  This paper proposes a low-code solution to build an AI tutor that leverages
advanced AI techniques to provide accurate and contextually relevant responses
in a personalized learning environment. The OpenAI Assistants API allows AI
Tutor to easily embed, store, retrieve, and manage files and chat history,
enabling a low-code solution. Large Language Models (LLMs) and
Retrieval-Augmented Generation (RAG) technology generate sophisticated answers
based on course-specific materials. The application efficiently organizes and
retrieves relevant information through vector embedding and similarity-based
retrieval algorithms. The AI Tutor prototype demonstrates its ability to
generate relevant, accurate answers with source citations. It represents a
significant advancement in technology-enhanced tutoring systems, democratizing
access to high-quality, customized educational support in higher education.
