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

作　　者：Liangkeyi SUN 

机构地区：[1]University of Macao

出　　处：《Artificial Intelligence Education Studies》2025年第1期41-52,共12页人工智能教育研究(英文)

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.

关 键 词：Artificial Intelligence in Education Knowledge Graphs Causal Inference Personalized Learning Adap-tive Learning Systems 

分 类 号：O15[理学—数学]