming Domain

The insufficient communication between mentors and students has been one of the main disadvantages of modern programming learning platforms. In this paper, we propose the development of a web-based intelligent tutoring system with a question-answering (QA) system to provide live interaction between students and a mentor figure. We propose the implementation of an alternative QA system using a large language model (LLM) and a retrieval-augmented generation (RAG) mechanism. We utilized the LangChain library and integrated the RAG mechanism with the history-aware retriever and direct integration into the web application. We performed internal and external evaluations in the form of qualitative evaluation via subjective scoring towards answers from various quantized LLMs in both single-turn and multi-turn conversation scenarios. We conclude that the Llama 3 model displays consistent and promising results compared to other models and that documents with a higher character count may act as better knowledge bases for the RAG process.