## 3 Qualitative Analysis of the Application of AR in Industrial Assistance and Training
### AR-Based Training Aids  
In the context of training, whether in an academic or industrial setting, AR-based tools have gained ground in recent years. These tools facilitate realistic simulations, provide practical scenarios and offer instant feedback, which leads to a more agile and deeper learning process.  
In terms of _personalised support_, AR facilitates the adaptation and personalisation of content in different digital formats, adjusting to the specific needs of operators. This enhances comprehension and minimises cognitive load. A clear example is the possibility to establish remote connections through AR, which integrate digital prompts and data directly into the operator's real environment, helping to perform technical support tasks in a more dynamic and interactive way [45].  
AR _scenario-based training_ provides an immersive and realistic learning experience. The integration of 3D models and multimedia elements affords users the opportunity to immerse themselves and learn in virtual environments that replicate real-world situations [40]. This methodology not only enhances the understanding of concepts but also allows for the training of specific technical skills, such as assembly and maintenance. Furthermore, it promotes the development of critical thinking and more informed decision-making [56].  
Improvement driven by _feedback-driven_ improvement through the integration of AR enables systems to provide immediate feedback to the user, allowing operators to have a greater amount of data from the machinery, equipment or products generated [54]. For example, in assembly operations, AR can provide visual guidance and instant corrections, leading to constant improvement in task execution, contributing to the optimisation of throughput and quality [57].  
In terms of _task-oriented training_, AR has proven to be an effective tool for instructing complex operations. For example, [PERSON] and [PERSON] [76] introduced a framework that facilitates the choice of AR applications, using the Spherical Fuzzy COPRAS multi-criteria decision method. This methodology favours a more accurate selection of AR tools that match with specific training demands, ensuring that users benefit from relevant and efficient training. Furthermore, [PERSON] et al. [53] proposed a system that provides more focused task management training through simulations that emulate real-life situations.  
_Simulated-based practice_ in AR has shown great potential for training in manufacturing, assembly and maintenance tasks. This is evidenced by the work of [PERSON] et al. [40], who designed an AR training platform that allows users to interact with virtual elements in a three-dimensional space, providing an opportunity to practise and home skills in a safe and controlled environment. The incorporation of AR into simulation-based training represents a significant advance in technical education, providing more immersive and effective learning, innovatively merging theory with practical application.  
Finally, _virtual caching_ and _AR tutorials_ have recently emerged as innovative training tools. Within this framework, [PERSON] and [PERSON] [63] highlight how AR has enabled the delivery of remote manufacturing engineering education during the COVID-19 pandemic without compromising the quality and effectiveness of learning. Furthermore, AR has been demonstrated to be an invaluable tool in training operators with no or limited prior training to perform specialised tasks, as evidenced by the work of [PERSON] et al. [65]. This versatility of AR, which adapts to varied scenarios and meets multiple needs, not only evidences its adaptability but also underlines its transformative potential in 21 st-century training and industry.