Cognitive Workload Management in Industry 5.0: Human-Centric Design and Technological Integration

Industry 5.0 marks a shift from the automation-centric focus of Industry 4.0 toward a human-centric paradigm, where operator well-being, safety, and collaboration with intelligent systems are placed at the core of industrial design. While technologies such as artificial intelligence, collaborative robotics, and augmented and virtual reality (AR/VR) have reduced physical demands on workers, they have simultaneously introduced new sources of cognitive strain. Operators today act less as manual laborers and more as supervisors, decision-makers, and collaborators within complex human-machine systems, facing challenges such as information overload, rapid task switching, and the need to calibrate trust in automated partners. In this evolving context, emerging Artificial General Intelligence (AGI)-oriented approaches may offer new opportunities to better understand and interpret human cognitive and emotional states, behavioral patterns, and their interaction with increasingly intelligent industrial systems.Unmanaged cognitive workload (CWL) is closely linked to increased error rates, safety incidents, fatigue, and diminished long-term well-being. Traditional assessment tools, such as post-task subjective questionnaires, are limited in dynamic, high-stakes industrial settings, as they provide retrospective rather than real-time insight. Addressing this gap requires interdisciplinary collaboration across ergonomics, cognitive psychology, and engineering to develop robust, non-intrusive methods for continuously monitoring and managing cognitive demands on the shop floor. The increasing capability of advanced AI and AGI-oriented systems to integrate multimodal information may further support the development of more comprehensive models of human cognitive, emotional, and behavioral states, enabling more adaptive and human-aware forms of human-machine collaboration.This Research Topic invites contributions that advance the theoretical, methodological, and applied understanding of CWL management, supporting the transition toward safer, more efficient, and human-centric industrial environments aligned with Industry 5.0 principles.We welcome original research, reviews, case studies, and perspectives addressing, but not limited to:- Advanced technology integration: AR, VR, and AI applications for reducing cognitive strain during maintenance and assembly tasks- Real-time CWL monitoring: physiological and behavioral methodologies for continuous cognitive load assessment-Predictive modeling: data-driven approaches for forecasting cognitive demands before they affect performance or safety- Task allocation models: human-machine task distribution frameworks that prioritize operator well-being- CWL assessment frameworks: new or refined tools and metrics for evaluating cognitive workload- Adaptive system design: systems that respond dynamically to an operator’s cognitive state- Artificial General Intelligence and human-state understanding: AGI-oriented and advanced AI approaches for interpreting and integrating cognitive, emotional, physiological, and behavioral indicators of human operators- Human-aware AGI for adaptive collaboration: intelligent systems capable of understanding operator states and behaviors to support context-aware decision assistance, task adaptation, and safer human-machine collaboration- Resilient and sustainable industrial environments: strategies balancing operational efficiency with long-term workforce sustainabilityBy fostering interdisciplinary discussion across ergonomics, human factors, artificial intelligence, and engineering, this Research Topic aims to advance CWL assessment frameworks and support the design of safer, more efficient, adaptive, and human-centric industrial systems." Topic editors: Francesco Facchini, Doris Aschenbrenner, Sotirios Panagou Source: https://www.frontiersin.org/research-topics/84119/cognitive-workload-management-in-industry-50-human-centric-design-and-technological-integration