摘要
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,lim-iting their ability to uncover the causal mechanisms behind learning outcomes.This study explores the in-tegration 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.
