AI-Enabled XR for Industrial Operations and Applications

Extended Reality (XR), including virtual reality (VR), augmented reality (AR), and mixed reality (MR), is increasingly gaining relevance in industrial environments. Rather than being viewed only as an immersive visualization technology, XR is now emerging as an interface layer that connects workers, engineers, and operators to product data, digital twins, work instructions, IoT telemetry, maintenance knowledge, and enterprise systems. Current evidence suggests that XR creates the strongest practical value in bounded industrial workflows such as training, assembly, inspection, maintenance, logistics, remote collaboration, and design review. At the same time, artificial intelligence (AI) is shifting XR from static visualization toward context-aware support through scene understanding, computer vision, speech interfaces, adaptive guidance, predictive analytics, and intelligent knowledge retrieval. Together, AI and XR are opening new opportunities for more human-centred, resilient, and effective industrial systems.GoalThis Research Topic aims to collect high-quality contributions that explore how AI-enabled XR can support industrial operations and applications in real-world contexts. While immersive technologies are frequently discussed in broad terms, the most urgent and valuable questions in industry are more specific: how XR can improve task execution, strengthen situational awareness, reduce errors, accelerate training and onboarding, support maintenance and troubleshooting, improve inspection and quality assurance, and enable more effective interaction with digital twins and operational data.We are particularly interested in work that goes beyond proof-of-concept demonstrations and addresses measurable value, deployment logic, and human factors in applied industrial settings. The topic welcomes research from manufacturing, logistics, energy, construction, transportation, engineering, and other sectors where XR can support frontline work, operational decision-making, and interaction with complex assets, systems, and data. Particular attention is also given to the role of AI in making XR systems more adaptive, intelligent, and context-aware through capabilities such as visual recognition, multimodal interaction, predictive support, conversational interfaces, and domain-specific knowledge access. In addition, this Research Topic explicitly welcomes contributions examining the real-world workplace implications of AI in the development and use of XR for industrial contexts. We encourage submissions that move beyond assumptions that AI simply replaces people and instead investigate what is actually happening in practice: whether teams are reduced or restructured, whether productivity gains are achieved with the same workforce, how roles and required skills evolve, and how AI changes collaboration among developers, engineers, operators, and decision-makers. Scope and information for authorsThis Research Topic welcomes contributions on industrial uses of XR, including but not limited to: XR for industrial training and onboarding; guided assembly and maintenance; inspection and quality assurance; logistics and warehouse support; remote expert collaboration; immersive engineering and design review; XR interfaces for digital twins; AI-enabled contextual guidance; computer vision and speech interfaces in operational workflows; adaptive XR systems; predictive maintenance support; and human factors in industrial XR, including workload, trust, ergonomics, usability, safety, and cybersickness. Contributions addressing interoperability, enterprise integration, governance, and responsible deployment of AI-enabled XR in industrial settings are also encouraged.We welcome Original Research, Review, Mini Review, Systematic Review, Methods, Perspective, and Case Study manuscripts. Submissions should demonstrate clear relevance to industrial operations or industrially grounded applications. Outside the scope of this Research Topic are manuscripts focused primarily on general classroom education, teacher-learner pedagogy, metaverse environments without clear industrial relevance, or XR interaction technologies that are not explicitly linked to industrial workflows, operational performance, or professional practice. Topic editors: Radu Petruse, Saša Ćuković, Marta Del Rio-Guerra, Mihai Neghina Source: https://www.frontiersin.org/research-topics/81509/ai-enabled-xr-for-industrial-operations-and-applications