## 3 Qualitative Analysis of the Application of AR in Industrial Assistance and Training  
To analyse the most frequent applications of AR in industrial care and training, a qualitative analysis of the 60 articles was conducted using NVivo 14, following the guidelines of [PERSON] and [PERSON] [105]. This analysis involved the coding and classification of the articles into specific application areas, employing a tri-level coding method based on [PERSON]'s grounded theory [103]. A graphical representation of the results of this analysis is presented in Figure 2.  
Figure 1: A flow chart of the phases of the analysis process. A total of 60 articles were analysed, of which 29 were identified as focusing specifically on industrial assistance, representing 53.85% of the total number of studies reviewed (Figure 3). This research was subdivided into two subcategories: (a) industrial operations AR guidance and (b) safety training. The first category, which constitutes 40.08% of the reviewed studies, focuses on the manipulation of industrial machinery and equipment. In this context, the use of virtual assistance through AR interfaces is employed to enhance efficiency in industrial tasks [42]. This category of research explores the potential of process visualisation and real-time information to assist in the coordination of complex tasks, thereby ensuring efficient execution. In this context, coordinated task management is of paramount importance, as it optimises integration and work dynamics in industrial scenarios. The second category, representing 13.77% of the studies analysed, focuses on the use of visual guides for the proper operation of equipment. This category is specifically dedicated to providing assistance in the guidance of essential aspects such as risk detection and incident prevention. By integrating AR techniques, the aim is to deepen the perception and recognition of potential hazards in the work environment. The utilisation of visual guidance facilitates the secure usage of equipment, thereby fostering a more robust and efficacious prevention culture [47]. The 24 studies, which constitute 46.15% of the total and focus on training, were classified into four main subcategories: (a) AR immersive learning, (b) AR for manufacturing process analysis, (c) AR-assisted design tools, and (d) AR-based teaching aids. Immersive learning through AR, which accounts for 9.61% of training-related studies, promotes interactive learning and skills development through simulations [40]. By simulating a real environment in a virtual context, a risk-free learning experience is provided. This allows operators to experience, and practice in, complex scenarios without the dangers inherent in a real industrial environment. The application of AR in manufacturing process analysis, which constitutes 17.31% of the studies, improves the visualisation and understanding of complex configura  
Figure 2: Network of reference nodes for AR applications in industrial assistance and training. tions in production. This encompasses real-time control, fault diagnosis, operation simulation, workflow optimisation, predictive maintenance, quality control analysis, resource allocation strategy and energy efficiency analysis. All of these facilitate a dynamic interaction, which contributes to optimisation and quality control in production processes [44]. * AR-aided design tools, which comprise 3.84% of the studies, enhance the 3D visualisation of prototypes and models, fostering an immersive and interactive design approach. This innovation optimises efficiency and accuracy in the design process, providing an accurate representation of industrial projects [71]. * AR-based learning aids in industry, which account for 15.39% of studies, provide a tangible learning experience. These solutions, which include personalised assistance, on-the-job training, virtual training and AR tutorials, facilitate the understanding of complex processes and enhance concept retention, thereby aligning with the demands of the industrial sector [69].