Integrating Knowledge Graphs and Causal Inference for AI-Driven Personalized Learning in Education

Abstract

Artificial Intelligence (AI) has revolutionized education by enabling personalized learning experiences through adaptive platforms. However, traditional AI-driven systems primarily rely on correlation-based analytics, limiting their ability to uncover the causal mechanisms behind learning outcomes. This study explores the integration of Knowledge Graphs (KGs) and Causal Inference (CI) as a novel approach to enhance AI-driven educational systems. KGs provide a structured representation of educational knowledge, facilitating intelligent content recommendations and adaptive learning pathways, while CI enables AI systems to move beyond pattern recognition to identify cause-and-effect relationships in student learning. By combining these methods, this research aims to optimize personalized learning path recommendations, improve educational decision-making, and ensure AI-driven interventions are both data-informed and causally validated. Case studies from real-world applications, including intelligent tutoring systems and MOOC platforms, illustrate the practical impact of this approach. The findings contribute to advancing AI-driven education by fostering a balance between knowledge modeling, adaptability, and empirical rigor.