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Review

Intelligent Virtual Reality and Augmented Reality Technologies: An Overview

by
Georgios Lampropoulos
1,2
1
Department of Applied Informatics, School of Information Sciences, University of Macedonia, 54636 Thessaloniki, Greece
2
Department of Education, School of Education, University of Nicosia, Nicosia 2417, Cyprus
Future Internet 2025, 17(2), 58; https://doi.org/10.3390/fi17020058 (registering DOI)
Submission received: 31 December 2024 / Revised: 18 January 2025 / Accepted: 26 January 2025 / Published: 2 February 2025
(This article belongs to the Special Issue Virtual Reality and Metaverse: Impact on the Digital Transformation of Society II)
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Versions Notes

Abstract

:
The research into artificial intelligence (AI), the metaverse, and extended reality (XR) technologies, such as augmented reality (AR), virtual reality (VR), and mixed reality (MR), has been expanding over the recent years. This study aims to provide an overview regarding the combination of AI with XR technologies and the metaverse through the examination of 880 articles using different approaches. The field has experienced a 91.29% increase in its annual growth rate, and although it is still in its infancy, the outcomes of this study highlight the potential of these technologies to be effectively combined and applied in various domains transforming and enriching them. Through content analysis and topic modeling, the main topics and areas in which this combination is mostly being researched and applied are as follows: (1) “Education/Learning/Training”, (2) “Healthcare and Medicine”, (3) “Generative artificial intelligence/Large language models”, (4) “Virtual worlds/Virtual avatars/Virtual assistants”, (5) “Human-computer interaction”, (6) “Machine learning/Deep learning/Neural networks”, (7) “Communication networks”, (8) “Industry”, (9) “Manufacturing”, (10) “E-commerce”, (11) “Entertainment”, (12) “Smart cities”, and (13) “New technologies” (e.g., digital twins, blockchain, internet of things, etc.). The study explores the documents through various dimensions and concludes by presenting the existing limitations, identifying key challenges, and providing suggestions for future research.

1. Introduction

Artificial intelligence (AI) is rapidly advancing as a field of study and due to its wide applicability and potentials, it is rapidly being integrated into different domains. AI refers to smart systems that simulate human intelligence and mimic the way they think, communicate, and act [1,2,3] as the development of these systems is driven by the human nervous system and humans’ innate ability to learn, adapt, and reason [4,5,6]. Through the use of AI, intelligent systems [7,8,9], virtual agents and assistants [10,11,12], and multi-agent systems [13,14,15] can be created. Recent literature review studies have explored its use in various domains, such as education [16,17,18], industry [19,20,21], healthcare [22,23,24], business [25,26,27], smart cities [28,29,30], etc. The outcomes of these studies highlight the potential of AI to transform and enrich various sectors, which, in turn, reveals the need to further explore its capabilities to be used in combination with other novel technologies to further amplify its impact.
Immersive technologies can be greatly influenced and improved through the integration of AI. Recent studies have highlighted the benefits that this combination can potentially yield [10,11,31,32]. Specifically, emphasis is being placed on the use of AI within augmented reality (AR), virtual reality (VR), and mixed reality (MR) environments. AR focuses on embedding interactive digital information and content in users’ physical environment [33,34] and is closer to the real world in the “reality-virtuality continuum” [35] while VR focuses on virtual environments that fully engulf and immerse users [36,37,38], thus separating them from the real environment and, as a result, it is closer to the virtual environment in the continuum. Additionally, the metaverse, which is characterized by its realistic virtual experiences and environments that constitute an extension of the real environment [39,40,41], is closely related to XR technologies and the creation of virtual worlds and environments with high levels of embodiment, interactivity, and persistence [42,43]. As these technologies create new ways for users to interact, communicate, and experience events, they are increasingly being used in various settings and domains including education [44,45,46,47], industry [48,49,50], healthcare [51,52,53], business [54,55,56], smart cities [57,58,59]. The studies highlighted the role of VR and AR in each domain and the benefits they can yield. The domains, although indicative, were selected to highlight the similarities in terms of application domains among AI, AR, and VR.
The outcomes of the recent studies have revealed the positive impact that they can have in different domains. Hence, studies have also started to examine their combined use. However, although these technologies constitute established fields of studies on their own, their inter-relationship has yet to be examined in detail. As a result, there has not been any study that has examined the current state of the art regarding the use of AI within VR and AR environments and the metaverse. Examining the use of AI within extended reality (XR) environments can bring about new use cases as well as new opportunities. Additionally, by integrating AI, user-tracking, monitoring, and data processing can be improved and content and activities recommendation can be enhanced. Through this approach, more adaptive and personalized experiences, unique to each individual, can be created within immersive and interactive environments. Hence, it is vital to examine the convergence of these technologies. As this field of study is advancing, it is important to have a representation and mapping of the existing literature to identify emerging thematic areas and topics, limitations and challenges, and future research areas. Therefore, to bridge this gap, the aim of this study is to provide an overview and mapping of the existing literature about the convergence of AI with VR and AR technologies as well as to reveal future research directions. The main contributions of this study are the in-depth analysis of the document characteristics, the definition of the more advanced research domains and of the emerging ones, the identification of the most widely explored topics, themes, and trends, and the provision of future research areas while considering the challenges presented in the literature. To provide a thorough, valid, and reproducible analysis, the study follows the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) [60] framework to report the document identification, processing, and selection and utilized widely accepted tools and approaches, such as Bibliometrix [61], VOSviewer [62], and topic modeling through Latent Dirichlet Allocation (LDA) [63]. The study structure is as follows: the main methods and materials used are presented in Section 2. The analysis of the document collection is presented in Section 3 and in Section 4, the findings are further discussed and summarized. In Section 5, the conclusions of this study are presented, the implications are highlighted, the limitations are detailed, and future research directions are suggested.

2. Materials and Methods

As the study strives to explore the use of AI and XR technologies from a general perspective without being limited to a specific domain, a bibliometric analysis, scientific mapping, and content analysis approach was followed to present the state of the art. This approach is deemed suitable to examine similar topics with broad reach [64]. Moreover, to ensure an accurate, valid, and reproducible analysis of the literature, the study followed the PRISMA statement [60] as well as clearly defined guidelines presented in the literature [61,65].
Furthermore, the study used different approaches and tools to analyze the related studies. Specifically, the open-source tool Bibliometrix along with the related method defined by Aria and Cuccurullo [61] were used to carry out the bibliometric and scientific mapping of the literature. To further examine the related documents and their networks, VOSviewer was also used [62]. To identify the most prominent topics discussed within the document, topic modeling through the use of LDA [63] was conducted. The tools used are being widely adopted by similar studies which highlights their suitability and effectiveness. Additionally, the use of different tools and approaches enabled a more thorough representation of the state of the art.

2.1. Systematic Literature Review Process

Taking the findings of recent studies [66,67] into account, Scopus and Web of Science were selected as the main data sources to identify studies relevant to the topic due to their being highly regarded, containing impactful documents, and being used in other literature review and bibliometric analysis studies. Another reason for the selection of these databases was the ability to use the extracted information with the aforementioned tools [61,62].
Moreover, different combinations of keywords were tested to ensure that the most relevant documents were identified. The final query defined and used was the following: (“augmented reality” OR “AR” OR “virtual reality” OR “VR” OR “mixed reality” OR “MR” OR “extended reality” OR “XR” OR “metaverse”) AND (“artificial intelligence” or “AI”). It should be noted that although the abbreviations might identify some documents that are not relevant (e.g., MR can also be magnetic resonance, etc.), it was deemed appropriate for them to be used to avoid missing any potentially relevant documents. As a result, during the initial screening process, several documents were deemed to be out of scope. Additionally, as the aim of this study was to provide an overview of the topic, specialized keywords that could restrict the search to specific domains or provide explicit directions were not used. In this sense, the document collection would contain a larger number of documents but would also sufficiently provide a general representation of the current literature.
The final search for relevant documents using the aforementioned query was conducted on Scopus and Web of Science in December 2024 to identify suitable studies based on their title and abstract. In this study, only documents written in English were included. Additionally, to ensure that the most up-to-date research is being reported, the analysis involves studies that were published in the last decade, that is, 2015–2024. Following the guidelines specified within the PRISMA framework, the steps taken to search, identify, and process the related documents are presented in Figure 1.
Initially, the document collection comprised 12,281 documents with 7983 documents retrieved through Scopus and 4298 retrieved through Web of Science. The documents were then examined to identify duplicate documents using automatic and manual approaches. In total, 3533 duplicate documents were identified and removed from the document collection. As a result, the document collection consisted of 8748 documents before the initial screening which was on the existence of keywords within the title and abstract of the documents. Additionally, in order for a study to be included in the analysis, the inclusion criterion that had to be met was for it to directly focus on AI and VR and/or AR or on their combination from a theoretical or experimental perspective. Hence, studies that focused on one of these technologies or simply mentioned these terms but did not focus on their use or combination were excluded. From this process, a total of 7651 documents were removed. The remaining 1097 documents were manually examined to determine their suitability. Specifically, 65 documents were removed as they were outside the scope of this study, 41 documents were removed since they were letters, notes, and abstracts only, 38 because they were editorials, 28 because they were proceedings, 27 because they were retracted documents, 10 because they were books, 5 because they were book reviews, and finally, 3 documents were removed because they were erratum/corrections. Consequently, a total of 880 documents were included and analyzed in this study.

3. Result Analysis

Various dimensions of the document collection were analyzed to map the state of the art regarding the combination of AI with AR and VR technologies in education. Initially, the details of the document collection are presented. The publication frequency and the annual citation distribution are presented. The study also looks into the authors’ affiliation and countries and focuses on identifying the collaboration among the different countries. The relevant documents that have received the largest number of citations were also identified. Using keywords plus and author keywords, the trends of the topic, its thematic map, and its thematic evolution were also examined. To identify more topics, the documents were clustered using both Bibliometrix and VOSviewer to carry out a keyword-based co-occurrence analysis. To further examine the topics, LDA [63] was used to carry out a topic modeling analysis of the document collection regarding the use of AI and XR technologies in education. The related outcomes of the LDA topic modeling are presented and discussed in the discussion section.

3.1. Analysis of the Document Collection

Following the aforementioned methodology and process, a document collection comprising 880 documents was created. Table 1 presents the main information of the document collection as well as details regarding the document types, authors, the authors collaboration, and the document contents. Specifically, the documents were published in 622 different sources from 2015 to 2024 with most documents being conference/proceedings papers (n = 397, 45.1%), followed by journal articles (n = 322, 36.6%). In total, 112 documents (12.7%) were published as book chapters within edited book collections and 49 documents (5.6%) were classified as review studies. The novelty and significance of the topic is highlighted by the extremely high annual growth rate of 91.28% in scientific production. The average document age was 1.36 years and each document received 6.67 citations on average. Moreover, a total of 2938 authors from 71 countries were involved in the publication of the related documents. Out of the 880 documents, 127 were single-authored documents (14.4) written by 119 different authors and the remaining documents had 4.1 co-authors on average. The international co-authorship rate was 15.0% which showcased the global interest in this emerging field of study and the fact that collaborations among researchers and institutions on a global scale have already started being established despite the recency of the topic.

3.2. Growth Trends in Publications and Citations

Figure 2 illustrates the sharp increase in publications from 2022 onwards, reflecting the rapid growth of interest in the field. Specifically, most documents were published in 2024 (n = 343, 39.0%), in 2023 (n = 246, 28.0%), and in 2022 (n = 119, 13.5%). Additionally, three main time periods can be observed: (1st) Initial conceptualization years: 2015–2018 in which 25 documents (2.8%) were published; (2nd) Materialization years: 2019–2021 in which 147 documents (16.7%) were published; and (3rd) Breakthrough years: 2022–2024 in which 708 documents (80.5%) were published. The outcomes are representative of the advancements in the respective fields that took place in recent years and the increasing interest in these fields. In addition to the annual number of published documents, the citable years and the mean total citations received per year were also explored. The related data is presented in Table 2.
The outcomes are in line with the advancements in the respective fields in recent years. When considering the applicability of these technologies and their potential to enrich and transform the educational process, the interest in the topic is expected to continue increasing. Moreover, the citations that the documents published in each year received were also explored as can be seen in Table 2, which depicts the year, the mean total citations per document (MeanTCperDoc), the number of published documents, the mean total citations per year (MeanTCperYear), and the citable years (CitableYears). Based on the outcomes, documents from 2022 had the highest average citations per year (MeanTCperYear = 5.52), reflecting their impact despite being recent. Additionally, documents published in 2021 (MeanTCperYear = 4.06) and in 2019 (MeanTCperYear = 3.11) also presented high mean total citations per year. Nonetheless, given the increasing interest in the field, the average document age (1.36 years), and the citable years of the documents, it is expected that these outcomes will change in the future. This outcome is further validated when considering the number of documents published in 2023 and 2024, their citable years, 2 and 1 years, respectively, and their existing mean total citations per year.

3.3. Sources Analysis

To identify the most frequently used outlets and their type (e.g., journal, edited book, conference/proceedings), the total number of documents published in each outlet was considered. Most of the 880 documents were published within conferences and proceedings, followed by journals, and edited books. However, to better comprehend their relevancy, Bradford’s law was applied which, in turn, resulted in the creation of three clusters with the sources in Cluster 1 being the most relevant ones. Specifically, Cluster 1 consisted of 67 sources (10.8%) in which 291 documents (33.0%) were published, Cluster 2 had 265 sources (42.6%) in which 299 documents (34.0%) were published, and Cluster 3 had 290 sources in which 290 documents (33.0%) were published.
Table 3 presents the top 10 sources of Cluster 1, based on Bradford’s law ranking. The top five sources in which most documents were published were as follows: “Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)”, “Springer Series on Cultural Computing”, “ACM International Conference Proceeding Series (ICPS)”, “Applied Sciences”, and “IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)”. It is worth noting that 3 sources had published 6 documents each, 5 sources had 5 documents each, 11 sources had 4 documents each, and 17 sources had 3 documents each. Additionally, when looking at the h-index of the sources based on the documents contained within the collection, the top four sources were as follows: “Applied Sciences”, “IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)”, “Journal of Physics: Conference Series”, and “Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)” (Table 4). The mixture of journals, proceedings, and edited collections among the top sources in both cases highlights the interdisciplinarity of the field and it being actively researched.

3.4. Authorship Patterns

Furthermore, the distribution of authors based on the number of documents to which they contributed was examined through the use of Lotka’s law. The related outcomes are presented in Table 5 and are visualized in Figure 3. Specifically, 77.9% of the authors (n = 2502) contributed to one document while 11.5% (n = 309) contributed to two documents. Additionally, it can be observed that there are researchers who actively research this field of study that have contributed to five or more related documents over the period of 2015–2024.

3.5. Author’s Countries and Affiliations Analysis

When considering the global interest in the topic and the international collaborations that materialized, it was deemed appropriate to examine the country whose authors most actively explore this field of study. The corresponding author’s or the first author’s (in case there was no corresponding author) country was considered to identify the countries whose authors have contributed to the most documents. In total, authors from 71 countries contributed to the creation and publication of the 880 documents. The related outcomes are presented in Table 6. It should be noted that SCP refers to the intra-country collaborations while MCP refers to inter-country collaborations. Based on the outcomes, China (n = 176), the United States (n = 132), India (n = 98), Italy (n = 46), and the United Kingdom (n = 34) were the top five countries based on their scientific production. China and the United States had the highest SCP among the 71 countries while the MCP value was the highest in the case of China (n = 24), followed by Singapore (n = 11). Nonetheless, the presence of countries from different continents further highlights the importance of the topic.
Besides the number of documents published, the citations received were also explored to better understand the impact of the published work. As it can be seen in Table 7, the United States has received the most citations (n = 1335) having received on average 10.1 citations per document, followed by China with 1209 total citations and 6.9 citations on average per document. It should be noted that among the countries with the most total citations, when considering the average document citations, Vietnam (82), Singapore (34.8), and France (21.2) had the highest number. However, to better assess the outcomes the overall number of documents published from each country should also be considered. For example, Vietnam had published 2 documents, while Singapore had 16 and France 6. Hence, it can be concluded that overall, the documents published by Singapore have been the most impactful ones when considering only the average citations received per document.
Furthermore, the country collaborations were also explored. In total, six clusters arose that reveal the joint efforts toward further advancing the field. It is particularly important to note that, in many cases, collaborations between authors from different continents materialized. The related outcomes are presented in the collaboration network (Figure 4) and in the collaboration map (Figure 5). Additionally, as it can be observed, China and the United States have more actively engaged in establishing international collaborations.
Moreover, the authors’ affiliation was also examined. Particularly, the information of all authors whose affiliation details were retrieved from the databases and who contributed to a document was considered. Therefore, the total number of documents published by authors from a specific country may be smaller than the sum of contributions from various affiliations within that same country. Table 8 presented the top affiliations based on the number of documents they have published. “National University of Singapore”, Singapore (n = 30), “Nanyang Technological University”, Singapore (n = 18), “City University of Hong Kong”, Hong Kong (n = 16), “Harvard Medical School”, United States (n = 15), and “Sungkyunkwan University”, South Korea (n = 14) arose as the top five affiliations.

3.6. Thematic and Topic Analysis

Focusing on the keywords, the documents were further examined. Both keywords plus (indexed keywords) and author’s keywords were used since they both can effectively represent the knowledge structure of the document collection [68]. Specifically, the keywords were used to explore the co-occurrence network, the trend topics, the thematic map, and the thematic evolution of the topic. To aid in the creation of the related networks, both Bibliometrix and VOSviewer were used.
Initially, the frequency of the keywords used in the documents was examined. In addition, the frequency of the topic keywords and the most commonly used relevant keywords were also identified. Specifically, Table 9 presents the related outcomes for the keywords plus (indexed keywords) while Table 10 depicts the related data for the author’s keywords. “E-learning”, “machine learning”, “deep learning”, “immersive”, “students”, “human-computer interaction”, “learning systems”, “engineering education”, “blockchain”, and “education” were the most common keywords plus (indexed keywords) while “machine learning”, “generative artificial intelligence”, “deep learning”, “education”, “blockchain”, “human-computer interaction”, “digital twin”, “internet of things”, “computer vision”, “explainable artificial intelligence”, and “simulations” were the most frequently used author’s keywords. Based on the keywords identified, it can be inferred that the role of AI within XR environments is mostly focused on the educational domain. Additionally, its close relationship with machine learning and deep learning is observed. The convergence of AI with AR and VR is also examined within the context of virtual environments, the metaverse, and digital twins. Emphasis is also placed on key technologies such as blockchain, generative AI, and explainable AI. Finally, due to their immersive and interactive nature and human-centric design, increased focus is placed on the field of human-computer interaction. It should be noted that in both cases, it is revealed that VR is more frequently examined when compared to AR, the metaverse, and MR.
To better comprehend the relationships among the keywords, keyword co-occurrence networks were created using Bibliometrix and VOSviewer. The related networks are displayed in Figure 6 (Bibliometrix) and in Figure 7 (VOSviewer). It should be noted that to create the Bibliometrix-based network, keywords plus were used, as the outcomes were more representative, and to create the VOSViewer-based network, both keywords plus (indexed keywords) and author’s keywords were used to provide a more thorough representation. Additionally, the total link strength of the VOSviewer keyword co-occurrence network was also examined. The 15 keywords with the highest total link strength are presented in Table 11. “Artificial intelligence” (n = 457, total link strength = 1184), “virtual reality” (n = 338, total link strength = 951), “augmented reality” (n = 251, total link strength = 677), “metaverse” (n = 152, total link strength = 431), and “machine learning” (n = 77, total link strength = 268) were the top five keywords with the highest total link strength. It should be noted that in case a keyword existed in both keyword sets, it was counted only once to avoid any bias.
As it can be observed, a total of five clusters emerged in both networks. Table 12 summarizes the clusters and related keywords for the Bibliometrix-based network while Table 13 summarizes the clusters and related keywords for the VOSviewer-based network. The clusters that arose highlight the multidimensional role and wide applicability of combining AI with XR technologies.
Specifically, emphasis is being placed on the use of generative AI and the metaverse to aid teachers and learners as well as on the use of XR simulations to enrich medical and healthcare education. Additionally, there is a clear focus on the adoption and use of machine learning and deep learning methods. Education is revealed as one of the main domains in which their use is mostly examined due to their potential to offer immersive, personalized, and interactive learning experiences. Studies have also focused on adopting additional novel technologies and approaches including virtual agents and avatars, virtual worlds, big data, internet of things, generative AI, blockchain, etc. Particular emphasis is also placed on the design aspects of AI-enriched XR applications and on the importance of human-computer interaction is highlighted. Finally, their role in the industrial sector and security considerations are also increasingly being explored. The related outcomes are further discussed and analyzed in the discussion section.
Moreover, the keywords were used to examine the thematic evolution of the topic through the period of 2015–2024. Specifically, the following four time periods were specified: (i) 2015–2018, (ii) 2019–2020, (iii) 2021–2022, and (iv) 2023–2024. Given the limited number of documents published during 2015–2018, the specific time period was not divided any further. Based on the outcomes presented in Figure 8, the following themes arose: (i) “augmented reality” and “virtual reality” (2015–2018), (ii) “artificial intelligence”, “design”, “visualization”, “learning systems”, “robotics” (2019–2020), (iii) “virtual reality”, “machine learning”, “systems”, “algorithm”, “augmented reality”, “simulation”, “immersive”, “impact”, “internet of things”, “model”, “virtual worlds”, “surgery”, “real-world”, “management”, “object detection”, “brain”, and “avatar” (2021–2022), and (iv) “virtual reality”, “surgery”, “artificial intelligence”, “impact”, “education”, “big data”, “user interface”, “recognition”, “management”, “e-commerce”, “challenges”, and “performance” (2023–2024). According to the thematic evolution of the topic, the gradual increase in the variety of topics explored can be observed. This fact highlights the wide applicability and potential of using AI within VR and AR environments across different domains and use cases. These outcomes become more evident when considering the trend topics that arose, which can be seen in Figure 9. Specifically, the initial emphasis on machine learning, deep learning, and neural networks has shifted toward a focus on the technologies of AR and VR. Once again, the ability of this combination to be integrated into various domains and transform them is observed with the focus being on education and training, Industry 4.0, and smart cities. However, over the last years (2022–2024), an increasing interest in exploring the field of AI and capitalizing on the use of generative AI within XR environments is observed. Finally, emphasis is also put on further exploring the adoption and use of the metaverse.
Finally, the keywords were used to examine the thematic map of the topic and cluster the documents to identify potential areas for future research. Specifically, the thematic map of the topic focuses on identifying the main themes presented within the document collection and divides the themes into Niche, Motor, Basic, and Emerging or Declining themes. Based on the data presented in Figure 10, the following five themes arose: (i) the Niche theme was related to “education” and “training”, (ii) the Motor theme was related to the “metaverse”, “digital twins”, “blockchain”, and “virtual avatars”, (iii) the Basic themes were related to (a) “human-computer interaction” and (b) “artificial intelligence”, “extended reality technologies” (AR, VR, and MR), “machine learning”, and “deep learning”, and (iv) the Emerging or Declining theme was related to “generative artificial intelligence”. These outcomes are in line with the aforementioned results. When clustering the documents based on the keywords used, a total of five clusters emerged all with high impact and centrality. The first cluster was related to: “virtual environments” and “machine learning” approaches (e.g., adversarial machine learning, contrastive learning, federated learning, etc.). The second cluster was related to “augmented reality”, “mixed reality”, “deep learning”, “machine learning”, and “human-computer interaction”. These outcomes further highlight the importance of machine learning and deep learning in the realization of AI within XR environments and in achieving high and effective human-computer interaction. The third cluster was associated with “augmented reality”, “virtual reality”, “mixed reality”, “artificial intelligence”, and “machine learning”; thus, highlighting their inter-relationship. The fourth cluster was related to “virtual reality”, “metaverse”, “artificial intelligence”, “e-learning”, and “students”; thus, highlighting the focus on the educational domain and the potential benefits that this combination can yield. The fifth cluster was related to the “metaverse”, “machine learning”, “blockchain”, “non-fungible tokens”, and “artificial general intelligence” which highlights the future trends in the field of virtual worlds and virtual communities.

4. Discussion

AI as well as VR and AR are increasingly being used in different sectors, yielding significant benefits and transforming them. XR technologies offer immersive, engaging, and interactive experiences [47,69,70]. However, these experiences should be carefully designed following appropriate guidelines and principles [71,72,73,74,75,76]. Studies have explored the use of AR and VR in different domains and use cases while reporting positive outcomes [77,78]. Simultaneously, AI is rapidly advancing and it is being integrated into various domains and aspects of everyday life [79,80]. Due to their nature and capabilities, these technologies can complement and enrich each other both in terms of functionality and capabilities [32].
This study focused on examining the existing literature to identify the role and integration of AI within VR and AR environments. Specifically, the study analyzed 880 documents relevant documents that were identified following the PRISMA guidelines. The related data was analyzed using content analysis, bibliometric analysis, and scientific mapping techniques. Additionally, the data is further explored through LDA as shown below. The documents had a significantly high annual growth rate (91.29%) and an average document age of 1.36 years highlighting the recency of the topic and the increased interest in further advancing this field of study. Additionally, the documents examined were written by 2938 authors and published in 622 different sources during the time period 2015–2024. Most documents were published as conference/proceedings papers, followed by journal articles. Additionally, the documents on average had 4.1 co-authors and an international co-authorship rate of 15.0%; thus, highlighting the multidisciplinary nature of the field and the need for global collaboration to further advance it.
Furthermore, most documents were published in the last three years with 2024 being the year with the most published documents, followed by 2023 and 2022. Based on the number of published documents, the 10-year time period examined was divided into three separate periods: 2015–2018: Initial conceptualization; 2019–2021: Materialization; and 2022–2024: Breakthrough. Additionally, the documents which received the highest mean total citations were published in 2019, 2022, and 2021, although this outcome is expected to change given the rapid development of the field and the increase in the number of new documents published. The sources in which the documents were published were categorized into three clusters following Bradford’s law and also analyzed based on their h-index. According to the related outcomes, the most relevant sources were identified.
Moreover, using Lotka’s law, the distribution of the written documents which the authors have contributed to is presented. Despite the vast majority having participated in a single document, there are authors who are actively pursuing this novel field of study and it is expected that these outcomes will also change in the near future. The authors were from 71 different countries across the globe and countries from different continents ranked among the top in terms of scientific production in the field. Similarly, the author affiliations were examined. The related outcomes highlighted the most productive and relevant countries. The development of international collaborations, which were categorized into six clusters, further highlight the diverse and complicated nature of the field and the need to examine it from multiple perspectives and incorporate the insights of authors from various backgrounds and expertise.
By examining both author’s keywords and keywords plus (indexed keywords) of the documents, the thematic areas and main topics covered were examined. The results revealed the close relationship of AI, AR, and VR with the field of education and healthcare and also highlighted their inter-relation and their close relationship with other novel technologies. Particular emphasis was also put on human-computer interaction and the application of machine learning and deep learning. To better comprehend these topics, LDA, which is a probabilistic Bayesian model with a three-level hierarchical structure [63], was also used to identify topics within the document collection based on the title and abstract of the documents. Hence, using LDA, the following general topics and categories of interest emerged: “Education/Learning/Training”, “Healthcare and Medicine”, “Generative artificial intelligence/Large language models”, “Virtual worlds/Virtual avatars/Virtual assistants”, “Human-computer interaction”, “Machine learning/Deep learning/Neural networks”, “Communication networks”, “Industry”, “Manufacturing”, “E-commerce”, “Entertainment”, “Smart cities”, and “New technologies” (e.g., digital twins, blockchain, internet of things, etc.). These outcomes are in line with the results of the keywords and trends analysis and further validate the topics/areas identified.
Furthermore, focusing on the total citations received within the document collection, the top documents relevant to the topic that explore the use of AI along with VR and/or AR were identified. The related outcomes are presented in Table 14 and are analyzed to provide an overview of the most impactful studies that currently guide this field of study.
Hwan and Chien [81] explored the metaverse through the lenses of AI. Their study went over the potential research issues, role, and definition of the metaverse and the role of AI within the metaverse. The study highlighted the potentials of the AI-enriched metaverse to support and improve the educational process. Additionally, it offered future research topics and directions and commented upon the wider use of the metaverse in the near future. Wen et al. [82] focused on VR space and the use of AI to improve sign language recognition to enable bidirectional communication using haptic devices. In their study, they used a deep learning model for the recognition and translation of the sign language. Their outcomes revealed the significant benefits that can be yielded when integrating AI within VR environments to improve everyday life and communication. Zhang et al. [83] focused on the transition from AR and VR to the realization of digital twins using AI sensing technologies in the context of the internet of things. The study commented upon the role of AR, VR, and digital twins and highlighted the ability of using AI to design effective intelligent sensor systems. Finally, they pointed out the ability of AI to optimize processes and improve automation and of the metaverse and digital twins to bring about new opportunities for achieving a smarter future and commented on the existing challenges.
In another study, Yang et al. [84] examined the combination of AI and blockchain with the metaverse. The study focused on the unique characteristics and aspects of the metaverse and how they can be enhanced by using AI. The study also went over the use of blockchain and its applicability within the metaverse. Moreover, it presented key challenges and open issues related to digital economies, technological limitations, governance, regulations, as well as security and privacy. Finally, the study highlighted the important role that both AI and blockchain will play in the creation of an ever-expanding metaverse. Huynh-The et al. [85] carried out an in-depth survey regarding the use of AI within the metaverse. The study went over the categorization of the different AI types, its role in the metaverse, as well as the technical aspects in which its integration can aid with, such as natural language processing, computer vision, blockchain, digital twins, neural interfaces, and networking. Additionally, it explored various application domains, such as healthcare, manufacturing, smart cities, and gaming while also commenting on its potential use in e-commerce, real estate, and decentralized finance.
Chen et al. [86] explored the integration of AI within AR microscopes for cancer diagnosis. The study focused on presenting the proposed platform which capitalizes on AR for effective representation and interactivity and on AI for identification. Overall, the study highlights the potential that the combination of these technologies can yield in the field of healthcare. Mozumder et al. [87] provided an overview regarding the future trends of the metaverse focusing on AI, internet of things, and blockchain. Their work focused on the medical domain and commented upon the virtual environments and worlds that can be created within the metaverse. Additionally, the study highlighted the technologies which the metaverse uses and explored AI use cases within the metaverse as well as the use of the metaverse in healthcare. Winkler-Schwartz et al. [88] focused on VR simulations in the context of assessing surgical expertise. Their approach emphasized machine learning and the role of AI in medical education. Specifically, they looked into how machine learning can be used in the context of VR simulations to evaluate users’ performances. The study also provides a general framework to effectively report and analyze studies that focus on machine learning and VR surgical simulations.
Sahu et al. [89] carried out a review regarding the use of AI within AR applications targeted at manufacturing. The study highlighted the benefits that AR can bring about and how AI can be used to further enrich AR applications. Specifically, the study focused on identifying the main concepts and the limitations of the existing methods and explored various AI-based approaches that could help address these challenges. The study also commented upon the benefits of AI in manufacturing and within AR-based applications. Chang et al. [90] explored 6G-enabled edge AI for the metaverse. Specifically, the study presented the main aspects of the metaverse and focused on the existing challenges that it faced. Additionally, the study looked into the limitations specified in the existing literature and provided future research directions. Holstein et al. [91] examined a mixed reality teacher awareness tool in the context of AI-enhanced classrooms. Specifically, the study focused on intelligent tutoring systems and advanced analytics which were displayed in an MR headset. Their study revealed that the use of MR-based teacher analytics can help address the learning outcome gaps observed among students of different levels of knowledge and skills. Finally, the outcomes of the study highlighted the benefits that the AI systems can bring in education and the potential that the combination of integrating human and machine intelligence can have in supporting students’ learning.
The outcomes of the aforementioned studies reveal the potentials of integrating AI within AR and VR environments as well as the metaverse across different contexts. Moreover, they highlight the need to integrate and combine new technologies to meet the emerging requirements. Based on the scope of the studies, it can be inferred that emphasis is being placed on the role of AI within the metaverse as well as within XR environments in the education and healthcare domains. The sections and topics covered in the aforementioned studies are in line with the topics and areas identified within this study. Additionally, the gradual evolution and shift of focus is also in line with the thematic evolution presented in this study. Hence, the results of this study further validate those of the previous literature regarding the potentials of combining AI with XR technologies and the metaverse and highlights its ability to be effectively integrated into different domains.
However, it should be noted that there are several open challenges and barriers that need to be addressed before these technologies are more widely adopted and applied. These barriers involve privacy and security issues, ethical concerns, technical and computational limitations, algorithmic bias considerations, software and hardware limitations, sustainability and interoperability considerations, as well as development and adoption hurdles [41,92,93,94,95]. As these challenges exist for AI, the metaverse, and XR technologies, emphasis should be placed on exploring them through the lenses of each individual technology as well as of their combined use.

5. Conclusions

XR technologies are rapidly advancing and being integrated into various domains. Specifically, the adoption and use of AR and VR have brought about several benefits and new opportunities to different sectors including education, healthcare, industry, etc. Simultaneously, due to the recent advances, AI is also gaining ground and being integrated into several domains reinforcing them and enriching them. These technologies can be combined to yield even greater outcomes; hence, the research into this topic is rapidly increasing. This study aimed to provide an overview through the examination, analysis, and mapping of the existing literature regarding the use of AI within AR, VR, and the metaverse.
To provide a thorough overview, the study followed the PRISMA guidelines and used different analysis methods and tools. Specifically, the study focused on carrying out a bibliometric analysis, scientific mapping, content analysis, and topic modeling of the related literature. In total, the study examined 880 documents which were identified from Scopus and Web of Science and were published during 2015–2024. The study examined the main characteristics of the document collection and focused on identifying emerging and trend topics and areas of focus.
The results of this study highlighted the potential that the integration of AI into AR, VR, and the metaverse can yield. Additionally, it revealed its wide applicability and capabilities of being effectively integrated into various domains. The study also confirmed the significance and novelty of the topic which showcases a significantly high growth rate (91.29%). Additionally, the study revealed the main research areas and directions and highlighted the following topics as the ones being more actively researched: “Education/Learning/Training”, “Healthcare and Medicine”, “Generative artificial intelligence/Large language models”, “Virtual worlds/Virtual avatars/Virtual assistants”, “Human-computer interaction”, “Machine learning/Deep learning/Neural networks”, “Communication networks”, “Industry”, “Manufacturing”, “E-commerce”, “Entertainment”, “Smart cities”, and “New technologies” (e.g., digital twins, blockchain, internet of things, etc.).
However, the study has some limitations. Specifically, the documents identified were retrieved from two databases and only English documents were examined. Since the goal of this study was to provide a general overview of the field, a more in-depth content analysis targeted to a specific domain was not carried out. As a result, there is a clear need for future studies to further analyze the integration of AI, VR, and AR across different settings through systematic literature reviews and case studies. Additionally, effective frameworks, standards, and guidelines on how to develop relative solutions and integrate them should be created. Emphasis should also be placed on examining and addressing the challenges and barriers associated with the effective integration of AI within XR environments, such as technical, hardware, and software limitations, algorithmic bias considerations, security and privacy issues, ethical concerns, as well as development and adoption hurdles. There is also a need to create valid evaluation metrics to assess its effectiveness. Future studies should also examine security, privacy, and ethical aspects associated with the use of AI, XR technologies, and the metaverse. Finally, it is important to explore users’ involvement, interactions, communications, perspectives, behaviors, and emotions while they are engaged within AI-enabled AR and VR environments as well as within the metaverse.

Funding

This research received no external funding.

Data Availability Statement

All of the data are contained within the article. The data supporting the conclusions of this article will be made available by the corresponding author upon reasonable request.

Conflicts of Interest

The author declares no conflicts of interest.

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Figure 1. PRISMA flowchart.
Figure 1. PRISMA flowchart.
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Figure 2. Annual total number of published documents.
Figure 2. Annual total number of published documents.
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Figure 3. Lotka’s law analysis.
Figure 3. Lotka’s law analysis.
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Figure 4. Country collaboration network.
Figure 4. Country collaboration network.
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Figure 5. Country collaboration map.
Figure 5. Country collaboration map.
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Figure 6. Keyword co-occurrence network—Bibliometrix.
Figure 6. Keyword co-occurrence network—Bibliometrix.
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Figure 7. Keyword co-occurrence network—VOSviewer.
Figure 7. Keyword co-occurrence network—VOSviewer.
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Figure 8. Thematic evolution of the topic.
Figure 8. Thematic evolution of the topic.
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Figure 9. Trend topics based on keywords plus.
Figure 9. Trend topics based on keywords plus.
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Figure 10. Thematic map of the topic.
Figure 10. Thematic map of the topic.
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Table 1. Document collection details.
Table 1. Document collection details.
DescriptionResultsDescriptionResults
Main information about data Document types
Timespan2015:2024Journal article322
Sources (Journals, Books, etc.)622Book chapter112
Documents880Conference/Proceedings paper397
Annual Growth Rate %91.29Review49
Document Average Age1.36Authors
Average Citations per Document6.674Authors2938
References20,100Authors of single-authored documents119
Document contents Authors collaboration
Keywords Plus (ID)2868Single-authored documents127
Author’s Keywords (DE)2202Co-authors per document4.1
International co-authorships %15
Table 2. Annual scientific production and citations.
Table 2. Annual scientific production and citations.
YearMeanTCperDocNumber of Published DocumentsMeanTCperYearCitableYears
2015610.610
20172.450.38
20188.68191.247
201918.65343.116
20207.86431.575
202116.24704.064
202216.551195.523
20235.282462.642
20240.913430.911
Table 3. Top sources of cluster 1 based on Bradford’s law.
Table 3. Top sources of cluster 1 based on Bradford’s law.
SourceRankFreqcumFreqCluster
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)119191
Springer Series on Cultural Computing215341
ACM International Conference Proceeding Series (ICPS)313471
Applied Sciences413601
IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)510701
IEEE Access69791
IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)78871
Analysis and Metaphysics88951
Applied Mathematics and Nonlinear Sciences981031
CEUR Workshop Proceedings1081111
Table 4. Most impactful sources based on h-index.
Table 4. Most impactful sources based on h-index.
Sourcesh-Indexg-Indexm-IndexTCNPPY_Start
Applied Sciences5101.25104132021
IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)570.71450102018
Journal of Physics: Conference Series440.82362020
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)460.849192020
Table 5. Distribution of authors based on the number of documents written.
Table 5. Distribution of authors based on the number of documents written.
Documents WrittenN. of AuthorsProportion of AuthorsDocuments WrittenN. of AuthorsProportion of Authors
125020.779670.002
23090.115750.002
3750.05810.0
4270.018920.0
5100.013
Table 6. Country document publication details.
Table 6. Country document publication details.
CountryDocumentsSCPMCPFreq.MCP_Ratio
China176152240.20.136
United States13212570.150.053
India989260.1110.061
Italy464150.0520.109
United Kingdom342860.0390.176
South Korea302460.0340.2
Australia262060.030.231
Canada251960.0280.24
Germany242220.0270.083
Spain181530.020.167
Table 7. Countries that received the most citations.
Table 7. Countries that received the most citations.
CountryTCAverage Document Citations
United States133510.1
China12096.9
Singapore55734.8
South Korea2949.8
Italy2585.6
Canada25510.2
India2432.5
Vietnam16482
Australia1626.2
France12721.2
Table 8. Most relevant affiliations based on the number of documents published.
Table 8. Most relevant affiliations based on the number of documents published.
CountryAffiliationNumber of ArticlesPercentage of Documents in the Collection
SingaporeNational University of Singapore303.4
SingaporeNanyang Technological University181.8
Hong KongCity University of Hong Kong161.7
United StatesHarvard Medical School151.6
South KoreaSungkyunkwan University141.5
CanadaMcGill University131.5
ItalyUniversità Politecnica delle Marche131.5
SlovakiaUniversity of Žilina131.5
United StatesUniversity of Central Florida131.4
United StatesCarnegie Mellon University121.4
Hong KongChinese University of Hong Kong122.0
Table 9. Most frequently used keywords plus (indexed keywords).
Table 9. Most frequently used keywords plus (indexed keywords).
Topic Keywords Relevant Keywords
WordsOccurrencesWordsOccurrences
“virtual reality”225“e-learning”53
“artificial intelligence”182“machine learning”44
“augmented reality”154“deep learning”37
“metaverse”57“immersive”37
“mixed reality”31“students”31
“extended reality”20“human-computer interaction”23
“learning systems”23
“engineering education”21
“blockchain”19
“education”18
Table 10. Most frequently used author’s keywords.
Table 10. Most frequently used author’s keywords.
Topic Keywords Relevant Keywords
WordsOccurrencesWordsOccurrences
“artificial intelligence”410“machine learning”65
“virtual reality”276“generative artificial intelligence”46
“augmented reality”211“deep learning”37
“metaverse”144“education”31
“extended reality”75“blockchain”27
“mixed reality”43“human-computer interaction”23
“digital twin”22
“internet of things”20
“computer vision”18
“explainable artificial intelligence”16
“simulations”16
Table 11. Total link strength of the keyword co-occurrence network—VOSviewer.
Table 11. Total link strength of the keyword co-occurrence network—VOSviewer.
KeywordsOccurrencesTotal Link StrengthKeywordsOccurrencesTotal Link Strength
“artificial intelligence”4571184“deep learning”51188
“virtual reality”338951“immersive”43177
“augmented reality”251677“education”51173
“metaverse”152431“digital twins”31142
“machine learning”77268“human-computer interaction”42139
“extended reality”75243“blockchain”32131
“e-learning”52223“students”32129
“mixed reality”51204
Table 12. Analysis of the keyword co-occurrence network—Bibliometrix.
Table 12. Analysis of the keyword co-occurrence network—Bibliometrix.
ClusterKeywords
Green cluster (n = 33)“virtual reality”, “artificial intelligence”, “augmented reality”, “e-learning”, “machine learning”, “deep learning”, “immersive”, “mixed reality”, “human-computer interaction”, “learning systems”, “engineering education”, “extended reality”, “blockchain”, “challenges”, “current”, “user interfaces”, “convolutional neural networks”, “virtual reality environments”, “management”, “virtual worlds”, “big data”, “decision-making”, “internet of things”, “three dimensional computer graphics”, “digital twin”, “learning algorithms”, “neural networks”, “sales”, “real-worlds”, “training systems”, “computer vision”, “data handling”, and “education computing”
Purple cluster (n = 5)“metaverse”, “students”, “generative artificial intelligence”, “visualization”, and “teaching”
Orange cluster (n = 4)“education”, “performance”, “surgery”, and “simulations”
Read cluster (n = 4)“virtual environments”, “adversarial machine learning”, “contrastive learning”, and “federated learning”
Blue cluster (n = 2)“systems” and “design”
Table 13. Analysis of the keyword co-occurrence network—VOSviewer.
Table 13. Analysis of the keyword co-occurrence network—VOSviewer.
ClusterKeywords
Red cluster (n = 21)“adversarial machine learning”, “computer vision”, “current”, “deep learning”, “e-learning”, “engineering education”, “generative adversarial network”, “generative artificial intelligence”, “human-computer interaction”, “immersive”, “immersive learning”, “learning algorithms”, “learning systems”, “students”, “teaching”, “three dimensional computer graphics”, “user interfaces”, “virtual environments”, “virtual reality”, “virtual reality environments”, and “visualization”
Green cluster (n = 16)“avatars”, “big data”, “blockchain”, “challenges”, “design”, “digital twin”, “explainable artificial”, “extended reality”, “gamification”, “healthcare”, “immersive technologies”, “internet of things”, “machine learning”, “metaverse”, “mixed reality”, and “security”
Blue cluster (n = 12)“artificial intelligence”, “augmented reality”, “education”, “framework”, “learning”, “management”, “performance”, “robotics”, “simulations”, “surgery”, “systems”, and “technology”
Yellow cluster (n = 3)“decision-making”, “industry 4”, and “training”
Purple cluster (n = 1)“virtual worlds”
Table 14. Documents with the highest number of citations.
Table 14. Documents with the highest number of citations.
DocumentDOITotal CitationsTotal Citations per YearNormalized Total Citations
[81]10.1016/j.caeai.2022.100082400133.3324.17
[82]10.1038/s41467-021-25637-w26265.516.13
[83]10.1002/aisy.20210022817357.6710.46
[84]10.1109/OJCS.2022.318824916655.3310.03
[85]10.1016/j.engappai.2022.1055811648231.03
[86]10.1038/s41591-019-0539-7150258.04
[87]10.23919/ICACT53585.2022.972880813043.337.86
[88]10.1016/j.jsurg.2019.05.01511519.176.17
[89]10.1080/00207543.2020.185963610526.256.46
[90]10.23919/jcin.2022.98151959732.335.86
[91]10.1007/978-3-319-93843-1_129713.7111.05
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Lampropoulos G. Intelligent Virtual Reality and Augmented Reality Technologies: An Overview. Future Internet. 2025; 17(2):58. https://doi.org/10.3390/fi17020058

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Lampropoulos, G. (2025). Intelligent Virtual Reality and Augmented Reality Technologies: An Overview. Future Internet, 17(2), 58. https://doi.org/10.3390/fi17020058

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