Industry 5.0 and Education 5.0: Transforming Vocational Educationthrough Intelligent Technology
Zhang Hongli, Leong Wai Yie
Abstract
As new directions of development, Industry 5.0 and Education 5.0 emphasize human-machine collaboration, personalized services, and the application of intelligent technologies. This paper aims to explore how intelligent technology can transform vocational education to enhance the effectiveness of vocational training and meet the demands of the modern economy. By analyzing the research gaps in personalized learning paths, emotion-driven learning, crossdisciplinary integration, and long-term learning behavior analysis, the paper proposes four improved algorithms: the adaptive learning path generation algorithm, the emotion-driven personalized learning algorithm, the cross-disciplinary knowledge graph algorithm, and the long-term learning behavior prediction algorithm. The research demonstrates that these innovative approaches effectively address the limitations of current personalized learning systems, providing theoretical and practical support for the intelligent transformation of vocational education.