Personalized AI Tutor Using Language Models and Learning Analytics

Abstract

This research proposes a comprehensive AI tutoring framework that leverages the capabilities of large language models (LLMs) and learning analytics to create a personalized and adaptive learning environment. Unlike traditional Intelligent Tutoring Systems (ITS), which are constrained by static rules and predefined flows, the proposed framework offers dynamic content generation, contextual responsiveness, and real-time performance monitoring. It integrates generative AI technologies with microservice-based architecture to enhance the learning experience through quizzes, feedback, and lesson adaptation. The system is built using modern web technologies such as FastAPI for the backend and React for frontend interaction. A four-week experimental evaluation with undergraduate students indicated significant improvements in engagement, knowledge retention, and learner satisfaction. This study demonstrates that generative AI, when combined with analytics and ethical safeguards, can scale personalized learning to diverse educational contexts.