How to Build an AI Tutor that Can Adapt to Any Course and Provide Accurate Answers Using Large Language Model and Retrieval-Augmented Generation

The advent of artificial intelligence (AI) has instigated a transformational wave across various sectors, with education standing as a salient beneficiary. AI's unrivaled capacity to enable personalized and adaptive learning experiences has propelled intelligent tutoring systems to the forefront of modern educational paradigms (Kasneci et al., 2023). These systems, powered by AI, offer individualized feedback and interactive learning modules designed to cater to each student's distinct learning needs. Nonetheless, the challenge of developing AI tutors capable of delivering consistently accurate and dependable responses across diverse academic disciplines persists. A notable hindrance to the reliability of AI in educational applications is the occurrence of'information hallucination', a phenomenon where AI-generated responses, while appearing valid, deviate from factual accuracy (Nye et al., 2023). Such inconsistencies can undermine confidence in AI-centric educational systems (Kasneci et al., 2023). Furthermore, the customization of these systems to align with specific course content necessitates access to current and pertinent educational materials, a task often complicated by the multifaceted nature of academic disciplines. To tackle these challenges, this paper introduces AI Tutor, a web application developed upon the sophisticated infrastructure of large language models (LLMs) and retrieval-augmented generation (RAG). AI Tutor is engineered to deliver accurate, contextually relevant responses by intelligently assimilating information from course-specific materials (Lewis et al., 2020).