## 3 Systematic Review Methodology
### Eligibility Criteria  
To refine the selection of relevant studies from the results returned by the register and database searches, the following inclusion and exclusion criteria were established to ensure the relevance and quality of the papers included in this review.  
* **Inclusion Criteria**
* **IC1**: Studies published between 2020-2024
* **IC2**: Peer-reviewed articles
* **IC3**: Referring to at least two combined technologies
* **IC4**: Full text available in English
* **Exclusion Criteria**
* **EC1**: Review papers, editorials, or opinion pieces
* **EC2**: Grey literature (technical reports, white papers, blogs)
* **EC3**: Retracted publications
* **EC4**: Publications without DOI
* **EC5**: Duplicate publications of the same study
* **EC6**: Papers presenting single-technology implementations
* **EC7**: Papers presenting system architecture proposals without any prototype implementations
* **EC8**: Content with missing or unclear methodology.
* **EC9**: Papers without adequate proof of a system's existence or inadequate testing of the proof-of-concept implementation (Simulations with fabricated data are not considered adequate as well as testing with a significant focus on a single part of the system, e.g., AI model improvements within IoT or data transfer latency, etc.) are not considered. Papers that neither show images nor mention a testbed or experiment are also excluded. Only implementations or experiments that use the proposed system in its entirety are considered, regardless of scale, as long as it could be applied to real-life scenarios, even after further improvements.  
To ensure the high quality of the articles included in our systematic review, additional criteria beyond those mentioned above were introduced. As the aim was to cover a broad range of application fields where convergence of AI, IoT, and VR takes place, in cases where there is an abundance of findings in a particular application area only the top 3 articles based on their research impact will be selected. This impact has been assessed using the following criteria:  
* **Number of citations** to the paper.
* **Impact factor** of the journal published.
* **Clarity of the methodology** followed (e.g., tables provided, graphs, step by step details etc.)  
This decision was made after a comprehensive review of the eligible literature, as it revealed a disproportionate number of papers were connected to specific domains such as healthcare, Industry 4.0 and more. To address this potential bias and enhance the generalizability of our systematic review, certain papers were strategically excluded. This approach was designed to ensure a more balanced and comprehensive exploration of the synergistic potential between AI, IoT, and VR, thus improving the overall precision and objectivity of this research.  
To address the potential limitations of solely relying on Scopus and Web of Science sources, we conducted a supplementary non-systematic search in the databases of the Institute of Electrical and Electronics Engineers (IEEE) and the Association for Computing Machinery (ACM). From these sources, a total of 509 papers were retrieved from the IEEE Xplore database and 1113 papers from ACM Digital Library. From those, 6 articles (3 from ACM and 3 from IEEE) were selected based on their relevance to the previously mentioned inclusion criteria, while also taking into account their recency, impact, and alignment with the review's objectives. This approach ensures a more inclusive and balanced representation of the literature while maintaining a manageable scope.  
As the newly included works were not identified through the systematic review process (but through a supplementary, non-systematic search) they have not been incorporated in the following PRISMA flowchart to avoid inaccurately representing the originally employed systematic methodology. To maintain transparency, reproducibility as well as adherence to PRISMA guidelines the supplementary works have been treated as a complementary addition and are discussed separately.