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.

The construction industry is presently going through a transformation led by adopting digital technologies that leverage Artificial Intelligence (AI). These industrial AI solutions assist in various phases of the construction process, including planning, design, production and management.  In particular, the production phase offers unique potential for the integration of such AI-based solutions.  These AI-based solutions assist site managers, project engineers, coordinators and other key roles in making final decisions. To facilitate the decision-making process in the production phase of construction through a human-centric AI-based solution, it is important to understand the needs and challenges faced by the end users who interact with these AI-based solutions to enhance the effectiveness and usability of these systems. Without this understanding, the potential usage of these AI-based solutions may be limited. Hence, the purpose of this research study is to explore, identify and describe the key factors crucial for developing AI solutions in the construction industry. This study further identifies the correlation between these key factors. This was done by developing a demonstrator and collecting quantifiable feedback through a questionnaire targeting the end users, such as site managers and construction professionals. This research study will offer insights into developing and improving these industrial AI solutions, focusing on Human-System Interaction aspects to enhance decision support, usability, and overall AI solution adoption. 

The adoption of Augmented Reality (AR) is increasing to enhance Human-System Interaction (HSI) by creating immersive experiences that improve efficiency and safety in various industries. In industrial maintenance, traditional practices involve physical documentation and device interactions, which might disrupt the task, affect efficiency, and increase the cognitive load for the maintenance personnel. AR has the potential to support and enhance industrial maintenance processes in these aspects. Therefore, the purpose of this research is to study and explore how advanced technologies like Artificial Intelligence (AI), AR and speech processing can be integrated to support hands-free, real-time task logging and interaction in maintenance environments. This is done by developing a demonstrator for Microsoft HoloLens 2 using Unity, C#, Azure Cognitive Services, and Azure Functions, which enables speech-to-text transcription for hands-free maintenance support. Using Azure’s speech recognition, the demonstrator can achieve high transcription accuracy in an AR environment, facilitating natural interactions between users and the augmented environment. The study aims to explore the potential of AR to reduce cognitive load, streamline workflows, and improve safety by enhancing HSI for maintenance personnel in high-stakes environments.