Embracing Levin’s Legacy: Advancing Socio-Technical Learning and Development in Human-Robot Team Design Through STS Approaches

Abstract

Abstract This paper investigates the synergy between Levin's theories on technology transfer as a socio-technical learning and developmental process (TLD process), action research, and sociotechnical systems (STS) theories. Levin's extensive work highlights the significance of technology transfer as a means for organizational development. His TLD process emphasizes the intricate interplay between technology, organizational change, and learning and highlights the importance of incorporating cultural knowledge and skills into the technological transfer process. Contemporary STS views are introduced to complement and extend Levin's theories by providing a systemic lens to understand the broader socio-technical context in which technology transfer occurs. To illustrate the synergies and potential challenges from Levin’s theories of technology transfer with contemporary STS concepts, we use a qualitative study of a unique case about the design and development of human-robot teams (HRTs) for construction tasks. Our findings reveal that while Levin's theories provide a valuable foundation for understanding technology transfer and organizational change, contemporary sociotechnical systems face unique challenges in the context of AI-driven human-robot teams where intelligent robots also contribute to the sociotechnical learning. Moreover, the rapidly evolving nature of technology and innovations could exponentially impact on multidisciplinary design teams, stakeholder participation and inter-organizational dynamics. The discussions suggest an extension of co-generative learning to incorporate of ‘collaborative intelligence’ between human-robot teams enabled by AI. Consequently, Levin's theories of technology transfer might not fully address the complex ethical dilemmas caused by AI-driven HRT systems. Therefore addressing these challenges requires ongoing dialogue and collaboration among researchers, practitioners, and policymakers with different disciplinary backgrounds to develop robust and reliable sociotechnical systems frameworks to navigate the complexities of robotics and AI in today's rapidly evolving technological landscape.

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