A User Model to Enable Hybrid Human-AI Collaboration

Miguel Angelo Machado Guimarães (), Davide Rua Carneiro (), José Miguel Pinto de Sousa (), Romão Filipe Dias Santos (), Maria Goreti Carvalho Marreiros () and António Manuel Lucas Soares ()
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Miguel Angelo Machado Guimarães: INESC TEC
Davide Rua Carneiro: INESC TEC
José Miguel Pinto de Sousa: INESC TEC
Romão Filipe Dias Santos: INESC TEC
Maria Goreti Carvalho Marreiros: Instituto Superior de Engenharia do Porto
António Manuel Lucas Soares: INESC TEC

A chapter in Technology Management for Intelligent, Open and Responsible Organizations and Ecosystems, 2026, pp 314-321 from Springer

Abstract: Abstract The shift to Industry 5.0 is based on human-centric, resilient, and sustainable industrial systems, where AI is no longer a simple tool nor a replacement for human faculties, but rather a collaborator. Thus, we move away from a model of full automation in which the human is left out of the loop, to a human-centric vision in which the human is assisted, augmented, and catered to by AI-driven agents that proactively adapt to human needs. To achieve this vision, it is fundamental that AI agents understand human needs, intentions, and workflows in real-time, enabling more intuitive, adaptive and meaningful interactions. This paper introduces the socio-technical context stream, a framework that provides AI with a real-time, human-centered semantic understanding of industrial contexts. By continuously integrating human intent, workflows, and situational factors, AI agents can dynamically adapt their interactions, fostering deeper, more intuitive and natural Human-AI collaboration. This paper focuses on the definition of a context model, and how it can be populated to then feed AI-driven agents with meaningful contextual information, with a particular focus on modeling human actors and envisioning the interaction with Digital Twins.

Keywords: Industry 5.0; Socio-Technical Systems; Digital Twin; Large Language Model; Human-AI Collaboration (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-23282-3_38

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