From Adoption to Everyday Use in Industry 5.0: Individual Experiences and Team Dynamics in Human–AI Collaboration across sectors

Technology adoption in Industry 5.0, as well as in non-industrial settings, is rarely a single “accept or reject” moment. It is a dynamic trajectory shaped by repeated interactions with systems that can recommend, predict, monitor, automate, or co-create. Individually, workers may experience shifting autonomy, new cognitive demands, changing error patterns, and tensions between legacy habits and new procedures, alongside concerns about competence, status, and professional identity. In teams, AI can redistribute attention and authority, change coordination and handoffs, and introduce new types of failures (for example, automation surprises, diffusion of responsibility, and muted dissent). These effects vary across people, and factors such as culture can influence trust and reliance norms. Additionally, neurodiversity and accessibility may affect usability and workload, while an ageing workforce can alter learning constraints and confidence levels. Furthermore, perceived fairness, legitimacy, and transparency can impact acceptance, compliance, and participation. Understanding these intertwined individual and team mechanisms is essential for designing tasks, training, opportunities for participation and influence, and adaptive or adaptable workflows and tools that account for differential needs, preferences, and capabilities, while supporting sustainable performance, development, and creativity in technology-rich work systems.

Industry 5.0 promotes human-centric, sustainable, and resilient work and organizational systems by integrating advanced digital technologies (including AI) into everyday work. This Research Topic examines how this integration is actually lived and coordinated, using a dual analytical lens: (1) individual-level human–technology interaction (HTI) and human–AI teaming (HAIT) (sensemaking, perceived control, risk perception, explainability experiences, workload and technostress, learning and self-efficacy, identity and meaning at work); and (2) team-level dynamics (coordination, communication, shared mental models, voice, conflict, leadership, and accountability when AI reshapes information flow and task allocation, collective and relational calibration of trust and reliance, task delegation, roles of the team members). We aim to illuminate mechanisms that explain when technology enables performance, learning, creativity, and innovation, versus when it creates misalignment, exclusion, or degraded cooperation. A core emphasis is on micro/meso processes relevant to performance, development and wellbeing: how people and teams adapt routines, redesign tasks, manage errors, and build competence over time, including during onboarding and continuous improvement. We are particularly interested in how these processes shape workers’ well-being in a holistic sense, including health-related aspects such as mental and cognitive workload, strain, and technostress, as well as hedonic and eudaimonic well-being. We explicitly welcome work that addresses inclusion and differential impacts in technology-rich environments. This includes topics such as professional identity threat, status loss, stereotypes, belonging, neurodiversity, and cross-cultural differences in trust and interpretation. We encourage viewing these issues as interactional and team processes rather than merely as broad policy debates. Our goal is to explore actionable implications for task and technology design, work organization, training, and human–AI collaboration.

We welcome empirical (including longitudinal/diary, field, experimental, mixed-methods, and intervention studies), theoretical, and review papers that address Industry 5.0 work contexts through a multi-level lens on HTI and Human-AI teaming. We encourage designs that capture change over time and link mechanisms to observable behavior and performance indicators. Studies may use process data (logs, interaction traces), multi-level modelling, or comparative designs across cultures, professional roles, career stages and organizational domains. Cross-sectional studies are also welcome when common method biases are well addressed. We are especially interested in submissions that examine adoption, integration, and everyday use through concrete individual- and team-level mechanisms, rather than broad macro-level discussions of digital transformation alone.

Topics include:
o individual experiences with technology, such as adoption, learning, explainability, performance, perceived control, mental and cognitive workload, technostress, and well-being across the transition from adoption to everyday use
o team dynamics in human–AI collaboration, such as coordination, communication, shared mental models, reliance, mutual support, and accountability when AI reshapes information flow, handoffs, and task allocation in ongoing work
o identity, diversity, inclusion and neurodiversity as interactional factors shaping use, trust, participation, and collaboration in technology-rich environments
o cross-cultural differences in trust, interpretation, and day-to-day use
o digital exclusion, perceived fairness and legitimacy, and related behaviors as conditions influencing acceptance, compliance, voice, and participation
o upskilling, reskilling and workforce ageing with attention to onboarding, learning constraints, confidence, and competence-building over time
o and task and workflow design for creativity and innovation, enabled or constrained by AI with clear relevance to human–AI interaction, team adaptation, or the lived coordination of work

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
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Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Industry 5.0, technology integration, trust and reliance, explainability, risk perception, coordination, transparency, accountability, upskilling and reskilling, workforce, aging, human-centered design, wellbeing, performance, multi-level analysis, team roles and team differentiation, human–AI teaming, human–technology interaction

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.