Integrating AI in Education for Personalized Learning Outcomes

Abstract

Artificial intelligence technology is driving the transformation of personalized education. This study explores the theoretical framework, implementation pathways, and actual effectiveness of intelligent technology supporting personalized learning, analyzes core mechanisms such as learner difference identification and adaptive path generation, elaborates on the operational principles of intelligent diagnosis,
content optimization, and feedback support, and constructs a multi-dimensional effectiveness evaluation system. Research shows that artificial
intelligence significantly improves learning efficiency and quality through precise diagnosis, dynamic adaptation, and intelligent feedback.
However, its effectiveness is constrained by factors such as technical accuracy, instructional design, and learner competency, requiring continuous optimization of the integration between technology and educational philosophy in practice.