REINFORCED-LLM TUTOR (RLT): A MULTI-AGENT FRAMEWORK FOR DYNAMICALLY PERSONALIZED LEARNING

Abstract: Traditional Intelligent Tutoring Systems (ITS) fail to scale due to static, pre-programmed pedagogy. This paper introduces the Reinforced-LLM Tutor (RLT), a novel architecture overcoming these limits. RLT synergistically integrates Large Language Models (LLMs), Reinforcement Learning (RL), and multi-agent systems. The framework features four modules: a Retrieval-Augmented Generation (RAG) Domain Knowledge Module to ensure factual accuracy, a Dynamic Student Model tracking cognitive/affective states, a Multi-Agent Pedagogical Core (Expert, Socratic, Motivational agents), and an Adaptive Policy Engine. This engine uses RL, modeled as an MDP, to learn an optimal teaching policy, creating a truly adaptive, self-improving tutor.

Keywords: Intelligent Tutoring Systems, Large Language Models, Reinforcement Learning, Personalized Learning, Multi-Agent Systems, Educational Technology, Gamification.