Integrating Collaborative Learning and Advanced Technology in Industry 5.0: A Systematic Mapping Study and Taxonomy

Abstract

Learning evolves through human interactions and adapting to technological advancements. The fourth industrial revolution elevated the importance of machines in improving competitiveness, efficiency, and product quality. However, this emphasis on technology overshadowed the human-centric perspective. In contrast, Industry 5 aims to foster collaboration between humans, robots, and digital systems. Our study utilized systematic mapping to explore collaboration in I5. Specifically, we investigated how collaborative learning occurs in industrial workplaces. The main objective was to identify prevalent collaborative learning techniques, technologies, challenges, and data types in I5 environments. The results reveal various collaborative learning approaches, highlighting their potential to improve productivity, innovation, and worker participation in I5. The study uncovers significant trends, such as the increasing reliance on digital platforms and Artificial Intelligence (AI)-driven tools, which facilitate collaborative learning while posing unique challenges. Our taxonomy provides a structured framework for understanding these dynamics, serving as a valuable guide for practitioners and researchers. The findings underscore the necessity for adaptive learning strategies and the integration of advanced technologies to promote effective collaboration in industrial settings. This research contributes to the theoretical understanding of collaborative learning in I5 and offers practical insights for its implementation, thereby supporting the evolution of industry practices in this new technological era.