Design of an Intelligent Agent-Based Adaptive Learning System: A Literature-Based Model Proposal

Abstract

The integration of artificial intelligence technologies into educational environments has accelerated the development of personalized and adaptive learning approaches. In this context, intelligent agent-based systems stand out with their potential to adapt learning processes according to students' individual characteristics, learning needs, and performance. The aim of this study is to examine the use of intelligent agents in education and to propose an integrated adaptive learning system model based on literature findings. The research was conducted with a literature-based qualitative approach, and studies on the use of intelligent agents in education in recent years were examined in terms of student modeling, content recommendation, performance evaluation and decision-making processes. As a result of the investigations, it was determined that intelligent agents are widely used in these components, but studies addressing these components in a holistic structure are limited. Accordingly, this study proposes a multi-agent adaptive learning model that integrates student modeling, personalized content recommendation, continuous assessment, and decision-making processes. Additionally, the model is enriched with a generative AI-powered agent component that provides real-time interaction, feedback, and dynamic personalization. The developed model will be evaluated through expert review, and the findings will be reported in future empirical studies. In conclusion, the proposed model is considered to have the potential to make learning processes more flexible, data-driven, and student-centered. The study is expected to contribute to the literature by providing an integrated conceptual framework for intelligent agent-based adaptive learning systems.