Parul Khanna
Ramin Karim
Phillip Tretten- Lulea Tekniska Universitet, Luleå, Sweden
The metaverse is an emerging technological shift that enhances collaboration, telepresence, and decision-making, and can revolutionise industrial maintenance practices. While immersive technologies, such as Extended Reality (XR) encompassing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), are widely applied in domains like gaming, healthcare, and education, their adoption in industrial workflows remains limited. Its development and implementation carry challenges, especially from a Human-System Interaction (HSI) perspective. The purpose of this research is to understand the key technological issues and challenges associated with the implementation and use of the metaverse in industrial maintenance from an HSI perspective. This study employs a structured, systematic literature review focusing on the metaverse, as enabled by immersive technologies, in the context of industrial maintenance. The reviewed literature was analysed using thematic qualitative analysis to identify recurring HSI-related challenges and to develop a taxonomy categorising these challenges. The analysis resulted in a taxonomy comprising seven key challenge categories: usability, data management, accessibility, user experience (UX), technological performance, environmental and contextual awareness, and trust and transparency. The findings highlight UX as the core factor influencing adoption, as most challenges directly or indirectly impact user experience. The findings indicate that addressing these challenges can enable intuitive, transparent, and reliable metaverse systems tailored to industrial needs. However, advancing the industrial metaverse will require an interdisciplinary approach that combines engineering, human factors, data science, and design to deliver systems that are both technologically advanced and human-centred.
1 Introduction
Digitalisation has revolutionised the maintenance process, particularly with the adoption of concepts such as cyber-physical systems (CPS) and the metaverse, which enable enhanced precision, reliability, and rapid information processing (Werbińska-Wojciechowska and Winiarska, 2023). This digital transformation not only reduces downtime and optimises the use of resources but also enhances safety in complex and high-stakes industrial environments (Saihi et al., 2024). Industrial maintenance is changing rapidly and becoming increasingly complex with the adoption of emerging digital technologies, including Machine Learning (ML), Artificial Intelligence (AI), Digital Twins (DT), and immersive technologies such as Extended Reality (XR) is becoming increasingly data-driven, collaborative, and complex. These advanced technologies help enhance condition monitoring, maintenance schedules and fault diagnosis and resolution (Ven et al., 2023). Traditional maintenance practices are being replaced by predictive and proactive maintenance strategies, which are supported by real-time data, analytics models, and immersive visualisations.
Integrating these advanced technologies and concepts within the traditional maintenance system introduces the need for systematic planning and technology management to overcome the complexities of digital transformation (Saihi et al., 2024). Such a transformation deals with more than just technical upgrades. It demands the rethinking of possible changes in the maintenance process, stakeholder collaboration, and organisational readiness (Onu et al., 2023; Souza et al., 2024). However, challenges arise in the context of data being fragmented across connected subsystems, cognitive overload of complex data being streamed for interpretation, as well as limited collaboration across teams, which can be due to resource limitations, geographical constraints, or organisational structures. These challenges highlight the need for a maintenance ecosystem that can be more immersive, intuitive, context-aware, and collaborative ecosystems capable of supporting telepresence, real-time monitoring and decision support. The concept of telepresence, first coined by Minsky (1980), refers to projecting one’s presence into a remote environment through technology. Later definitions emphasised both the ability to monitor and manipulate remote systems (Sheridan, 1992) and the subjective sense of presence in mediated environments (Steuer, 1992). In industrial contexts, telepresence enables maintenance teams to collaborate, monitor, and intervene in real time without physical co-location. Such a solution would allow the maintenance teams to work more effectively in increasingly complex environments.
The industrial metaverse is an emerging concept that presents potential in this context. The metaverse is not just a world we enter but a world we create, and its full potential is defined by how we interact with it. This statement encapsulates the immersiveness, user-centricity and interaction aspects of the metaverse, as inspired by Ball’s work (Heath, 2023). The term “metaverse” goes back to 1992, when Neal Stephenson coined the term in his novel “Snow Crash.” He defines metaverse as a version of the internet where humans interact with each other and other software components in a virtual world (Stephenson, 1992). Building upon this fictional idea, the metaverse has transitioned now to a framework driven by technologies including virtual reality (VR), augmented reality (AR), spatial computing, and AI. It represents an immersive digital world facilitated by the above-mentioned technologies and offers opportunities for collaboration, exploration, and innovation (Heath, 2023; Dwivedi et al., 2022). However, a successful adoption of the metaverse in maintenance requires a human-centric design and implementation of the technological ecosystem.
This is where Human-System Interaction (HSI) becomes essential. HSI focuses on human-centric design for an optimal interaction between humans, technology, and processes, especially concerning usability, trust, situational awareness, and safety (Jenkins et al., 2013). Despite its importance, HSI has received limited attention when it comes to the design and implementation of metaverse for industrial maintenance. This lack of attention to HSI aspects may result in increased cognitive load and hindrance in operations (Wang et al., 2023). Addressing these is essential to have a human-centred design and evaluation of the industrial metaverse ecosystem.
The purpose of this research is to understand the key issues and challenges associated with the implementation and use of the metaverse in industrial maintenance from an HSI perspective. The focus of this study is limited to the industrial maintenance domain, which stands out as a critical and complex application of the metaverse. Hence, the objectives of this research are:
Objective 1: To identify key HSI challenges in adopting the metaverse in industrial maintenance.
Objective 2: To develop a taxonomy to categorise the identified challenges, which can help in integrating metaverse within industrial maintenance operations.
During the literature analysis, it became evident that many of the observed HSI challenges are significantly influenced by the level of technology integration within industrial metaverse systems. In this context, the level of technological integration refers to how well metaverse facilitating tools and technologies like AR/VR, digital twins, IoT data streams, AI/ML models, network connectivity, and traditional maintenance systems integrate and function together. The reviewed literature emphasised that elements such as interoperability, data synchronisation, latency, hardware-software compatibility, and system stability directly impact critical HSI aspects, including cognitive load, trust, and interaction quality, which directly impact the usability of the system. These technological factors were not categorised as a distinct category in the taxonomy due to their overlap across multiple categories simultaneously. But their influence was strong enough to need more study.
Based on these objectives and insights, the research questions guiding this study are:
RQ1: What are the key HSI challenges in adopting the metaverse in industrial maintenance?
RQ2: How can a taxonomy be developed to categorise the challenges of integrating the metaverse within industrial maintenance operations?
RQ3: What is the relationship between usability aspects and technology integration in metaverse systems for industrial maintenance workflows?
The paper is structured as follows: Section 2 describes the research methodology used to conduct this research. Section 3 discusses the background of the metaverse’s evolution and HSI aspects related to it. Section 4 presents the outcome of the analysis of the literature review, including a detailed taxonomy of challenges associated with implementing the metaverse in industrial maintenance settings. Section 5 discusses the implications of these results within the context of an Industrial Metaverse framework. Section 6 describes a general discussion of the research, and Section 7 provides a conclusion and future work.
2 Research methodology
This study is done to understand the technological issues and challenges associated with the industrial metaverse in maintenance, particularly focusing on HSI. The first step is to explore the conceptual origin of the metaverse, narrowing it down to its industrial applications, particularly in maintenance. Further, the focus shifts to the HSI aspects, identifying research gaps and the HSI challenges in the industrial metaverse. The challenges are then systematically categorised, intending to guide the design and development of user-centric, intuitive, and inclusive metaverse systems that support maintenance professionals in performing their tasks more effectively and efficiently. Figure 1 illustrates the multi-stage process used to identify, filter, and include sources for the review. It highlights the progression from planning and database search to screening, extended search, and finally thematic analysis, ensuring a systematic and comprehensive identification of HSI challenges.
Figure 1. Research methodology followed in this study for creating the taxonomy of issues and challenges in integrating metaverse within industrial maintenance operations.
2.1 Planning
This study employs a Systematic Literature Review (SLR) approach, guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (Moher et al., 2009). The review is initiated by establishing a theoretical background and defining the research objectives. Following this, research questions were framed to investigate how the concept of Metaverse can be applied using immersive technologies such as VR, AR, and MR in industrial maintenance, with a specific focus on HSI. A research approach is then developed to answer the framed questions. A review of existing literature is conducted to establish a theoretical foundation for this study.
2.2 Conducting the review
The query used for the database search is (“metaverse” OR “virtual reality” OR VR OR “augmented reality” OR AR OR “extended reality” OR XR OR “mixed reality” OR MR) AND Maintenance AND (“Human system Interaction” OR “HSI” OR “Human machine interaction” OR “HMI” OR “Human computer interaction” OR “HCI” OR “software ergonomics” OR “User Experience”) which included terms related to immersive technologies and also kept the scope within the maintenance domain.
The search is restricted to the title, abstract, or keywords for Scopus, the title and abstract in the ACM Digital Library, and the abstract in the case of Google Scholar. Google Scholar was included because it aggregates publications from numerous major publishers (e.g., IEEE, Springer, Elsevier, Wiley, MDPI, and arXiv), ensuring wider coverage beyond the databases explicitly searched. In total, 611 resources were initially retrieved (Scopus = 441, Google Scholar = 97, ACM Digital Library = 73). Following the search, some inclusion and exclusion criteria were applied as follows.
• Only peer-reviewed papers published in English, from 2021 onwards, and with a technological focus were included.
• Studies that were duplicates, non-relevant, or focused solely on non-industrial contexts were excluded.
Following this screening process, we identified 59 research studies for inclusion in our literature review. A detailed review of these papers helped to preliminarily identify the key challenges and existing gaps in the application of metaverse technologies for industrial maintenance. To strengthen the review, an extended search is also conducted. This included the Backward citation tracking, which is a part of the snowballing method, by examining the reference lists of the shortlisted articles (Greenhalgh and Peacock, 2005; Wohlin, 2014). To make a solid theoretical foundation, standards for definitions and relevant white papers were also explored. In total, 91 relevant references were considered in this paper, ensuring a comprehensive foundation for addressing the research objectives.
2.3 Qualitative analysis
This study adopts a qualitative systematic review with thematic analysis of the selected literature, rather than a bibliometric or citation network approach. This approach follows the recommendations of the work discussed in Guest et al. (2012). The analysis aims to identify key themes, patterns, and challenges related to the integration of the metaverse in industrial maintenance processes. The taxonomy is organised into several categories, where each category represents a critical dimension of the challenges faced in integrating metaverse technologies into industrial maintenance workflows. Within the developed taxonomy, the categories and their further subcategories were systematically formed. We highlight key themes and recurring topics, and group related observations into broader themes. During the analysis, some overlapping themes were also identified, which reflected their multifaceted nature. The taxonomy is based on thematic analysis and is therefore not intended to present mutually exclusive sub-categories, as certain challenges naturally span multiple dimensions. Further, the subcategories were determined by delving deeper into the observations within each category. This iterative process ensured that the developed taxonomy is comprehensive and closely aligned with the raw data.
2.4 Illustrative conceptual mapping of usability and technology integration
In addition to the thematic analysis, an illustrative conceptual mapping is done to explore the relationship between usability and technology integration in metaverse-enabled maintenance systems. While reviewing the literature, it was noticeable that most studies discussing the metaverse in industrial maintenance touched on these two recurring aspects. References to ease of use, user adaptation, and cognitive workload were grouped under the dimension of usability, whereas references related to system connectivity, interoperability, and technological sophistication were grouped under technology integration. These two dimensions were then used as axes in a quadrant-based framework. Both were conceptualised as continuums (decreasing to increasing). Crossing these axes produced a four-quadrant map that helps illustrate the trade-offs highlighted in the literature. Only studies addressing both these dimensions were considered in this mapping. When reviewing the studies, many described systems that were advanced but difficult to use, or simple and user-friendly but limited in scope. Others highlighted cases where both usability and integration were either lacking or well aligned with each other. These patterns highlighted that the two dimensions often interacted in different ways, which led to arranging them on the crossing axes of usability and technology integration. The result was a conceptual map that illustrates the trade-offs observed across the literature. Themes identified in the literature were mapped along these axes according to their emphasis, resulting in four quadrants.
• Q1: Decreasing usability, increasing technological integration (complex advanced systems)
• Q2: Decreasing usability, decreasing technological integration (inefficient maintenance processes)
• Q3: Increasing usability, decreasing technological integration (accessible but limited systems)
• Q4: Increasing usability, increasing technological integration (balanced and optimal systems)
This mapping is not a quantitative model but an interpretive tool to communicate patterns more clearly. A detailed description of the four quadrants is provided in the Section 5.2.
3 Background
3.1 Evolution of metaverse
As per Gartner’s hype cycle for emerging tech 2022, the metaverse has entered the trend with an outlook of more than 10 years. It is seen as an evolution of the existing state of the internet, with significant potential, growing interest and the promise of transforming our digital experiences and interactions (Gartner, 2022). However, despite the rapidly growing interest, there is currently no universally developed standard for the metaverse. Industry leaders like Meta, Microsoft, McKinsey, and Nvidia envision the metaverse as the next evolution in digital interaction and connectivity, building on the foundation of the internet and mobile technology. Table 1 lists representative definitions and perspectives of the metaverse concept across academia and industry.
Table 1. Definitions and perspectives on the metaverse.
The COVID-19 pandemic has proven to be a catalyst in the emergence of this immersive technology (Wang et al., 2022). When mobility around the world was restricted due to the pandemic, technologies offering telepresence services proved to be essential. While the existing internet technologies made remote connections and collaborations possible, the need for immersive and interactive tools was observed (Astaneh Asl and Dossick, 2022; Fernandes and Werner, 2022). Metaverse offers one alternative for an immersive and interactive experience for remote connections (Astaneh Asl and Dossick, 2022; Yu et al., 2022; Mayer et al., 2023).
With the advancements and growth of the metaverse, its application is expanding beyond gaming, entertainment and social interaction to industries such as healthcare, education, and industrial operations and maintenance (Khanna et al., 2024; Mystakidis, 2022; Hwang and Lee, 2022; Kerawalla et al., 2006). A subset of this broader concept, the industrial metaverse has emerged as a promising innovation as an enabler for immersive simulations, effective maintenance, and collaborations in high-stakes environments in industries. This enhances the overall productivity, safety and efficiency in industrial settings. Maintenance-related tasks require high precision and real-time collaboration. Integration of metaverse in industrial maintenance enables virtual representations of physical assets, which are increasingly being used to monitor and optimise their operations and predict failures, resulting in timely maintenance, leading to cost savings and increased operational efficiency (Dwivedi et al., 2022). Additionally, the industrial metaverse offers opportunities for remote collaboration through shared digital environments. Users can simultaneously work together in the same digital environment without the geographic restrictions of being present at the physical site. This might help in minimising downtime and improving productivity. Moreover, training environments in VR or AR offer a real-life-like simulation, enabling users to acquire and develop critical skills in risk-free environments. This is particularly valuable in industries such as manufacturing, mining, aviation, and railway systems, where traditional skill training methods are costly and carry inherent safety risks (Khanna et al., 2024) according to the report by Siemens, together with the MIT Technology Review, the industrial metaverse is described as a metaverse sector that simulates complex systems such as machines, factories, cities, and transportation networks. It offers its users fully immersive, real-time, interactive, and synchronous representations and simulations of the real world (Siemens, 2024). It represents an advancement in the integration of digital technologies with industrial processes. It combines advanced technologies to create immersive digital environments that can enhance productivity, decision-making, and overall process optimisation in industrial settings (van der Valk et al., 2024; Ren et al., 2024). The work by van der Valk et al. (2024) characterizes the industrial metaverse into six core functionalities in supply chain management, including visibility and monitoring, prediction, simulation, collaboration, training, and optimisation. The application of the metaverse was observed throughout these phases, and a noticeably strong focus was found on optimisation, simulation, collaboration, and training (van der Valk et al., 2024).
Various industries, including education, tourism, construction and IT sectors, are adopting metaverse technology (Fernandes and Werner, 2022; Marti Mason et al., 2020; Proniewska et al., 2021; Mavrin et al., 2022; Chamorro-Atalaya et al., 2023; Karl et al., 2022; Standaert et al., 2021). The education industry is now shifting towards more online classes, which fosters innovation and creativity in teaching approaches (Chamorro-Atalaya et al., 2023; Wang M. et al., 2024). Corporate industries, especially the IT sectors, are also getting accustomed to the increase in online meetings and digital collaboration techniques (Karl et al., 2022; Standaert et al., 2021). In the smart manufacturing industry, the industrial metaverse is believed to facilitate enhanced visibility, intelligence, and overall production (Ren et al., 2024). This technology also holds potential in other industrial applications, like in the financial sector, telehealthcare and connected vehicles (Bhattacharya et al., 2023). However, the industrial metaverse is still being explored as it is in its early stages of development and implementation. It has several challenges that need to be addressed for successful adoption, and these include limitations in the design, implementation, and other technological aspects (van der Valk et al., 2024; Ren et al., 2024; Yang et al., 2023).
A key technology that is seen as a fundamental aspect of implementing the industrial metaverse is digital twins, which are digital representations of physical assets or processes (Ren et al., 2024; Yang et al., 2023; Mourtzis, 2023). ISO standard 30,173 defines a digital twin as “a digital representation of a target entity with data connections that enable convergence between the physical and digital states at an appropriate rate of synchronisation” (International Organization for Standardization & International Electrotechnical Commission, 2023). Digital twins enable real-time monitoring, which enhances the optimisation of industrial operations (Ren et al., 2024; Mourtzis, 2023). Complementing this ability, the integration of advanced technologies such as Extended Reality (XR), which is an umbrella term for technologies like VR and AR, facilitates enhanced telepresence among users (Bhattacharya et al., 2023). This is particularly useful where traditional ways of communication are difficult (Bhattacharya et al., 2023).
The industrial metaverse is emerging as a transformative tool in maintenance practices across various industries (Khanna et al., 2024; Oppermann et al., 2023; Shrestha and Imamoto, 2024). It offers innovative techniques for telepresence and assistance, predictive maintenance, and digital training. It has been proposed as an innovative and efficient way to disseminate information in the context of railway maintenance and operations (Shrestha and Imamoto, 2024). Remote assistance and collaboration are observed to be some of the key applications of the industrial metaverse. A 5G mixed reality toolbox has been developed to support hands-free remote assistance in industrial settings, providing mixed reality functionality for on-site and in-office workers collaborating in a shared digital space (Oppermann et al., 2023). This system allows technicians to work on the CAD data of actual machines, creating a realistic prototyping environment (Oppermann et al., 2023).
Predictive maintenance is another important application of the industrial metaverse. By leveraging advanced technology like digital twins and IoT technologies, maintenance personnel can monitor equipment in real-time, predict potential failures, and schedule maintenance activities proactively (Negi et al., 2024). Digital training is also an area where the industrial metaverse is making an impact. This facilitates a safe and cost-effective way of training the maintenance personnel (van der Valk et al., 2024). Implementation of the industrial metaverse in a maintenance context has significant challenges as well. These challenges include the requirement for high-performance computing infrastructure, issues regarding data security and the integration of legacy systems with such advanced technology (Oppermann et al., 2023; Prabadevi et al., 2025).
3.2 Human- system interaction in metaverse
HSI is comprehensively defined through various standards and guidelines that emphasise usability, user experience, accessibility, and inclusivity. Table 2 lists the definitions of some of the key HSI aspects.
Table 2. Key definitions related to HSI.
ISO standard on Ergonomics of human-system interaction - Processes for enabling, executing and assessing human-centred design within organisations further refines HSI by detailing the processes for enabling, executing, and assessing human-centred design within organisations, emphasising the importance of understanding user interactions, needs, and requirements (International Organization for Standardization, 2019b). The W3C guidelines on Accessibility, Usability, and Inclusion highlight the importance of accessibility and inclusion (World Wide Web Consortium/Web Accessibility Initiative, 2025). It ensures guidelines for a universal design principle so that a diverse range of users can interact with digital content (World Wide Web Consortium/Web Accessibility Initiative, 2025). Integrating these perspectives provides a robust framework for understanding and designing effective, efficient, and inclusive HSI.
HSI focuses on optimising the interaction between users and complex industrial systems. This involves designing intuitive, user-friendly, and accessible interfaces to enhance user performance. For example, extended reality (XR) technologies are being utilised to create more realistic and efficient interfaces for industrial maintenance processes (Kour et al., 2022). These technologies enable technicians to visualise and interact with digital representations of physical assets, which facilitates tasks such as maintenance, training, and telepresence and assistance (You et al., 2024). Further, advancements in technologies like AI and ML are supporting advancements in HSI aspects in the industrial metaverse. AI-based systems can adapt to and even predict user behaviour and provide customised assistance, enhancing the overall UX. For example, intelligent PID tuning systems in virtual reality scenes demonstrate how AI can be integrated into the industrial metaverse to optimise control processes (Chai et al., 2024).
Critical issues and challenges in HSI aspects restrict the industrial metaverse from realising its full potential. The implementation of HSI in the metaverse has several associated challenges, which include addressing the interaction and collaboration challenges in virtual-physical environments (Wenzheng, 2023; You et al., 2024). Addressing and overcoming these challenges requires research in interdisciplinary fields such as computer science, cognitive psychology, and engineering (Yang et al., 2024). Industrial systems often involve complex user interfaces, large-scale data management, and real-time decision-making, increasing the cognitive and physical demands on users. Effective and efficient HSI techniques in the industrial metaverse ensure an intuitive, easy-to-navigate system, helping users to manage information efficiently and adapt to dynamic virtual environments. Poorly designed systems can lead to misinformation, inefficiency, mental fatigue and errors (Norman, 2013). For example, users engaged in tasks like fault detection and diagnostics often face data visualisation challenges and poorly designed interfaces that increase cognitive load and limit their productivity (Khanna et al., 2024; Hashash et al., 2023).
In the industrial metaverse, usability and accessibility aspects of the systems play a crucial role. Such systems should be able to support an immersive collaborative environment and an adaptive and error-resistant workflow. However, current metaverse solutions often fail to meet the specialised needs of industrial users. Also, there is a significant need to address human factors in the industrial metaverse. Extended sessions in immersive digital environments can lead to mental fatigue, physical discomfort, and overall reduced user performance (Rebenitsch and Owen, 2016), factors that are critical in high-risk industries. In addition to this, the lack of inclusive and adaptive interfaces often restricts users with disabilities or those operating in high-stress environments (Dwivedi et al., 2022; Huang et al., 2022). Emerging technologies like Explainable AI (XAI) introduce new challenges to HSI aspects of such systems. XAI holds the potential to enhance the transparency, trust and adaptability of AI systems in the industry. Enhancing trust and transparency in these systems requires technological advancement and intuitive interfaces that will allow users to understand and comprehend system recommendations, leading to enhanced decision-making. This is crucial for high-stakes and collaborative environments (Khanna et al., 2024; Huang et al., 2022). These gaps highlight the need to address the multidimensional and interconnected issues and challenges of HSI in the industrial metaverse. Addressing the HSI aspects, including usability, accessibility, data management, and trust, can lead to enhanced adaptability and efficiency of such systems (Mystakidis, 2022).
3.3 Human- system interaction in industrial maintenance
3.3.1 Why does maintenance require human- system interaction?
Industrial maintenance involves time-critical tasks in high-stakes, dynamic and sometimes hazardous environments. This makes a robust HSI essential for accuracy and safety (Illankoon, 2020; Heinold et al., 2023). HSI enables maintenance personnel to effectively interpret complex sensor data and their diagnosis, perform root-cause analysis (RCA), and perform maintenance actions (Franciosi et al., 2019). As maintenance operations integrate digital tools such as dashboards, wearable AR systems, and predictive analytics, the potential for cognitive overload and error increases if interfaces are poorly designed (Illankoon, 2020; Costa et al., 2022). Studies highlight that user-centred, context-sensitive HSI improves productivity, reduces operational risk, and supports safe task execution (Illankoon, 2020; Hawwach, 2021). Conversely, inadequate HSI can lead to costly human error and increased downtime, especially in safety-critical sectors like energy and manufacturing (Illankoon, 2020).
3.3.2 How is human- system interaction different in metaverse?
The industrial metaverse changes the HSI aspects subsequently. It shifts the interaction from traditional 2D screens to immersive XR environments that blend the spatial 3D digital environment with the real one (Kour et al., 2022; Kour et al., 2025). Users must now interpret data and make decisions in environments where the digital and physical domains overlap, which amplifies cognitive demand and requires heightened situational awareness (Kour et al., 2025; AI-PRISM, 2024). New modalities such as gesture, gaze, and haptic feedback introduce their own ergonomic challenges, including gesture fatigue and head-mounted display discomfort (Kour et al., 2022; Kim et al., 2023).
An important challenge is to ensure user trust and transparency in intelligent systems, especially as recommendations and alerts increasingly rely on algorithmic and AI-driven processes. A clear explanation of system outputs becomes vital to prevent misuse or operator hesitation (Sharma et al., 2024; Wanner et al., 2022; TTC Labs and Meta, 2023). Another aspect is the need for adaptive interfaces that intelligently adjust to user expertise, task context, and environment. This aspect is under-researched and underdeveloped in current solutions (Kour et al., 2025; AI-PRISM, 2024).
Overall, research shows that successful HSI in the industrial metaverse do not just rely on technological innovation but also on strong human-centred design principles, encompassing ergonomics, explainability, and adaptability (Kour et al., 2025; Zhang S. et al., 2025).
4 Outcome of the analysis
A thematic analysis based on the literature review was done to develop a taxonomy of challenges. Figure 2 presents a word cloud generated through thematic analysis, where word size reflects the frequency of occurrence across the reviewed literature. The prominence of terms such as interaction, security, privacy, trust, usability, interface, and governance reflects the diversity of concerns surrounding human engagement with metaverse technologies in industrial contexts. These recurring concepts serve as the analytical foundation for identifying and categorising the key HSI aspects relevant to the industrial metaverse, facilitating the development of the proposed taxonomy of challenges.
Figure 2. Word cloud depicting the most frequently occurring themes identified in the thematic analysis.
Some of the areas highlighted in the thematic analysis, such as usability, interface design, accessibility, data management, and trust, have existing taxonomies. These taxonomies are often focused on domains, including Human-Computer Interaction (HCI), information systems, software engineering, and enterprise data management, among others. However, this study shows that integrating metaverse in industrial maintenance introduces unique challenges that are not reflected in existing taxonomies. Factors such as real-time synchronisation for digital twins, immersive AR/VR interaction, latency-sensitive data transmission, and multimodal collaboration create challenges that extend beyond the scope of existing taxonomies. While certain factors align with existing taxonomies, the scope of this study requires examining them from a different point of view, one that reflects the immersive, real-time, and safety-critical nature of maintenance workflows. This creates a need for a dedicated taxonomy that captures the HSI challenges emerging specifically when integrating metaverse within industrial maintenance.
Figure 3 shows the developed taxonomy highlighting the key HSI challenges that need to be addressed for enhanced integration of metaverse technology in traditional maintenance practices. Table 3 further expands this taxonomy by mapping each category to specific subcategories and associated user impacts, thereby translating the conceptual structure into an actionable framework. This taxonomy forms the basis for understanding the diverse challenges and highlights critical areas requiring attention to ensure seamless integration of metaverse technologies into industrial workflows.
Figure 3. Visual overview of the proposed HSI taxonomy, showing the seven interrelated challenge categories identified through the systematic review.
Table 3. Taxonomy of HSI challenges in the industrial metaverse, showing categories, subcategories, and their impacts on users in industrial maintenance.
4.1 Usability and interface design
Effective HSI of a system depends on good usability and interface design, which reflects how simple it is for users to become familiar with, utilise, and navigate a system (International Organization for Standardization, 2018, 2019a). In the case of complex systems, users face challenges related to navigation, which requires extensive training (Jiang et al., 2025). Systems with navigation challenges might result in users focusing more on understanding the system rather than accomplishing their tasks. Sometimes, it might even become a challenge for the users to find the desired information (Zhang C. et al., 2025). Such challenges increase cognitive load among users, which overwhelms them and reduces their working efficiency (Jiang et al., 2025; Cao et al., 2025). Usage of such non-intuitive systems may lead to user fatigue, which negatively impacts decision-making and user satisfaction (Cao et al., 2025; Omar et al., 2024). This further affects the user’s ability to stay focused and engaged, which increases the risk of errors and lowers the user’s productivity (Zhang C. et al., 2025; Omar et al., 2024). Addressing these challenges can help elevate system adoption. It is crucial, especially in high-stakes industrial systems with complex workflows (International Organization for Standardization, 2019a; Cao et al., 2025). These challenges can be mitigated through human-centred design, intuitive navigation structures, and iterative usability testing to ensure systems remain simple, efficient, and user-friendly.
4.2 Data management
Data management in the metaverse refers to handling and managing data, as it is the core for insightful analysis and decision support. It is also a significant challenge due to the vast amounts of real-time data that must be efficiently processed and displayed for user decision-making. Delayed, unsynchronised or incorrect data disrupts the original workflow, can lead to incorrect actions and reduces user trust (Ren et al., 2024; Mourtzis, 2023; Wang Y. et al., 2024). It proves to be a concern, especially in scenarios that require critical decision-making with up-to-date real-time data. Another significant challenge with managing data in the industrial metaverse is data interoperability. The industrial metaverse is an ecosystem consisting of diverse data sources. Poor data integration among these sources often results in disrupted or inefficient workflows and, in some cases, redundancies. This can negatively impact the overall UX (Saihi et al., 2024; Ven et al., 2023; Ren et al., 2024). Another concern that emerged during the analysis is data security. Industrial metaverse systems handle huge volumes of sensitive data. Users have a fear of data breaches or even the misuse of information (Sharma et al., 2024; Wanner et al., 2022; Chen et al., 2023; Odeh and Yang, 2024). Such concerns directly impact user engagement and trust in the system (Wanner et al., 2022). Improved data governance frameworks, secure interoperability standards, and real-time synchronisation mechanisms are essential to overcome data-related challenges.
4.3 Accessibility
The system’s accessibility aspects focus on designing user-friendly systems that can be used by a diverse range of users with varying characteristics, including different skill levels, prior experience, cognitive capacities, and disabilities. Well-accessible systems can accommodate the widest range of users without needing adaptations (International Organization for Standardization, 2025), (World Wide Web Consortium/Web Accessibility Initiative, 2025). Users with visual impairments or limited familiarity with metaverse technologies struggle to interact with the systems effectively. This lack of inclusivity while designing the systems also limits the usability aspects for diverse groups of users. It poses as a critical issue, especially in industrial environments that employ a diverse workforce (International Organization for Standardization, 2019a, 2019b; Omar et al., 2024). During our analysis, we observed another challenge due to the lack of adaptive interfaces (Wang et al., 2023; Hamdani and Chihi, 2025; Darejeh et al., 2024). There is a need to adjust the industrial metaverse systems to user-specific roles, needs, preferences, or skill levels (Hamdani and Chihi, 2025; Darejeh et al., 2024). For example, users with no or less familiarity with metaverse technology found the systems overwhelming, while users with some experience felt restricted by inflexible interfaces that did not accommodate advanced workflows (International Organization for Standardization, 2025; Kour et al., 2025). Designing with universal design principles, adaptive interfaces, and inclusive technologies can help ensure accessibility for diverse users and equitable adoption across the workforce.
4.4 User experience (UX)
UX in the industrial metaverse reflects how users perceive and engage with digital environments. From our analysis, UX emerged as a core factor that could influence the successful adoption of industrial metaverse systems (Dwivedi et al., 2022; Wang et al., 2023; Kour et al., 2025). Systems that would disrupt the flow of user tasks due to lag, latency, or inconsistency of data would lead to frustration and overall bad UX (Ren et al., 2024; Hashash et al., 2023; Huang et al., 2022). Other factors that would affect UX included cognitive load, which was a result of complex industrial workflows combined with poorly designed systems. This often leads to human errors, reduced efficiency, and mental fatigue (Jiang et al., 2025; Zhang C. et al., 2025; Cao et al., 2025). Personalisation of interfaces also emerged as a critical challenge that could impact UX. While there are challenges due to the lack of adaptive interfaces, their importance extends beyond accessibility to directly impact UX. Industrial metaverse interfaces that fail to adapt to individual user preferences and workflows can result in inefficiencies and a suboptimal experience (International Organization for Standardization, 2019b; Yang et al., 2024; Hamdani and Chihi, 2025). Inconsistent interface design is another challenge to be addressed for enhanced UX. When working with an integrated system, users can struggle with the inconsistency of the interface layouts, terminology, or navigation patterns across various applications, which may include both metaverse and non-metaverse platforms. Such inconsistencies might result in delays and user frustration (Ven et al., 2023; Wang et al., 2023; Ren et al., 2024). Enhancing personalisation, consistency, and cognitive support through adaptive interfaces and design guidelines can significantly improve overall UX.
4.5 Technological performance
Technological performance challenges in the industrial metaverse are related to the technological design, integration, and optimisation of the system (Ren et al., 2024; Yang et al., 2023; Zhang S. et al., 2025). Performance challenges due to frequent crashes or system failures can lead to disruptions in maintenance tasks and negatively impact the overall trust in the metaverse system (Oppermann et al., 2023; Wanner et al., 2022; Mourtzis et al., 2020). High-stakes industrial environments can rely heavily on continuous operation, and such inconsistency and instability in the system can result in major productivity losses (Ven et al., 2023; Ren et al., 2024). Hardware compatibility also emerged as another critical challenge (Ven et al., 2023). Integrating advanced technology like Metaverse with legacy systems often requires high-end devices to function optimally (Ven et al., 2023). It can be a challenge in industrial settings, where users may not always have access to the latest hardware. Scalability is another technological challenge that has emerged (Wang et al., 2018). Systems may find it challenging to adapt to rapid growth or increased demand. It can be a result of increased data velocity or a rise in the number of users (Wang et al., 2018). The systems should be designed to handle such increased loads without compromising performance (Ren et al., 2024). Addressing this concern is important as it puts the systems at risk of slowdowns or even failures during high-stakes operations in industrial settings (Oppermann et al., 2023; Hashash et al., 2023). Latency adds to this list of technological challenges, which negatively impacts real-time responsiveness or decision-making (Hashash et al., 2023). Addressing technological performance challenges requires robust system optimisation, scalable infrastructure, hardware and software compatibility checks, and latency reduction strategies.
4.6 Environmental and contextual awareness
In the context of the industrial metaverse, environmental and contextual awareness describe the system’s ability to perceive, comprehend, and adapt to the real, physical world while also considering the situational context of the surrounding environment (Ren et al., 2024; International Organization for Standardization & International Electrotechnical Commission, 2023). It facilitates realism of the real world in the digital world (Mystakidis, 2022; Kim et al., 2023). Inaccurate simulations of real-world environments lead to ineffective outcomes for the intended task or even safety risks in high-stakes industrial settings (Oppermann et al., 2023). Another significant challenge is that users require realistic and seamless interaction with the digital world to use the metaverse technology optimally (Hashash et al., 2023; Huang et al., 2022). Industrial environments, which are often dynamic and unpredictable, require metaverse systems that must adapt in real-time to changes in their surroundings (Ren et al., 2024; Mourtzis et al., 2020). Developing such metaverse systems requires significant advanced technology to enhance the ecological validity, which essentially refers to how real the virtual world feels (Kim et al., 2023; Cao et al., 2025). Investing in accurate simulations, real-time sensor integration, and adaptive modelling techniques can help mitigate these environmental and contextual awareness challenges.
4.7 Trust and transparency
In the context of the industrial metaverse, trust and transparency refer to the user’s confidence in the system’s reliability and their ability to understand its process and output (Wanner et al., 2022). Decision-making processes. Such challenges arise when systems lack explainability, which creates uncertainty among users about how the outputs are generated or the reasoning behind the decisions or suggestions, especially in recommendation systems (Sharma et al., 2024; Wanner et al., 2022). In such cases, users often hesitate to rely on the technology, which also slows its adoption (Dwivedi et al., 2022; Kour et al., 2025). Low transparency in such a system creates an opaque box effect, which reduces the user’s confidence in the system’s accuracy and reliability, as they are not able to trust and validate its output (TTC Labs and Meta, 2023). Addressing these challenges not only builds trust among users but also facilitates their engagement confidently with the technology (Wanner et al., 2022; Chen et al., 2023). This will ensure a step closer towards the successful adoption of technology and its effective use in industrial applications. Building trust requires integrating explainability techniques like explainable AI, transparent processes, and validation mechanisms that allow users to understand and verify system outputs.
Table 3 shows the taxonomy based on seven categories. These categories are further broken down into sub-categories and listed with the identified corresponding challenges. Some sub-categories may overlap conceptually, as challenges such as latency, cognitive load, or data inconsistency influence multiple HSI aspects.
To complement the qualitative categorisation presented in Table 3, a frequency analysis of the reviewed studies was conducted. Figure 4 shows the number of studies associated with each HSI category, providing a simple quantitative perspective on their relative prominence. Although the taxonomy remains qualitative in nature, these frequency values offer an indicative overview that enhances transparency and supports the identification of the most emphasised challenges in the literature.
Figure 4. Frequency of HSI challenge categories across the reviewed literature.
5 Implications of the outcome
5.1 Interconnected HSI challenges: impact on the user
The outcome of this study also highlights overlapping challenges across the identified seven categories. This reflects the interconnected nature of HSI dimensions. Figure 5 shows a conceptual representation of the seven HSI challenge categories and their interconnections, illustrating how each dimension influences the overall UX and ultimately impacts the end user.
Figure 5. Interconnected HSI challenge categories shaping UX in the industrial metaverse.
Challenges associated with accessibility, such as inclusivity for users with disabilities and user characteristics like experience, skill level, familiarity with XR, cognitive load tolerance and ergonomic comfort directly have an impact on the overall UX. Users are likely to have a poor interaction experience if there are accessibility issues, as it leads to disengagement. Complex navigation and inconsistent design layouts, which result in poor interface design, have a negative impact on interaction quality and increase cognitive load among users. These are directly related to the critical elements of the UX. Accurate and real-world environmental simulations depend on robust system performance. On the other hand, realistic simulations require high computation as well. Technological issues like high latency or system instability can negatively affect the interactions with the metaverse environment. It may result in reducing immersion and decision-making capabilities. These aspects are also critical for smooth real-time data integration. Such technological issues might lead to delays and inconsistency in data, which might disrupt further workflows and might also affect UX. Further, a lack of system transparency or explainability reduces user trust and negatively impacts interaction quality. Transparent systems lead to enhanced user satisfaction and overall engagement.
These identified overlaps in the categories highlight the interconnected nature of HSI challenges in the industrial metaverse. Addressing these issues holistically is essential to enhance the UX, usability, adoption, and overall effectiveness of metaverse technologies in industrial settings.
5.2 Usability vs. technology integration
During this study, the relationship between the usability aspects and technology integration for metaverse systems in industrial maintenance workflows is also studied. Figure 6 represents this relationship in a quadrant-based framework. Each quadrant represents the effect of usability and the level of technology integration on overall system performance. Integrating advanced technologies like metaverse with poor or low usability might lead to complex system interfaces. Such interfaces demand extensive efforts from the users to adapt and learn the system, even though the system is technologically advanced. This leads to increased cognitive load and operational inefficiencies among the users. They may face navigation-related issues, which lead to increased frustration, errors, and poor UX, and might also result in a decreased likelihood of system adoption. This has been observed in AR-based remote maintenance solutions that integrate real-time data overlays but impose high cognitive load and usability burdens (Q1: Complex System Interfaces) (Yang et al., 2024; Cao et al., 2025; Omar et al., 2024; Mourtzis et al., 2020).
Figure 6. Usability vs. integration of emerging technologies in metaverse systems in industrial maintenance workflows for optimal HSI.
Inefficient maintenance processes often result when there is a lack of both usability aspects and adoption of advanced technologies. These systems are facilitated by traditional and outdated tools and methods that require significant manual effort. Such systems fail to leverage advanced technologies and provide intuitive interfaces, as they are inadequate for addressing the evolving complexities of industrial maintenance tasks. It increases the difficulty for users in using the system. Such systems are typical of legacy maintenance environments with low interoperability, where fragmented tools and manual effort dominate (Q2: Inefficient Maintenance Processes) (Ven et al., 2023).
User-friendly training models are created with a high level of usability consideration and ease of use. It ensures that the users can quickly adapt to and navigate such interfaces with minimal effort or training. However, their simplicity accompanies restricted advanced technological capabilities. Their applicability is limited to more complex and high-stakes industrial workflows. Balancing simplicity with functionality is essential for enhancing such a system’s utility without compromising accessibility. An example is stand-alone VR training environments that are intuitive and easy to adopt but lack integration with live operational data, limiting their industrial applicability (Q3: Accessible but Limited Systems) (Chamorro-Atalaya et al., 2023; Kim et al., 2023).
To overcome this challenge, a balanced approach to having adequate usability enhancements alongside the integration of cutting-edge technologies is required. It ensures that such advanced systems remain intuitive and user-friendly. Enhanced collaboration tools are created when there is a balance of considering usability aspects and high technology adoption. Such tools offer intuitive interfaces and smooth functionality and facilitate the effective integration of cutting-edge technologies. This reduces cognitive load and increases user engagement, enabling real-time communication and decision-making. It makes the system ideal for complex industrial workflows. Appropriate consideration of both technological adoption and user-centric design principles should be aligned to achieve this balance. Balanced solutions are exemplified by digital-twin platforms integrated with IoT and collaborative XR, which combine real-time monitoring with intuitive dashboards to support predictive maintenance and teamwork (Q4: Balanced and Optimal Systems) (Ren et al., 2024; Oppermann et al., 2023; Wang Y. et al., 2024).
This analysis highlights the need to balance usability and technological advancements for optimal system performance. The goal should be to move towards the “Enhanced Collaboration Tools” quadrant, where advanced technology integration is balanced with high usability. This creates systems that are efficient, user-friendly, and suitable for collaborative tasks. This analysis provides actionable insights that encourage prioritising usability enhancements and technological advancements. This approach will also facilitate broader user adoption and improved UX.
6 Discussion
The findings from this study highlight the critical HSI-related issues and challenges of integrating metaverse technologies into industrial maintenance workflows. The proposed taxonomy offers a structured way to interpret these challenges, demonstrating how usability, UX, data management, accessibility, technological performance, environmental awareness, and trust-related factors collectively influence system adoption and effectiveness. A central insight from the analysis is UX as one of the most crucial factors in the adoption and effectiveness of the system. Many of the identified challenges impact UX, either directly or indirectly. Poor usability and interface design, such as complex navigation or inconsistent layouts, lead to higher cognitive load and user fatigue. This impacts user engagement and overall satisfaction. Trust and transparency have also emerged as important factors because operators must rely on system assistance and recommendations during fault diagnosis and maintenance execution. When system outputs are unclear or hard to understand and trust, uncertainty and a lack of confidence can slow down the flow of work. These effects are particularly critical in maintenance contexts, where operators depend on timely, accurate, and interpretable system feedback to support decision-making in safety-critical environments.
However, looking at all the categories and supporting literature, it is evident that industrial maintenance demands a unique context for HSI to be considered. Maintenance workflows are often performed in dynamic, time-critical, and sometimes hazardous environments. Therefore, factors such as latency, system instability, and understanding of the environment around could have a major impact on the success of maintenance workflows. If these factors are not considered accordingly, they can disrupt the user’s interaction quality, further impacting UX. To enhance the effectiveness of the systems, they need to be easy and, if possible, enjoyable to use and intuitive, qualities that drive productivity and sustained adoption. These dynamics distinguish industrial maintenance from other sectors that use immersive technologies. While other industrial and non-industrial sectors, such as education, gaming, healthcare, or manufacturing, use immersive technologies like AR/VR mainly for training, design review, or user engagement, industrial maintenance requires instant system feedback, accurate digital-twin synchronisation, and accurate interactions since maintenance workflows can have direct operational and safety consequences. While immersion, learning effectiveness, and comfort are relevant in the maintenance domain, they are accompanied by operational and safety requirements that expand the complexity of interaction.
Overall, the taxonomy developed in this study provides a structured understanding of the multifaceted HSI challenges posed by industrial metaverse integration. As metaverse continues to expand and integrate more advanced technologies, the developed taxonomy, which accommodates the current trends related to the technology, may serve as a foundation for further exploration and research.
7 Limitations of the study
Despite its contributions, this study has some limitations that should be acknowledged. The review adopts a qualitative systematic approach based on thematic analysis of selected literature. Although descriptive quantitative elements, such as word clouds and category-level frequency counts, were used to support and contextualise the findings, the study does not include detailed quantitative or statistical analyses that could provide additional analytical depth. The scope of this study was focused on thematic identification and synthesis. Therefore, bibliometric or citation network analyses were not conducted. While such approaches could offer complementary insights into the evolution and structure of research themes within the industrial metaverse domain, they were considered beyond the focus of the present work and are suggested for future research.
Furthermore, while this review is based solely on published literature and does not include empirical analysis, the authors have conducted a separate study that investigates real-world experiences with metaverse-enabled and immersive technologies, including human-centric digital and AI technologies in industrial environments. This complementary work involved the development of a demonstrator and the collection of quantitative feedback from industry professionals. Readers interested in practical insights and user-centred evaluations can refer to this study (Khanna et al., 2025). In addition, thematic analysis inherently involves interpretive judgement. Although systematic procedures were followed to identify and categorise themes, some degree of subjectivity cannot be entirely eliminated. Finally, the review is limited to industrial maintenance applications of the metaverse. While this focus enables domain-specific insights, the findings may not be directly transferable to other industrial or non-industrial contexts without further adaptation.
8 Recommendations from this study
Based on the findings of this systematic review and the proposed taxonomy, some recommendations can be derived for researchers, system designers, and industry practitioners. These recommendations can be helpful for people involved in the development and deployment of metaverse technologies for industrial maintenance. In particular, UX should be treated as a core design objective throughout the system development lifecycle. This analysis highlights that many HSI challenges directly or indirectly influence UX, and early and continuous user involvement, iterative usability testing, and applying human-centred design principles are essential to ensure intuitive and effective system interactions. Transparency and explainability are particularly important in metaverse-enabled maintenance systems that incorporate AI-driven recommendations or decision-support mechanisms. Providing clear explanations of system outputs and underlying decision logic can enhance user trust and reduce uncertainty in safety-critical maintenance scenarios. From a technical perspective, system design should prioritise interoperability, real-time data integration, and overall technological performance. Robust data management frameworks, low-latency communication, and compatibility with legacy systems are necessary to support reliable maintenance workflows and minimise disruptions during operation. Finally, the proposed taxonomy serves as a mechanism for applying these recommendations by offering a structured evaluation and design tool. It enables researchers and practitioners to systematically map system features against key HSI dimensions, identify where UX-, trust-, or performance-related gaps exist, and make informed design or improvement decisions. In this way, the taxonomy helps translate high-level recommendations into actionable guidance tailored to industrial maintenance contexts.
9 Conclusion and future work
This study demonstrates that the successful development and implementation of the metaverse in industrial maintenance contexts depends on addressing critical HSI challenges. While immersive technologies have been widely explored in domains such as gaming, healthcare, and education, the findings of this review highlight their substantial and distinct potential within industrial maintenance. In particular, metaverse-enabled systems offer opportunities for real-time monitoring, collaboration, and remote intervention. However, its effectiveness is strongly influenced by HSI-related barriers.
The primary contribution of this work is the development of a taxonomy that categorises HSI challenges into seven interrelated areas: usability and interface design, data management, accessibility, UX, technological performance, environmental and contextual awareness, and trust and transparency. Across these categories, UX emerges as a central factor, as many of the identified challenges directly or indirectly shape how users interact with and adopt metaverse systems. These findings emphasise the importance of prioritising human-centred design principles alongside technological advancement when deploying immersive systems in industrial maintenance environments.
Future research would prioritise refining these UX aspects by focusing on interrelated factors, including trust, explainability, and usability. In particular, future work could explore.
• Immersive technologies and how their design influences usability in complex industrial maintenance tasks.
• Integrating and evaluating multi-modal interaction features, such as speech and haptic feedback, to assess their effectiveness in supporting maintenance activities and reducing operator workload.
• Studying explainability and feedback mechanisms to enhance trust in metaverse-assisted maintenance systems.
• Exploring context-aware AI assistants that adapt support based on task and user context.
Enhancing these elements holistically will strengthen user engagement and promote wider adoption of the industrial metaverse across complex industrial environments.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
PK: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing. RK: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing – review and editing. PT: Supervision, Writing – review and editing, Conceptualization.
Funding
The author(s) declared that financial support was received for this work and/or its publication. The authors gratefully acknowledge the European Commission for supporting the Marie Sklodowska Curie program through the H2020 ETN MOIRA project (GA 955681).
Acknowledgements
The authors also wish to express their sincere gratitude to Dr. Ravdeep Kour, Senior Lecturer, Operation and Maintenance Engineering, Luleå University of Technology, Sweden, for her valuable guidance and insights that contributed to the development of this work.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was used in the creation of this manuscript. Generative AI was used for language editing and framing. Specifically, Grammarly (current version: 2025.10) and ChatGPT (OpenAI, GPT-5, September 2025 version) were used to improve clarity, grammar, and readability. No generative AI tools were used to generate scientific content, data, or references.
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Keywords: human-system interaction (HSI), immersive technologies, maintenance, metaverse, telepresence, user experience (UX)
Citation: Khanna P, Karim R and Tretten P (2026) Taxonomy of human-system interaction challenges for metaverse integration in industrial maintenance. Front. Virtual Real. 7:1718280. doi: 10.3389/frvir.2026.1718280
Received: 03 October 2025; Accepted: 09 January 2026;
Published: 29 January 2026.
Edited by:
Salvatore Livatino, University of Hertfordshire, United KingdomReviewed by:
Jenny Coenen, The Hague University of Applied Sciences, NetherlandsMuhammad Umer, University of West Florida, United States
Copyright © 2026 Khanna, Karim and Tretten. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Parul Khanna, cGFydWwua2hhbm5hQGx0dS5zZQ==