Authors - Mehzabul Hoque Nahid, Fatema Tuz Zahra, Mubashshir Bin Mahbub, Saleh Ahmed Jalal Siam Abstract - Personalizing learning in higher education presents a significant challenge due to the difficulty of providing individual feedback to large student cohorts. This study proposes an intelligent tutoring system based on a multi-agent architecture utilizing Large Language Models (LLMs) to address scalability and adaptability issues. The proposed architecture integrates two complementary subsystems: a reactive module that answers student queries using Retrieval-Augmented Generation (RAG) to ensure accuracy based on course materials, and a proactive module that autonomously analyzes student profiles to generate personalized study plans without direct instructor intervention. The system was implemented using Lang- Graph for agent orchestration and MongoDB for state persistence. Experimental validation was conducted using a curated golden dataset from a university course. Results demonstrate a retrieval precision of 94.2% and a faithfulness score of 87.8%, significantly mitigating hallucinations common in monolithic models. Furthermore, the operational cost analysis indicates high financial viability for mass implementation. This dual approach offers a robust solution for automated, highquality educational support, effectively bridging the gap between standardized teaching and personalized learning needs.
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