Application of Generative Artificial Intelligence Technology in Customized Learning Path Design: A New Strategy for Higher Education
Ying Li, Wei Ji, Jiaqi Liu, Wenqing Li
Abstract
In today's rapidly evolving education field, generative artificial intelligence (AI) technology is increasingly becoming a key force in promoting teaching innovation and personalized learning solutions. This article deeply explores the application of generative AI technology in designing customized learning paths in higher education and its profound impact on educational practice. Through specific case analysis, this study reveals how generative AI can effectively analyze students' learning behaviors, preferences, and performance to automatically generate and adjust learning paths in real time, thereby providing students with a highly personalized learning experience. Research results show that using generative AI technology can significantly improve learning efficiency, increase student engagement and satisfaction, and optimize learning outcomes. In addition, this article also demonstrates the ability of generative AI to promote innovation in learning content and teaching methods, improve the efficiency of educational resource utilization, and enhance the adaptability of learning paths. Through these findings, this paper provides valuable insights for higher education institutions, pointing out the importance and feasibility of leveraging generative AI technologies to design personalized learning paths, while highlighting key considerations that should be noted during implementation.