Design of an Intelligent Tutor System for the Personalization of Learning Activities Using Case-Based Reasoning and Multi-Agent System

Abstract

The impact of Artificial Intelligence (AI) has significantly remodelled the educational environment, with tutoring systems emerging as essential tools for adapting personalized learning tracks. This article explores the significant benefits achieved through the smooth integration of Intelligent Tutoring Systems (ITS) and Multi-Agent Systems (MAS) with Case-Based Reasoning (CBR). Intelligent tutoring systems, which operate as an interactive platform, exploit the strength of educational data mining to construct meticulously personalized learner profiles. In tandem, multi-agent systems facilitate dynamic collaboration between a whole range of agents, including profile agents, recommendation agents, assessment agents and adaptation agents. This collaborative effort aims to orchestrate personalized learning activities that are finely adjusted to respond to the specific needs of each learner. The introduction of case-based reasoning elevates the sophistication of personalized learning by exploiting the depth of prior knowledge and experience. By systematically exploring a specific knowledge base of similar cases, the system provides recommendations and proven solutions. This ensures a learning experience that not only works with each learner's unique profile but also guarantees relevance and effectiveness. This article embarks on a comprehensive exploration of personalized learning activities by integrating ITS, MAS and CBR transparently. The main objective is to optimize learning engagement and effectiveness by proactively adapting educational content to the individual needs of each learner. This exploration is part of the continued focus on improving the educational experience through the advancement of AI and educational technologies.