AI Meets Higher Education: Applying Artificial Intelligence to Personalized Learning Platforms

Abstract

With the continuous development of higher education, the diversity of student backgrounds and needs is increasing, making traditional teaching models increasingly unable to meet the needs of all students. The rapid development of artificial intelligence technology has provided new solutions for personalized learning. This study aims to explore the application of artificial intelligence in personalized learning platforms in higher education. Through theoretical analysis, technical implementation, and empirical research, the effectiveness of the platform in improving learning experience and educational quality is verified. The study first analyzes the challenges faced by higher education and the potential of artificial intelligence, then proposes an application framework for a personalized learning platform based on artificial intelligence, including intelligent recommendation systems, adaptive learning paths, intelligent tutoring and feedback, and learning analysis and prediction. Subsequently, the design and implementation of the platform are introduced in detail, including platform architecture design, core functional modules, and technical implementation. Through empirical research, the significant role of the platform in improving learning outcomes, user satisfaction, and teaching effectiveness is verified. Finally, the research findings, limitations, and future research directions are discussed, and practical suggestions and future prospects are put forward. This study provides a feasible personalized learning solution for higher education institutions, which is conducive to improving educational quality and student satisfaction, and promoting educational equity and resource optimization.