## 2 Materials and Methods  
This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [52] guidelines to report the identification and processing of the documents which comprised the document collection examined in this study. Specifically, this approach was followed due to its rigor, to provide transparency in the processing steps, and to increase the reproducibility of the data generated. Additionally, the study followed specific guidelines and principles defined in the literature to effectively carry out a systematic content analysis and to conduct an in-depth bibliometric analysis and scientific mapping of the existing documents [53; 54; 55]. This approach is deemed suitable to thoroughly examine current topics [56]. The content analysis was manually carried out, but for the bibliometric analysis and mapping of the existing literature, specialized tools, such as Bibliometrix [55] and VOSviewer [57], were used. To obtain a better understanding of the topics defined within the collection, topic modeling was conducted using Latent Dirichlet Allocation (LDA), which is a probabilistic Bayesian model characterized by its three-level hierarchical structure [58]. Furthermore, the document identification and collection processes targeted Scopus and Web of Science (WoS) databases due to their being widely used in similar studies, rigor, quality of documents, and their relevancy to the topic [59; 60]. Additionally, the specific databases were selected due to the data they generate being used in the tools adopted in this study (e.g., Bibliometricrix) without requiring any manual modification, thus further improving the reproducibility and transparency of the study. Therefore, the use of only two databases can be regarded as a limitation of this study. As the study focused on the use of extended reality technologies and intelligent tutoring systems, related keywords were identified and used to search for relevant to the topic documents. Specifically, the following query was used to identify relevant documents in Scopus and WoS: (\"intelligent tutoring system\" OR \"intelligent tutoring systems\" OR \"intelligent tutor\" OR \"intelligent tutors\") AND (\"AR\" OR \"VR\" OR \"augmented reality\" OR \"virtual reality\" OR \"metaverse\" OR \"extended reality\" OR \"mixed reality\"). It should be noted that more specialized keywords were avoided to provide a more general and thorough representation of the literature without giving explicit directions. The final query ran on the title, abstract, and keywords of the documents in January 2025, so the related outcomes are representative of the documents published until December 2024. However, only documents that were published in English were considered. No more limitations were set when searching for documents. However, apart from the initial search for relevant documents, the inclusion criterion set for a study to be included was for it to involve the use of intelligent tutoring systems within extended reality technologies and the metaverse. To ensure that all relevant studies were included, no further exclusion criteria were specified. During the initial stages of the document eligibility assessment, emphasis was placed on the document title and abstract to determine whether they were in line with and met the inclusion criterion set. Afterwards, during the screening process, the full-text of the documents was examined to determine whether or not a study was relevant and met the inclusion criterion. Hence, studies that did not meet the specific requirement during the different stages of the eligibility assessment were deemed out of scope and were removed. The detailed process followed to identify and process the related documents to create the document collection that is examined in this study is presented in Figure 1. Initially, 403 relevant documents were identified (Scopus: 317 and WoS: 86), out of which 71 were duplicates and were removed.