Impact of Augmented Reality on Assistance and Training in Industry 4.0: Qualitative Evaluation and Meta-Analysis

Abstract

In the context of Industry 4.0, industrial environments are at a crossroads, facing the challenge of greater flexibility and significant technical skills gaps. In this situs, Augmented Reality (AR) emerges as a transformative tool, enhancing the synergy between technical staff and emerging technologies. This article focuses on exploring the integration of AR in Industry 4.0, with a particular emphasis on its role in improving technical assistance and training. The research addresses the ways in which AR not only facilitates more efficient processes but also acts as an essential bridge for training and skills development in constantly changing technological environments. It investigates the significant impact of AR on both optimising work processes and training workers to meet the emerging challenges of Industry 4.0. Through a qualitative analysis, the studies are categorised according to their application domains, grouping them into specific thematic areas. Subsequently, a meta-analysis is conducted to determine the actual impact of AR in the sector. The findings reveal a positive and significant correlation between the implementation of AR and its effectiveness in assistance and training in the framework of Industry 4.0. Finally, the article delves into an analysis of current limitations and challenges, providing insights into possible developments and trends in the use of AR for assistance and training in Industry 4.0.

1. Introduction

The emergence of new challenges in today’s industrial environments, characterised by an increasing demand for customised products, high quality standards and reduced product life cycles, has led to a significant tension between market demands and labour market needs [1,2,3,4]. This situation is further compounded by the disruption of learning curves, which is particularly evident in maintenance, assembly and machine repair, which are critical aspects of industrial processes [5,6,7,8,9]. Furthermore, the effective management of process complexity is complicated by the diversity of the workforce, which includes factors such as ageing and heterogeneous workers [10,11,12,13].

In light of the aforementioned context, industrial systems must possess the capacity for reconfiguration and flexibility in order to adapt swiftly to market changes [14,15,16]. In this context, highly experienced operators are of great importance, as they possess the necessary skills to perform tasks such as programming, maintenance and diagnostics [17,18,19,20]. These workers, due to their cognitive abilities and flexibility, are of great importance for the ability to adapt to changing situations and requirements [21,22]. However, they face a high cognitive load due to the changing nature of their roles and work environments [23,24,25]. Therefore, the optimisation of information processes is essential to mitigate these burdens.

Augmented Reality (AR) represents an innovative solution within Industry 4.0 strategies, facilitating the interaction between personnel and advanced technologies. This integration of manual and automated processes offers a potential solution to the challenges of Industry 4.0 [26,27]. AR-based cognitive assistance systems offer a significant potential to enhance the productivity and agility of industrial systems [28,29,30]. These devices not only facilitate efficient information representation but also assist operators in perceiving, receiving and processing information [31,32]. By considering the individual roles, qualifications and personal characteristics of employees, it is possible to adapt information specifically to the user and the environment, thus achieving an optimal distribution of information in production plants and increasing the efficiency of industrial processes [33,34].

However, empirical research in real industrial settings is limited, and existing studies sometimes show ambiguous results [35,36,37,38]. Similarly, individual studies in the scientific literature often describe the positive effects of AR. However, there is a paucity of empirical evidence regarding the actual impact on employees’ cognitive load levels or their performance in industrial settings.

Therefore, a qualitative analysis is required to address the inconsistencies and to assess the order to which AR applications in assistance and training within Industry 4.0 facilitate workers to overcome the technical barriers of the new industrial model, improving their interactivity and user understanding. This is so as to achieve with this technological tool an improvement in operational efficiency.

In addition to the aforementioned qualitative analysis, a meta-analysis is conducted to synthesise the findings of multiple studies in order to obtain a more comprehensive and statistically robust empirical picture of the true impact of AR in Industry 4.0 in relation to the aforementioned issues.

In both studies, we commenced with a systematic review and bibliometric analysis of 60 articles [39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98] published between January 2012 and February 2024 [99]. The use of VOSviewer [100,101], a tool that specialises in the construction and visualisation of bibliometric networks, enabled the authors to gain insights into the current landscape of AR applications in training and industrial assistance. Based on the findings derived from the research co-occurrence analysis, and in accordance with Turabian’s guidelines [102], the research questions that the present research aims to answer were formulated.

• RQ1: What are the most prevalent applications of AR technology in industrial assistance and training?
• RQ2: To what extent does the implementation of AR contribute to improving the effectiveness of assistance and training processes in the industrial sector?

The following structure is employed in the paper: Section 2 describes the research methodology employed. Section 3 presents a detailed classification of the primary studies analysed by domains of application of AR in industrial care and training, specifying their respective applications. Section 4 specifically focuses on a meta-analysis of the impact of AR in industrial care and training. Section 5 discusses the results obtained in the study. Finally, Section 6 presents the general conclusions and proposes directions for future research. This study is part of a larger project, whose ultimate goal is to develop an AR architecture aimed at both the assistance and training of workers in the context of Industry 4.0. The objective is to ensure their effective integration, with the aim of improving both efficiency and safety in the workplace.

2. Materials and Methodology

This study forms part of a larger project, whose ultimate goal is to devise two distinct architectures for the implementation of AR within the context of Industry 4.0. The first architecture is intended for use in industrial assistance scenarios, while the second is designed for worker training. In both cases, the objective is to develop architectures that guarantee industrial safety, while utilising this technology. Figure 1 illustrates the project phases preceding the proposed AR architectures.

In the initial phase of this process, the evaluation examines the manner in which corporate entities and research centres utilise AR in the context of industrial assistance and training. In accordance with this classification, we employed the NVivo 14 software to conduct a qualitative analysis and corresponding coding, guided by the methodology established by Saldaña [103]. In this manner, the 60 studies were classified into three categories: 29 focused on the care domain, 24 on the training domain, and 7 addressed both areas. Within the assistance domain, 29 studies were categorised into specific areas, such as the use of AR to guide industrial operations and safety-focused assistance. Conversely, in the context of training, the 24 studies were grouped into areas such as immersive learning, manufacturing process analysis, design tools and AR-based learning resources for industrial applications.

In a second phase, a data table was constructed by meticulously examining each of the 60 articles. For data extraction, the content analysis technique proposed by Gurevitch et al. [104] was used. This data collection format permitted the recording of essential information from each primary study, including the following: the literature citation, the industrial sector addressed, the training and/or assistance applications, the hardware and software employed and the data collection and evaluation tools used, as well as the benefits and challenges of AR in industrial contexts. For this purpose, Review Manager 5.4.1 software was employed to generate the risk of bias analyses and corresponding forest plots. Once the dataset had been reviewed and cleaned, articles were selected for meta-analysis according to the inclusion criteria.

Finally, in a third phase, the results of both analyses were used to examine and discuss the current challenges associated with the integration of AR in training and industrial assistance contexts.

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 Bazeley and Jackson [105]. This analysis involved the coding and classification of the articles into specific application areas, employing a tri-level coding method based on Saldaña’s grounded theory [103]. A graphical representation of the results of this analysis is presented in Figure 2.

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 configurations 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].

In the 7 studies that integrate the themes of assistance and training, both categories are applied together, indicating a growing trend in research and application within the sector, in which AR acts as a unifying link between various industrial tasks. By combining these categories, a synergy is created that improves industrial process support and raises the quality of technical education. The joint implementation of these areas underlines the value of AR in providing integrated and comprehensive solutions in different industrial domains. The following subsections will examine the findings of the 60 articles in greater detail, categorising them according to their application of AR. Each subsection will highlight the key contributions of these studies, emphasising their relevance and application in the industrial context.

3.1. AR Virtual Operation Guide

AR virtual assistants provide immersive experiences that enhance operators’ understanding of complex operations through AR simulations, which create safe and hazard-free environments. This assistance mode has been extensively researched and adapted across various industrial contexts. For instance, Zubizarreta et al. [49] proposed an AR virtual assistance guide, while Mourtzis et al. [59] developed support systems for real-time remote maintenance using AR. Malta et al. [67] introduced an AR maintenance assistant utilising YOLOv5, while Fiorentino et al. [42] employed interactive projections for maintenance tasks. Na’amnh et al. [70] developed an AR application to simplify the mechanical bar-bending process, and Liu et al. [81] proposed an AR-based intelligent predictive maintenance approach for industrial machinery integrated with IoT.

AR interfaces have become invaluable tools for enhancing performance in activities such as assembly and process visualisation. Their intuitive and interactive guidance capabilities simplify complex task execution, thereby improving overall efficiency. AR interfaces have been employed in various contexts, including the creation and management of BIM projects [43], providing task assistance based on deep learning and 3D spatial mapping with RGB-D data [55]. Additionally, the use of AR combined with infrared thermography has proven effective for industrial maintenance [72]. Furthermore, a projected AR assistance system for manufacturing with multimodal interaction has been proposed [82].

With regard to the efficacy of AR systems in assembly performance, studies by Runji and Lin [58] demonstrate the potential of AR-based assistance in assembly operations. Furthermore, the integration of simulation and AR methodologies has been explored as a means of enhancing manual assembly assistance [51]. More recently, research has highlighted the benefits of AR in improving assembly performance. For instance, AR-assisted object mapping has been shown to facilitate worker assistance and training, significantly improving efficiency and reducing error rates compared to other guidance methods [85]. Other research has addressed real-time occlusion processing for AR assembly assistance systems using monocular imaging [83], the creation of AR-assisted hands-on experiences to enhance assembly line performance [54] and AR systems that facilitate dynamic gesture recognition and prediction [64]. Additionally, deep learning-based techniques have been integrated for recording and managing instructions in AR-assisted assembly [79].

The utilisation of AR for process visualisation facilitates a more lucid and efficacious comprehension of workflows and operations. Lodetti et al. [80] conducted a qualitative study on the use of mobile remote assistance with AR for the visualisation of industrial processes. Zubizarreta et al. [49] put forth a framework to facilitate the implementation of this visualization in the industrial sector. In a similar vein, Lampen et al. [51] combined simulation techniques with AR visualisation to enhance operator assistance in industrial manufacturing tasks.

Real-time feedback represents a foundational application of AR in industry, facilitating immediate communication and correction during manufacturing, fabrication and assembly processes. In a study by Buń et al. [73], the potential of AR devices for remote support in manufacturing was investigated. In their study, Park et al. [97] employed AR in the context of task support, utilising advanced techniques such as 3D spatial mapping and RGB-D data, which resulted in fast and accurate real-time responses. Dong et al. [64] developed an AR system for manufacturing assistance, which offered dynamic feedback and gesture prediction. Li et al. [79] integrated register management into AR-assisted assembly instructions, providing real-time feedback.

Finally, the role of AR in task coordination emerges as a technological solution that offers enhanced capabilities to synchronise, direct and align complex processes within the industrial environment. Serván et al. [39] developed an assembly instruction communication system using AR, while Moghaddam et al. [68] explored AR for worker assistance and training, emphasising its potential in improving task coordination and efficiency. Furthermore, Angelopoulos and Mourtzis [75] presented an AR assistance system for the adaptive maintenance of on-demand manufactured machinery.

3.2. Safety Training

A multitude of potential risk factors exist in industrial environments, and in response to this and the recognised potential of AR, enhanced safety management systems are being implemented to protect workers.

In the field of visual guides, virtual training systems have been developed that have improved current ways of teaching safety protocols related to manufacturing tasks [46,50]. The adoption of AR panoramic viewers has enhanced the user interaction experience in safety training [58].

With regard to equipment handling, there is a growing development of augmented tools designed to enhance the safety awareness and hazard identification capabilities of workers in industry [46,50]. Li et al. [74] have developed an AR system designed to facilitate workers’ immediate identification of hazards associated with machinery and equipment directly in the workplace.

In hazard identification, several researchers [47,58] have developed realistic and immersive environments, in which they have conducted controlled experiments that show an improvement in risk identification and management. These studies demonstrate that the utilisation of AR for safety training can enhance the risk identification and management abilities of industrial workers and professionals.

In the context of accident prevention, while a great deal of research has been conducted on the development of systems and functions using AR, there is a notable lack of studies that critically analyse the impact and effectiveness of these tools in real-world contexts [62]. Among the limited body of research, a comprehensive and rigorous evaluation of an AR-assisted safety training system has been conducted, utilising advanced technologies such as fuzzy logic [76]. Furthermore, in order to expand the possibilities and applications of AR, several experts have investigated its combination with other emerging technologies, developing a safety assistance system using panoramic AR technology [74]. Finally, a recent study indicates that the utilisation of AR enhances the retention of knowledge, a crucial factor in ensuring long-term safety in industrial settings [86].

3.3. AR Immersive Learning

The industrial field demands a high level of visuospatial skills, which have been limited due to the limited visualisation capabilities of traditional 2D designs. The incorporation of AR is presented as an answer to enrich and improve the training experience in multiple aspects.

In the field of interactive learning, AR has fostered the creation of virtual and interactive learning systems. For instance, Lim and Lee [41] presented an AR-based immersive e-learning system utilising a dynamic tracking technique. Tsai et al. [52] developed an interactive AR-based tool for assembly line training that facilitates the planning of projects and the monitoring of equipment and processes by operators. These AR-based solutions are designed to guide workers in comprehending complex industrial processes by enabling the visualisation and transmission of interactive information related to physical behaviour.

In skill development, Young et al. [53] designed an AR-based teaching and training system for industrial applications, which enhanced students’ comprehension and abilities in overseeing and directing operational designs on assembly lines. Furthermore, innovative teaching tools have been implemented, such as the content-authoring case study by van Lopik et al. [56], which concentrated on developing operators’ practical abilities through AR in small Industry 4.0 companies.

Simulated-based training through AR has been employed to address limitations such as overcrowded classrooms or a lack of adequate training equipment. Webel et al. [40] developed an AR training platform for assembly and maintenance skills, providing immersive lab-based training systems to guide students through industrial maintenance tasks. Meanwhile, Moghaddam et al. [68] investigated the potential of AR for training workers by recreating industrial environments and superimposing images as a teaching tool, thus bringing hands-on experience into the classroom. Following this line of enquiry, the study by Samala et al. [95] provided a mobile AR application that simulates the CNC machining process, facilitating interactive learning and skill development in a safe and controlled environment. This immersive training modality enhances students’ hands-on experience, making the learning process more immersive and effective.

In the area of risk-free learning, AR has made significant contributions. Wang et al. [44] conducted a detailed analysis of AR assembly line research, highlighting the inherent complexity and risks and emphasising the importance of immersive, safety-oriented learning. Training in real environments frequently fails to adequately address safety issues, which is why the virtuality of AR has been employed to establish a safer approach in the industrial sector. The study indicates that AR can enhance specific aspects of safety training, with positive feedback from participants. The immersive learning environment generated by AR not only captures students’ interest in safety issues but also increases their enthusiasm for learning.

3.4. AR for Manufacturing Process Analysis

The incorporation of AR in industrial manufacturing represents a significant advancement in the field, offering innovative solutions to a range of challenges, including real-time monitoring, failure mode analysis, manufacturing simulation, workflow optimisation, predictive maintenance, quality control analysis, resource allocation strategies and energy efficiency.

Through real-time monitoring, AR enables a more comprehensive visualisation of the underlying structural information, integrating real and virtual elements. In their study, Scaravetti and François [69] explored the potential of AR in mechanical engineering training, aiming to provide a more detailed visualisation of mechanical processes and their associated variables. Concurrently, Li et al. [74] developed a method integrating deep learning and AR to monitor in real time the actions of robots and their interaction with operators.

In the field of failure mode analysis, Ortega et al. [72] have made significant advances with the creation of MANTRA, a system that fuses AR with infrared thermography to identify failures in industrial maintenance activities. This combination facilitates a more detailed visualisation and richer interaction with structural elements, integrating simulations, animations and sounds. This innovative approach not only enables the inspection of machinery and equipment from different perspectives and with different layers of virtual information, but also promotes greater efficiency in the industrial sector.

In manufacturing simulation, AR facilitates the understanding and analysis of manufacturing processes. Lai et al. [60] have made a significant contribution to this field with the development of an AR-based simulation system for mechanical assembly, with a particular focus on worker-centred smart manufacturing. Wang et al. [44] conducted a detailed analysis of augmented simulations in assembly lines, highlighting the various applications and advances in this field.

In the context of workflow optimisation, AR has proven to be a valuable tool in assembly lines. In this context, studies by Drouot et al. [77] and Raj et al. [98] represent an important breakthrough, experimentally exploring the effect of AR in improving industrial production. Both investigations focus on how AR can improve the efficiency of assembly processes and reduce the mental workload of operators. The ability of AR to automatically overlay 3D models enables users to manipulate these models, including rotating, tilting and zooming them. This facilitates insight and decreases the time taken to complete tasks, with fewer failures than traditional methods. Consequently, more efficient and accurate planning and workflows are achieved.

In the field of predictive maintenance, AR can be used to enrich the visualisation of data and intrinsic details, facilitating a broader understanding of the abstract concepts and algorithms that govern manufacturing processes. Kim et al. [61] implemented facility segmentation to achieve a more accurate representation of aspects such as stress, strain and strength limits in various structural joints. In a similar vein, Dong et al. [64] designed an AR-assisted system for dynamic gesture recognition and prediction in the context of industrial assembly, which enhances the early identification of potential defects. Both investigations demonstrate the advantages of AR in the field of predictive maintenance, as it enables the adaptation, updating and expansion of models without the need for high costs or compromises to safety.

In terms of quality control analysis, AR has emerged as a valuable tool, allowing for more accurate and detailed assessment. A study by De Feudis et al. [78] explored the application of AR in quality assessment, making use of hand tool tracking through machine vision techniques. AR, with its capacity to provide real-time visualisations, is distinguished by its adaptability and flexibility, enabling the constant optimisation of quality protocols and the agile adaptation of quality standards to the changing dynamics of the industrial sector. Furthermore, AR’s intrinsic capacity to integrate virtual information into real-world contexts provides a unique perspective for the identification and correction of deficiencies in production processes.

Resource allocation strategies in industry are a crucial aspect that determines the efficiency and effectiveness of production processes. A concrete example of this integration can be found in the work of Pilati et al. [57], where AR was used for real-time motion capture. This enabled a detailed understanding of equipment and operators, which in turn enabled a more accurate allocation of tasks and resources. As a result, the right people were assigned to specific functions, optimising the efficiency of manufacturing processes. The real-time visualisation of production processes through AR enables the detailed analysis of resource requirements and consumption at each stage, facilitating more accurate planning and optimal resource allocation. This approach minimises waste and maximises utilisation, as it allows for the identification of areas where resources can be more efficiently allocated.

Finally, energy efficiency analysis AR offers a unique platform to visualise, monitor and analyse energy consumption in real time, resulting in better energy management and control. The study by Park et al. [55] implemented AR to analyse energy efficiency by leveraging 3D spatial mapping and RGB-D data. This approach enabled a detailed visual representation of energy flows and consumption patterns in an industrial context. The ability to visualise this data in a three-dimensional format provided a clear view of how and where energy was consumed, identifying areas for potential improvement. The application of AR in energy efficiency analysis is not limited to visualisation; the integration of deep learning-based algorithms and data analytics techniques into AR allows for a more accurate and contextual interpretation of energy metrics. This can lead to the identification of patterns and trends that might otherwise go unnoticed, which in turn can lead to more effective optimisation strategies. In addition, the ability to overlay real-time data and analytics on a visual representation of the industrial environment can help engineers, technicians and other professionals better understand how their actions and decisions impact energy consumption. This holistic view can foster a greater responsibility and awareness in energy management, driving changes in practices and behaviours towards more sustainable and efficient energy use.

3.5. AR-Aided Design Tools

In the field of engineering, two-dimensional (2D) illustrations have long been the primary communication tool. However, 2D representations often prove inadequate when depicting designs that are visually intensive. In contrast, AR offers new possibilities, enhancing both interactive and immersive design by offering more complete and dynamic representations.

In the field of interactive design, AR has transformed the user experience by enabling users to navigate three-dimensional spaces through dynamic representations in various formats. For example, Richard et al. [71] introduced INTERVALES, a virtual and augmented interactive framework for the design of industrial environments and scenarios. Holm et al. [45] explored the potential of AR to assist novice operators in the design of virtual content within industrial contexts. This innovation enables users to design, evaluate and visualise aspects such as internal structures, temperature variations and electronic components. It has been demonstrated that operators utilising these AR tools demonstrate enhanced design abilities, outperforming those who rely on traditional paper-based methods.

In the field of immersive design, AR has established itself as a transformative tool in industrial design, providing users with deeply immersive virtual experiences. Izquierdo-Domenech et al. [84] advanced this field by achieving intense situational immersion and multimodal interaction in industrial applications, all enhanced by artificial intelligence. Similarly, Fuertes et al. [93] presented a methodology for the creation of demonstration models that utilise AR to facilitate the comprehension of advanced technologies. This approach provides an interactive, hands-on learning environment that accurately reflects the industrial setting, thus facilitating the acquisition of knowledge and skills. Furthermore, Lim and Lee [41] developed an immersive AR-based learning system for electronic design utilising dynamic marker techniques. These developments demonstrate that the ability to enter a three-dimensional space and interact with virtual components through AR significantly enhances understanding, innovation and efficiency in design, far surpassing the limitations of traditional two-dimensional methods.

3.6. 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, Omerali and Kaya [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, Young 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 Webel 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 hone 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 coaching and AR tutorials have recently emerged as innovative training tools. Within this framework, Alahakoon and Kulatunga [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 Chalhoub 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 21st-century training and industry.

4. Meta-Analysis of the Impact of AR on Industrial Assistance and Training

The application of AR in the industrial sector, particularly in the areas of care and training, has increasingly captured the attention of the academic community. However, responses to the second research question on the impact of AR on the effectiveness of industrial assistance and training (RQ2) have been diverse and, in some cases, contradictory.

To address this question, a meta-analysis was conducted, which synthesised data from original research related to the question posed [106,107,108]. The purpose of this approach was to combine findings from primary studies to analyse the divergences between them and to quantitatively synthesise those results with similar characteristics.

Of the 60 studies initially considered, 14 met the inclusion criteria and were subjected to meta-analysis using Review Manager 5.4.1 software [109,110,111]. These criteria were based on five key aspects:

• The studies were to focus on the application of AR in industrial training or assistance contexts.
• It was essential that the studies had a methodological design that included experimental and control groups or, alternatively, included pre- and post-tests.
• Studies had to provide an adequate amount of descriptive data, such as the mean (M) and standard deviation (SD), as well as results of significance analyses, reflected in p values or other data relevant to a quantitative assessment.
• The research had to focus on individuals linked to the industrial sector, whether they were working professionals or students in industrial training.
• The findings of the study were to have been published between January 2012 and February 2024, thus ensuring the relevance and timeliness of the information.

4.1. Risk of Bias Assessment of Included Studies

The meta-analysis technique employed in this study allows for the synthesis and review of primary research. Its effectiveness depends on an accurate and detailed analysis of the included studies. In our research, both authors examined the risk of bias of the selected studies using the Cochrane assessment tool [112,113]. This assessment encompasses critical aspects such as random sequence generation, allocation concealment, the blinding of participants and personnel, the blinding of the outcome assessment, incomplete outcome data, selective reporting and other biases.

The graphical representation of the risk of bias of the selected studies, illustrated in Figure 4 and Figure 5, indicates that the majority of the research assessed is of low risk and identified as randomised controlled trials. However, we found one study with insufficient information and one study with ambiguous results. Notwithstanding the rigour of many studies, it is notable that the methods of allocation concealment are often not detailed. Given the specific characteristics of training and assistance in the industrial sector, blinding is not common in experimental research in this area, as it is not usually exposed to information bias during the design, data collection or analysis phases. In terms of data integrity and the possible selective omission of results, the vast majority of studies show a low risk. Nevertheless, one study was identified in which the same sample was used for the experimental and control groups, which could potentially influence the study’s conclusions. However, the large study sample mitigates any negative effect on the overall results, maintaining a low risk of bias assessment.

4.2. Heterogeneity Assessment of Included Studies

The initial assumption underlying the meta-analysis was that all the included studies should be homogeneous in their results. However, upon analysis, a significant heterogeneity was detected among them. This variability was due to factors such as the population studied, the methodological design and the metrics used, which in some cases led to an inadequate clustering of data and thus to conclusions that may lack robustness. Given this heterogeneity, the Q and I² were employed to assess consistency across the studies. The levels of heterogeneity were defined using I² thresholds of 25%, 50% and 75%, representing a low, medium and high heterogeneity, respectively. The Q-test was based on the total variance. If the p value was greater than 0.1, the studies were considered homogeneous, and a fixed-effect model was used. Conversely, if the p value was less than 0.1, heterogeneity was evident, and a random effects model was chosen for further analysis [114].

A heterogeneity assessment was performed on the data extracted from the 14 selected studies. The results demonstrated a Q of 90.84, an I² of 86% and a p < 0.00001 (Figure 6), indicating a pronounced heterogeneity among the studies. This supported the decision to adopt a random effects model for the analysis. According to the standardised criteria for interpreting effect sizes, a small impact is considered to be 0.2, a moderate impact 0.5 and a large impact 0.8 [115]. The overall effect size obtained from the studies was SMD = 0.79, with a 95% confidence interval between 0.60 and 0.98. This highlights the significant benefit of AR in industrial training and assistance. Although this benefit is modest, it is not considered to be of paramount importance, suggesting that challenges remain in the application of AR in this area. Further reflection on the underlying causes of these limitations leads us to ponder the contemporary challenges in integrating this technology into the training and care settings.

4.3. Sensitivity Analysis of Included Studies

Given the notable heterogeneity identified in the meta-analysis (Q = 90.84, I² = 86% > 75%, p < 0.00001), a sensitivity analysis was performed to validate the accuracy of the results obtained. This analysis was conducted on the 14 studies, excluding those that demonstrated significant differences [42,60,62,68,85,91]. In order to assess the possible variations in overall effect sizes, the aforementioned studies were excluded. Following this exclusion, the heterogeneity linked to the impact of AR on industrial training and attendance decreased, registering an I² = 0% and a p = 0.65 (Figure 7). This reduction in heterogeneity suggests that differences in design protocols, data collection tools and measurement methods could be the underlying factors for the heterogeneity initially detected. This analysis indicates that the efficiency of AR in terms of assistance and training is significantly positively correlated with its implementation in the industrial context.

5. Discussion of the Application and Effectiveness of AR in Industrial Assistance and Training

The adoption of technologies such as AR in the field of industrial assistance and training is at an early stage, but with great potential to revolutionise industry as an innovative way of transmitting essential skills and knowledge through the continuous training of employees, workers and professionals. Its application extends to workshops, factories and construction projects, where AR allows workers to access real-time information, visualise complex processes and receive remote assistance, resulting in more practical training and more efficient problem solving. However, the integration of this technology into assistance and training within the industrial sector presents challenges that are detailed and discussed, complemented by academic findings from previous research.

Immersive AR training promotes interactive learning and skills development through simulation-based instruction by recreating a real environment in a virtual context, providing a risk-free learning experience. This methodology, supported by research such as that of Webel et al. [40], has proven to be essential for exploring and practising in complex environments without facing the inherent dangers of a real industrial environment. It is particularly useful in the training of personnel in technical and healthcare disciplines.

Conversely, AR serves as an invaluable tool in the analysis of manufacturing processes, enabling the visualisation and comprehension of intricate configurations in production. Its applications encompass real-time control, failure analysis, task simulation, workflow optimisation and energy optimisation. These contributions are pivotal to optimisation and quality control in manufacturing within Industry 4.0, as evidenced by studies such as Wang et al. [44].

In the field of design, AR tools are emerging as catalysts for the 3D visualisation of prototypes and models. These tools foster a more immersive and participatory design process, optimising the accuracy and efficiency of the creative process, according to Richard et al. [71]. Thus, a more faithful and tangible representation of the projects under development is achieved.

AR-supported industry learning tools provide an immersive, hands-on learning experience. These tools encompass a range of services, including personalised assistance and on-site training, as well as virtual training and AR tutorials, which are tailored to industrial needs. Scaravetti and François [69] highlight that these tools have enhanced information retention and facilitated the understanding of complex procedures.

In the context of Industry 4.0, real-time feedback provided by AR stands out as an essential application, as it enables instant communication and correction during manufacturing, fabrication and assembly processes. For instance, Buń et al. [73] investigated the utilisation of AR devices for remote support in manufacturing. Similarly, Park et al. [55] applied AR in task assistance, utilising advanced techniques such as 3D spatial mapping and RGB-D data, resulting in rapid and precise real-time responses. Dong et al. [64] designed an AR system for manufacturing assistance, providing dynamic feedback and motion prediction.

Nevertheless, despite the proliferation and diversity of AR applications in industry, a gap in design methodology has been identified: the absence of a unified and systematic baseline approach to designing adaptive AR assistance instructions for industrial operations. This gap becomes even more palpable when considering the complexity and variability of industrial operations, where each process or task has specific characteristics, risks and requirements. AR has the potential to offer customised and adaptive solutions for each of these scenarios. However, without a systematic design approach, there is a risk that AR solutions are developed in an ad hoc manner, which could lead to inconsistencies, redundancies or even failures in their implementation. This lack of standardisation and consistency in the design of support instructions can limit the effectiveness of AR, wasting its potential and, in adverse situations, adding complications or risks to industrial processes.

Conversely, in the field of safety training, AR is being integrated as a tool that can enhance the standard of training deemed effective in Industry 4.0. Virtual training systems have been developed that have enhanced the current methods of teaching safety protocols related to manufacturing processes [46,50]. The adoption of panoramic AR viewers has enhanced the interactive user experience in safety training [58]. In terms of equipment operation, an AR system has been proposed that enables workers to immediately detect hazards associated with equipment and machinery in their work environment [74].

Furthermore, the utilisation of AR has enabled workers to gain enhanced capabilities for the identification of risks in real time. Research conducted by Tatić and Tešić [47] and Runji and Lin [58] has demonstrated the potential of AR in facilitating the improvement of risk identification and management, thereby contributing to the creation of safer workspaces.

Although AR has been demonstrated to be effective in training and hazard detection, its capacity for early risk prevention in the context of Industry 4.0 has not yet been fully explored. Nevertheless, despite the progress made, occupational risk prevention involves not only the identification of hazards but also the anticipation and proactive management of potential risks before they become real threats. While AR has proven its efficacy in training and hazard identification, its potential for anticipatory risk prevention in the context of Industry 4.0 has not yet been fully realised. This gap in the application of AR for the proactive prevention of occupational hazards presents a significant opportunity for research and development. Industry could greatly benefit from AR systems that not only detect hazards but also propose early solutions and prevention strategies.

Such applications could encompass a range of scenarios, from AR simulations that recreate various risk scenarios and provide real-time solutions, to systems that supervise and monitor the work environment and alert workers to potential risks before they become workplace accidents.

6. Conclusions

In the context of Industry 4.0, the implementation of AR has experienced a significant boom, particularly in the areas of assistance and training. The results of the studies analysed highlight the wide variety of applications that have been identified and categorised into specific areas. In the area of assistance, applications are predominantly focused on virtual operation guides and safety assistance. Meanwhile, in training, applications range from immersive learning to manufacturing process analysis, including assisted design tools and AR-based training resources.

Despite the considerable heterogeneity among the studies analysed, the findings of the meta-analysis indicate a positive and significant correlation between the effectiveness of AR in care and training and its application in industry. This correlation, although moderate, suggests that, despite challenges in its effective implementation, the use of AR in industry contributes significantly to the improvement of care and training processes. Consequently, further research into and optimisation of the integration of AR in industrial environments is essential to enhance its effectiveness.

Although the results of the study indicate the significant potential of AR to revolutionise industrial processes, there are certain limitations and challenges in its adoption, particularly in the areas of training and assistance within Industry 4.0. One of the main limitations identified is the absence of a unified and systematic method for developing adaptive AR support instructions for complex industrial operations. Furthermore, despite AR’s potential to improve safety in industrial environments, its capacity for proactive risk prevention has yet to be explored. Further research in this direction is essential to ensure that AR not only optimises productivity but also actively contributes to the creation of safer working environments in Industry 4.0.