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Citation: Lampropoulos, G.
Intelligent Virtual Reality and
Augmented Reality Technologies: An
Overview. Future Internet 2025,17, 58.
https://doi.org/10.3390/fi17020058
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Review
Intelligent Virtual Reality and Augmented Reality Technologies:
An Overview
Georgios Lampropoulos 1,2
1Department of Applied Informatics, School of Information Sciences, University of Macedonia,
54636 Thessaloniki, Greece; glampropoulos@uom.edu.gr
2Department of Education, School of Education, University of Nicosia, Nicosia 2417, Cyprus
Abstract: The research into artificial intelligence (AI), the metaverse, and extended real-
ity (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 com-
bined 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 mod-
els”, (4) “Virtual worlds/Virtual avatars/Virtual assistants”, (5) “Human-computer inter-
action”, (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 con-
cludes by presenting the existing limitations, identifying key challenges, and providing
suggestions for future research.
Keywords: artificial intelligence; AI; virtual reality; augmented reality; extended reality;
mixed reality; metaverse; review; bibliometric analysis; topic modeling; scientific mapping
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
–
3
] as the development of these systems is driven by the human
nervous system and humans’ innate ability to learn, adapt, and reason [
4
–
6
]. Through
the use of AI, intelligent systems [
7
–
9
], virtual agents and assistants [
10
–
12
], and multi-
agent systems [
13
–
15
] can be created. Recent literature review studies have explored its
use in various domains, such as education [
16
–
18
], industry [
19
–
21
], healthcare [
22
–
24
],
business [
25
–
27
], smart cities [
28
–
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 inte-
gration of AI. Recent studies have highlighted the benefits that this combination can
Future Internet 2025,17, 58 https://doi.org/10.3390/fi17020058
Future Internet 2025,17, 58 2 of 25
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 envi-
ronment [
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
–
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
–
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 edu-
cation [
44
–
47
], industry [
48
–
50
], healthcare [
51
–
53
], business [
54
–
56
], smart cities [
57
–
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 3and 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
Future Internet 2025,17, 58 3 of 25
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 fol-
lowed 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 ap-
propriate 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 con-
ducted 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
Future Internet 2025,17, 58 4 of 25
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 remain-
ing 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.
Future Internet 2025, 17, x FOR PEER REVIEW 4 of 25
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 docu-
ments were removed. The remaining 1097 documents were manually examined to deter-
mine their suitability. Specifically, 65 documents were removed as they were outside the
scope of this study, 41 documents were removed since they were leers, notes, and ab-
stracts 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.
Figure 1. PRISMA flowchart.
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 iden-
tified. 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 docu-
ments 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
Figure 1. PRISMA flowchart.
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
Future Internet 2025,17, 58 5 of 25
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 com-
prising 880 documents was created. Table 1presents the main information of the doc-
ument 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 documentsbeing 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 high-
lighted 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.
Table 1. Document collection details.
Description Results Description Results
Main information about data
Document types
Timespan 2015:2024 Journal article 322
Sources (Journals, Books, etc.)
622 Book chapter 112
Documents 880 Conference/Proceedings
paper 397
Annual Growth Rate % 91.29 Review 49
Document Average Age 1.36 Authors
Average Citations per
Document 6.674 Authors 2938
References 20,100 Authors of single-authored
documents 119
Document contents Authors collaboration
Keywords Plus (ID) 2868 Single-authored documents 127
Author’s Keywords (DE) 2202 Co-authors per document 4.1
International
co-authorships % 15
3.2. Growth Trends in Publications and Citations
Figure 2illustrates 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
Future Internet 2025,17, 58 6 of 25
citable years and the mean total citations received per year were also explored. The related
data is presented in Table 2.
Future Internet 2025, 17, x FOR PEER REVIEW 6 of 25
enrich and transform the educational process, the interest in the topic is expected to con-
tinue increasing. Moreover, the citations that the documents published in each year re-
ceived 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, docu-
ments published in 2021 (MeanTCperYear = 4.06) and in 2019 (MeanTCperYear = 3.11)
also presented high mean total citations per year. Nonetheless, given the increasing inter-
est in the field, the average document age (1.36 years), and the citable years of the docu-
ments, 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.
Figure 2. Annual total number of published documents.
Table 2. Annual scientific production and citations.
Year MeanTCperDoc Number of Published Documents MeanTCperYear CitableYears
2015 6 1 0.6 10
2017 2.4 5 0.3 8
2018 8.68 19 1.24 7
2019 18.65 34 3.11 6
2020 7.86 43 1.57 5
2021 16.24 70 4.06 4
2022 16.55 119 5.52 3
2023 5.28 246 2.64 2
2024 0.91 343 0.91 1
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 proceed-
ings, followed by journals, and edited books. However, to beer comprehend their
Figure 2. Annual total number of published documents.
Table 2. Annual scientific production and citations.
Year MeanTCperDoc
Number of
Published
Documents
MeanTCperYear
CitableYears
2015 6 1 0.6 10
2017 2.4 5 0.3 8
2018 8.68 19 1.24 7
2019 18.65 34 3.11 6
2020 7.86 43 1.57 5
2021 16.24 70 4.06 4
2022 16.55 119 5.52 3
2023 5.28 246 2.64 2
2024 0.91 343 0.91 1
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
Future Internet 2025,17, 58 7 of 25
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 con-
sidered. 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 3presents 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 Interna-
tional 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.
Table 3. Top sources of cluster 1 based on Bradford’s law.
Source Rank Freq cumFreq Cluster
Lecture Notes in Computer Science
(including subseries Lecture Notes
in Artificial Intelligence and
Lecture Notes in Bioinformatics)
1 19 19 1
Springer Series on Cultural
Computing 2 15 34 1
ACM International Conference
Proceeding Series (ICPS) 3 13 47 1
Applied Sciences 4 13 60 1
IEEE International Conference on
Artificial Intelligence and Virtual
Reality (AIVR)
5 10 70 1
IEEE Access 6 9 79 1
IEEE Conference on Virtual Reality
and 3D User Interfaces Abstracts
and Workshops (VRW)
7 8 87 1
Analysis and Metaphysics 8 8 95 1
Applied Mathematics and
Nonlinear Sciences 9 8 103 1
CEUR Workshop Proceedings 10 8 111 1
Future Internet 2025,17, 58 8 of 25
Table 4. Most impactful sources based on h-index.
Sources h-Index g-Index m-Index TC NP PY_Start
Applied Sciences 5 10 1.25 104 13 2021
IEEE International Conference on
Artificial Intelligence and Virtual
Reality (AIVR)
5 7 0.714 50 10 2018
Journal of Physics: Conference Series 4 4 0.8 23 6 2020
Lecture Notes in Computer Science
(including subseries Lecture Notes in
Artificial Intelligence and Lecture Notes
in Bioinformatics)
4 6 0.8 49 19 2020
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 5and 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.
Table 5. Distribution of authors based on the number of documents written.
Documents
Written
N. of
Authors
Proportion
of Authors
Documents
Written
N. of
Authors
Proportion
of Authors
1 2502 0.779 6 7 0.002
2 309 0.115 7 5 0.002
3 75 0.05 8 1 0.0
4 27 0.018 9 2 0.0
5 10 0.013
Future Internet 2025, 17, x FOR PEER REVIEW 8 of 25
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.
Table 5. Distribution of authors based on the number of documents wrien.
Documents Written N. of Authors Proportion of Authors Documents Written N. of Authors Proportion of Authors
1 2502 0.779 6 7 0.002
2 309 0.115 7 5 0.002
3 75 0.05 8 1 0.0
4 27 0.018 9 2 0.0
5 10 0.013
Figure 3. Lotka’s law analysis.
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 coun-
tries contributed to the creation and publication of the 880 documents. The related out-
comes 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.
Figure 3. Lotka’s law analysis.
Future Internet 2025,17, 58 9 of 25
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 collabo-
rations 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.
Table 6. Country document publication details.
Country Documents SCP MCP Freq. MCP_Ratio
China 176 152 24 0.2 0.136
United States 132 125 7 0.15 0.053
India 98 92 6 0.111 0.061
Italy 46 41 5 0.052 0.109
United Kingdom 34 28 6 0.039 0.176
South Korea 30 24 6 0.034 0.2
Australia 26 20 6 0.03 0.231
Canada 25 19 6 0.028 0.24
Germany 24 22 2 0.027 0.083
Spain 18 15 3 0.02 0.167
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 consid-
ered. 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.
Future Internet 2025,17, 58 10 of 25
Table 7. Countries that received the most citations.
Country TC Average Document Citations
United States 1335 10.1
China 1209 6.9
Singapore 557 34.8
South Korea 294 9.8
Italy 258 5.6
Canada 255 10.2
India 243 2.5
Vietnam 164 82
Australia 162 6.2
France 127 21.2
Future Internet 2025, 17, x FOR PEER REVIEW 10 of 25
Figure 4. Country collaboration network.
Figure 5. Country collaboration map.
Moreover, the authors’ affiliation was also examined. Particularly, the information of
all authors whose affiliation details were retrieved from the databases and who contrib-
uted 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 Uni-
versity 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 affilia-
tions.
Figure 4. Country collaboration network.
Future Internet 2025, 17, x FOR PEER REVIEW 10 of 25
Figure 4. Country collaboration network.
Figure 5. Country collaboration map.
Moreover, the authors’ affiliation was also examined. Particularly, the information of
all authors whose affiliation details were retrieved from the databases and who contrib-
uted 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 Uni-
versity 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 affilia-
tions.
Figure 5. Country collaboration map.
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
Future Internet 2025,17, 58 11 of 25
authors from a specific country may be smaller than the sum of contributions from various
affiliations within that same country. Table 8presented 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.
Table 8. Most relevant affiliations based on the number of documents published.
Country Affiliation Number of Articles Percentage of Documents
in the Collection
Singapore National University of Singapore 30 3.4
Singapore Nanyang Technological University 18 1.8
Hong Kong City University of Hong Kong 16 1.7
United States Harvard Medical School 15 1.6
South Korea Sungkyunkwan University 14 1.5
Canada McGill University 13 1.5
Italy
UniversitàPolitecnica delle Marche
13 1.5
Slovakia University of Žilina 13 1.5
United States University of Central Florida 13 1.4
United States Carnegie Mellon University 12 1.4
Hong Kong Chinese University of Hong Kong 12 2.0
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 key-
words 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 addi-
tion, the frequency of the topic keywords and the most commonly used relevant keywords
were also identified. Specifically, Table 9presents 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”, “ex-
plainable 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, gen-
erative 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.
Future Internet 2025,17, 58 12 of 25
Table 9. Most frequently used keywords plus (indexed keywords).
Topic Keywords Relevant Keywords
Words Occurrences Words Occurrences
“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.
Topic Keywords Relevant Keywords
Words Occurrences Words Occurrences
“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
To better comprehend the relationships among the keywords, keyword co-occurrence
networks were created using Bibliometrix and VOSviewer. The related networks are dis-
played 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 repre-
sentation. 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), “vir-
tual 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.
Future Internet 2025,17, 58 13 of 25
Future Internet 2025, 17, x FOR PEER REVIEW 12 of 25
Topic Keywords Relevant Keywords
“engineering education” 21
“blockchain” 19
“education” 18
Table 10. Most frequently used author’s keywords.
Topic Keywords Relevant Keywords
Words Occurrences Words Occurrences
“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
To beer comprehend the relationships among the keywords, keyword co-occur-
rence 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-occur-
rence 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), “vir-
tual 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.
Figure 6. Keyword co-occurrence network — Bibliometrix.
Figure 6. Keyword co-occurrence network—Bibliometrix.
Future Internet 2025, 17, x FOR PEER REVIEW 13 of 25
Figure 7. Keyword co-occurrence network — VOSviewer.
Table 11. Total link strength of the keyword co-occurrence network — VOSviewer.
Keywords Occurrences Total Link Strength Keywords Occurrences Total Link Strength
“artificial intelligence” 457 1184 “deep learning” 51 188
“virtual reality” 338 951 “immersive” 43 177
“augmented reality” 251 677 “education” 51 173
“metaverse” 152 431 “digital twins” 31 142
“machine learning” 77 268 “human-computer interaction” 42 139
“extended reality” 75 243 “blockchain” 32 131
“e-learning” 52 223 “students” 32 129
“mixed reality” 51 204
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 net-
work. 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 ma-
chine learning and deep learning methods. Education is revealed as one of the main do-
mains 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,
Figure 7. Keyword co-occurrence network—VOSviewer.
Future Internet 2025,17, 58 14 of 25
Table 11. Total link strength of the keyword co-occurrence network—VOSviewer.
Keywords Occurrences Total Link
Strength Keywords Occurrences Total Link
Strength
“artificial intelligence” 457 1184 “deep learning” 51 188
“virtual reality” 338 951 “immersive” 43 177
“augmented reality” 251 677 “education” 51 173
“metaverse” 152 431 “digital twins” 31 142
“machine learning” 77 268 “human-computer
interaction” 42 139
“extended reality” 75 243 “blockchain” 32 131
“e-learning” 52 223 “students” 32 129
“mixed reality” 51 204
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.
Table 12. Analysis of the keyword co-occurrence network—Bibliometrix.
Cluster Keywords
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.
Cluster Keywords
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”
Future Internet 2025,17, 58 15 of 25
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 spec-
ified: (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 di-
vided 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”, “immer-
sive”, “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 in-
terface”, “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 explor-
ing 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.
Future Internet 2025, 17, x FOR PEER REVIEW 15 of 25
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 ma-
chine learning, deep learning, and neural networks has shifted toward a focus on the tech-
nologies 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 in-
creasing 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.
Figure 8. Thematic evolution of the topic.
Figure 9. Trend topics based on keywords plus.
Figure 8. Thematic evolution of the topic.
Future Internet 2025,17, 58 16 of 25
Future Internet 2025, 17, x FOR PEER REVIEW 15 of 25
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 ma-
chine learning, deep learning, and neural networks has shifted toward a focus on the tech-
nologies 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 in-
creasing 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.
Figure 8. Thematic evolution of the topic.
Figure 9. Trend topics based on keywords plus.
Figure 9. Trend topics based on keywords plus.
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.
Future Internet 2025,17, 58 17 of 25
Future Internet 2025, 17, x FOR PEER REVIEW 16 of 25
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 learn-
ing”, and (iv) the Emerging or Declining theme was related to “generative artificial intel-
ligence”. 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 “ma-
chine learning” approaches (e.g., adversarial machine learning, contrastive learning, fed-
erated learning, etc.). The second cluster was related to “augmented reality”, “mixed re-
ality”, “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, highlight-
ing 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.
Figure 10. Thematic map of the topic.
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,
Figure 10. Thematic map of the topic.
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
–
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 integra-
tion 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
Future Internet 2025,17, 58 18 of 25
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 health-
care 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 ap-
plication 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 inter-
est 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.
Table 14. Documents with the highest number of citations.
Document DOI Total
Citations
Total Citations
per Year
Normalized Total
Citations
[81] 10.1016/j.caeai.2022.100082 400 133.33 24.17
[82] 10.1038/s41467-021-25637-w 262 65.5 16.13
[83] 10.1002/aisy.202100228 173 57.67 10.46
[84] 10.1109/OJCS.2022.3188249 166 55.33 10.03
[85] 10.1016/j.engappai.2022.105581 164 82 31.03
[86] 10.1038/s41591-019-0539-7 150 25 8.04
[87] 10.23919/ICACT53585.2022.9728808 130 43.33 7.86
[88] 10.1016/j.jsurg.2019.05.015 115 19.17 6.17
[89] 10.1080/00207543.2020.1859636 105 26.25 6.46
[90] 10.23919/jcin.2022.9815195 97 32.33 5.86
[91] 10.1007/978-3-319-93843-1_12 97 13.71 11.05
Future Internet 2025,17, 58 19 of 25
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
Future Internet 2025,17, 58 20 of 25
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. More-
over, 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 aforemen-
tioned 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 compu-
tational limitations, algorithmic bias considerations, software and hardware limitations,
sustainability and interoperability considerations, as well as development and adoption
hurdles [
41
,
92
–
95
]. As these challenges exist for AI, the metaverse, and XR technolo-
gies, 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. Si-
multaneously, 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, analy-
sis, 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.
Future Internet 2025,17, 58 21 of 25
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 con-
firmed 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 di-
rections and highlighted the following topics as the ones being more actively researched:
“Education/Learning/Training”, “Healthcare and Medicine”, “Generative artificial in-
telligence/Large language models”, “Virtual worlds/Virtual avatars/Virtual assistants”,
“Human-computer interaction”, “Machine learning/Deep learning/Neural networks”,
“Communication networks”, “Industry”, “Manufacturing”, “E-commerce”, “Entertain-
ment”, “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 sup-
porting 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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