## 4 Overview of Included Studies
Advancing Industry 4.0, the authors of [69] proposed a Human Collaborative Intelligence empowered Digital Twin framework (HCLINT-DT), integrating XR, AI, and IoT to advance collaborative intelligence in various fields. The framework uses natural language processing (NLP) technologies, such as speech-to-text and named entity recognition, to enable users to create and share annotations (textual, visual, vocal) that link real-world objects annotated via AR with their virtual counterparts within DTs. This facilitates knowledge preservation and collaborative learning. Real-world testing was conducted through a use case involving family photo albums, where participants interacted with both AR and VR interfaces to annotate and explore memories. DL models such as YOLOv5 were used for object detection, identifying photos within the user's view and retrieving associated annotations. An online survey showed satisfaction with automatic photo identification but mixed preferences for AR experiences over traditional albums which suggest areas for improvement. The frameworks use in industrial settings is exemplified through testing in an electrical systems production facility, where workers assembling switchboards used AR for real-time component recognition and annotation. This eliminated the need to move between physical switchboards and computers, improving efficiency and collaboration by displaying relevant information directly in the user's view. Some limitations that could be pointed out are the reliance on user engagement for annotations where AI could supplement knowledge in known areas. Implementing advanced technologies such as XR and crowd intelligence may introduce technical challenges while the scalability of this model is still in question, as the introduction of more and more annotations and data points might hinder the real-time performance. Recent research by [PERSON] et al. [PERSON] et al. (2017) extends previous work on DTs in Industry 4.0 by implementing and refining existing technology to create a DT system specifically for electric utilities, integrating AI, IoT, and VR into a four-layer model. The foundation layer comprises a global 3D modeling repository, legacy system integration, data universalization, and cybersecurity. The IoT-based Dynamic Devices and Sensors Layer enables real-time asset monitoring, while the AI layer transitions from corrective to predictive maintenance by automating tasks such as anomaly detection. The Human Interface layer uses AR and VR for remote assistance and training, supported by a centralized \"Universal Virtual Data Lake\" (UVDL) for data storage and analysis. Preservation of legacy systems is an advantage of this work as it is something that is usually overlooked. A real-world application was showcased using a Network Digital Twin (NDT) to create a 3D replica of a city's electrical grid, improving asset management and service quality. The system combines image processing, IoT-based real-time data collection, and point cloud technology for precise spatial data capture, allowing users to visualize and interact with electrical infrastructures. AI is used for predictive maintenance and grid management, while AR and VR improve training and field operations through the UVDL. A cost-benefit analysis revealed a 90% reduction in inspection hours, a 65% decrease in on-field time. Challenges faced included the integration of various technologies and the need for a cultural shift within the workforce to adapt to new digital tools. The authors skillfully managed to combine existing knowledge to prove the real-world applicability of such technologies, moving beyond the conceptual stage and into practical, real-life implementation--a stage at which many research works often remain. The use of VR to enrich the educational experience is not new. The synergistic capabilities of both AI, IoT and VR, on the other hand, have not been fully explored yet regarding the enhancement of the learning experience. Authors of [PERSON] et al.