Intelligent human-centric manufacturing in industry 5.0: A review of large language model enabled extended reality systems

Abstract

Manufacturing systems are progressively anticipated to evolve from automation-focused efficiency to human-centered intelligence, adaptability, and collaborative decision-making, in accordance with the Industry 5.0 vision. The amalgamation of Large Language Models (LLMs) with Extended Reality (XR) establishes a formidable framework for incorporating cognitive reasoning, contextual awareness, and intuitive interaction directly into industrial settings. Although recent research have shown the efficacy of LLM–XR systems in task execution and operational support, their comprehensive impact throughout the manufacturing lifecycle has not been extensively investigated. This analysis analyzes LLM–XR integration from a lifecycle viewpoint by combining industrial studies primarily published between 2015 and January 2026. It encompasses the entire manufacturing lifecycle, including applications in design, planning, assembly and disassembly, maintenance, training, quality inspection, and human–robot collaboration. These innovations collectively signify a shift from localized operational assistance to shared cognitive platforms that facilitate spatial-semantic reasoning, explicable decision-making, and human-AI collaboration. The research delineates essential technological facilitators that support the efficient implementation of LLM-XR, encompassing multimodal interaction, spatially grounded knowledge enhancement, reasoning-based intelligence, and ergonomics-conscious system design. Furthermore, ongoing issues of system reliability, grounding in domain knowledge, computational limitations, and user trust are rigorously analyzed. This review synthesizes LLM–XR integration across the manufacturing lifecycle and develops a generalized conceptual reference architecture that organizes recurring functional components, interaction mechanisms, information flows, and adaptive feedback processes across design, planning, execution, and quality-related activities.