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.

Keywords:

• Collaborative learning
• human-centric approach
• human-robot collaboration
• industrial workplaces
• Industry 5
• systematic mapping

Acknowledgements

The authors would like to thank FAPERGS (Foundation for the Supporting of Research in the State of Rio Grande do Sul), CNPq/Brazil (National Council for Scientific and Technological Development), and CAPES/Brazil (Coordination for the Improvement of Higher Education Personnel) – Finance Code 001. We would also like to thank the University of Vale do Rio dos Sinos (UNISINOS) for supporting the development of the present study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Robson Kerschner de Lima

Robson Kerschner de Lima is a Ph.D. Candidate in Applied Computing at the University of Vale do Rio dos Sinos (UNISINOS). He is a researcher at Unisinos University and has been working for more than 15 years in software development and management contribution.

Wesllei Felipe Heckler

Wesllei Felipe Heckler is a Ph.D. student in the Applied Computing Graduate Program (PPGCA) at the University of Vale do Rio dos Sinos (UNISINOS). He has a masters degree in Applied Computing and an undergraduate in Computer Science. His main research interests are Machine Learning and technology applied to health.

Rosemary Francisco

Rosemary Francisco is Ph.D. in Business Administration with a doctoral internship at the University of Helsinki, Finland. She is a professor and a researcher at Unisinos University and has been working for more than 20 years in software engineering subjects.

Jorge Luis Victória Barbosa

Jorge Luis Victória Barbosa received M.Sc. and Ph.D. in computer science from the Federal University of Rio Grande do Sul, Brazil. He conducted post-doctoral studies at Sungkyunkwan University (South Korea) and University of California Irvine (USA). Jorge is a full professor at University of Vale do Rio dos Sinos (UNISINOS).