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Heliyon 10 (2024) e28602
Available online 26 March 2024
2405-8440/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Review article
Factors affecting metaverse adoption in education: A systematic
review, adoption framework, and future research agenda
Safwan Maghaydah
a
,
b
, Mostafa Al-Emran
a
,
c
, Piyush Maheshwari
a
, Mohammed
A. Al-Shara
d
,
e
,
*
a
The British University in Dubai, Dubai, United Arab Emirates
b
Abu Dhabi University, Abu Dhabi, United Arab Emirates
c
Department of Computer Techniques Engineering, Dijlah University College, Baghdad, Iraq
d
Department of Informatics, College of Computing & Informatics, Universiti Tenaga Nasional, Selangor, Malaysia
e
Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Selangor, Malaysia
ARTICLE INFO
Keywords:
Factors
Metaverse adoption
Education
Systematic review
Adoption framework
Research agenda
ABSTRACT
The Metaverse, underpinned by its technical infrastructure, heavily relies on user engagement
and behavior for successful integration into educational settings. Understanding its driving factors
is essential for such a platform to transition from theory to practice, especially in educational
settings. However, these factors remain elusive due to inconsistencies in infrastructure and en-
vironments. Therefore, this systematic review aims to ll this void by presenting an integrative
view on Metaverse adoption in education. This is achieved via three primary dimensions:
establishing a taxonomy of the factors inuencing Metaverse adoption in education, proposing a
framework for Metaverse adoption, and suggesting future research trajectories in this domain.
The review systematically classies the inuential factors into four distinct categories: psycho-
logical and motivational factors, quality factors, social factors, and inhibiting factors. The pro-
posed framework provides a structured approach for future studies investigating the Metaverse
adoption in educational settings. The proposed framework also emphasizes that educational in-
stitutions should not only consider the technical prerequisites but also the social, psychological,
and motivational aspects of the Metaverse. The study also pinpoints several critical research
agendas to enhance our understanding of Metaverse adoption in education. The insights from this
review are invaluable for educational institutions, policymakers, developers, and researchers,
signicantly enriching the emerging eld of Metaverse adoption.
1. Introduction
The information and communication technology (ICT) industry experiences a paradigm shift every decade, and it has been sug-
gested that the Metaverse is the new paradigm for the current decade [1]. In 2021, Mark Zuckerberg introduced the concept of a
transformative period for the digital landscape, wherein human physical presence would be integrated into an innovative virtual
construct termed the Metaverse [2]. The Metaverse is a developing technology that has drawn the interest of many educational
* Corresponding author. Department of Informatics, College of Computing & Informatics, Universiti Tenaga Nasional, Selangor, Malaysia.
E-mail addresses: safwanmaghaydah1970@gmail.com (S. Maghaydah), mustafa.n.alemran@gmail.com (M. Al-Emran), piyush.maheshwari@
buid.ac.ae (P. Maheshwari), mohamed.a.alshara@gmail.com (M.A. Al-Shara).
Contents lists available at ScienceDirect
Heliyon
journal homepage: www.cell.com/heliyon
https://doi.org/10.1016/j.heliyon.2024.e28602
Received 13 May 2023; Received in revised form 20 March 2024; Accepted 21 March 2024
Heliyon 10 (2024) e28602
2
researchers and practitioners [3,4]. Avatars, blockchain technology, and virtual reality (VR) headsets contributed to this new iteration
of the Internet, which also features a new way of fusing the real and virtual realms [5–7]. [8]. Metaverse is a term that refers to the next
generation of the Internet, where users can have an immersive online experience in a network of virtual environments [9,10]. The
progression of technology supporting the Metaverse’s development is accelerating quickly. This advancement incorporates the uti-
lization of VR headsets, augmented reality (AR), extended reality (XR), and haptic gloves [11]. In this digital world, users have avatars
that they can use to interact with other users and objects in the same environment.
Recently, the concept of the Metaverse has garnered signicant attention from higher education institutions (HEIs). Emerging
technologies in HEIs have become increasingly important in recent years [12,13]. The Metaverse offers a dynamic learning envi-
ronment and a new form of real-time interaction for people [14]. The Metaverse provides an immersive learning environment that
boosts motivation by enabling students to participate in virtual classes and communicate with instructors and peers via avatars [15,
16]. The Metaverse offers a more comprehensive and realistic learning experience than traditional virtual or augmented reality-based
education. For instance, in learning English as a foreign language, the Metaverse aims to create a life-like environment that allows
learners to use English for different activities (work, learning, social events), distinct from virtual reality-based education concen-
trating solely on language courses [17]. As a result, the Metaverse enables learners to operate in more authentic contexts than virtual
reality education. Additionally, the Metaverse provides a formal training program. Instruction comes from a non-player character
master. This character can simulate an authentic training process. For example, it can mimic a three-month period in a professional
training institute. In contrast, augmented reality-based training is limited. It is typically a short-term activity. This training often lasts
only 1 h to complete a specied practice [18].
The development of the Metaverse has opened up new possibilities for education [19]. Despite the early stage of this technology,
many studies have highlighted its potential benets and challenges [20]. Even with considerable research, a clear understanding of the
elements that drive Metaverse adoption remains elusive [21]. This deciency impedes researchers and experts in formulating efcient
methods to boost the acceptance of the Metaverse. It highlights the pressing need for more in-depth research and a detailed exami-
nation of this eld [22]. Although previous studies have delved into different facets of Metaverse adoption, a comprehensive review
using recognized adoption theories and models to grasp this domain has yet to be conducted [23]. This indicates a lack of compre-
hensive understanding and possibly a fragmented approach in prior research. In addition, earlier studies might have examined some
factors inuencing Metaverse adoption in education, but a comprehensive taxonomy that systematically categorizes these factors into
distinct groups appears to be missing. Most attention might have been focused on the technical prerequisites, neglecting these
non-technical but essential aspects. The literature also lacks a structured framework for adopting the Metaverse in education. This gap
makes it difcult to systematically explore and analyze the factors inuencing this adoption, thereby hindering the development of
efcient strategies to encourage more widespread use of the Metaverse in educational settings. Therefore, this systematic review aims
to comprehensively analyze the current literature on Metaverse adoption in education. To achieve this aim, the following research
objectives were put forward.
RO1. To analyze the key factors inuencing the adoption of Metaverse in education.
RO2. To propose a Metaverse adoption framework for educational purposes.
RO3. To provide critical research agendas on Metaverse adoption research.
A systematic review of multiple studies was conducted to accomplish these objectives, focusing on the latest developments in
Metaverse adoption in the education sector. The review highlights the most recent trends and ndings related to implementing
Metaverse for educational purposes. This systematic review provides the most recurrent adoption theories for Metaverse and the
factors that signicantly affect its adoption in education. Additionally, this review proposes a Metaverse adoption framework for
educational purposes. The proposed framework incorporates the synthesized factors, providing a comprehensive guide for institutions
to assess their readiness for Metaverse adoption. This systematic review not only provides insights into the current state of Metaverse
adoption in education but also identies several promising avenues for future research. These research agendas aim to ll the gaps in
the current body of knowledge and further advance our understanding of Metaverse adoption in education. Given the rapid devel-
opment of Metaverse technology and the growing interest in its educational applications, this study is timely and signicant. It is hoped
that its ndings will inform and inspire future research and practice in Metaverse adoption in education.
2. Background
2.1. Technology adoption theories and models
Theories and models of technology adoption are essential to understanding how individuals and organizations adopt new tech-
nologies. They offer insights into the determinants and processes involved in technology adoption decisions. Several theories and
models have been instrumental in scrutinizing technology adoption at the individual and organizational levels. These models have
been developed through a continuous process of validation and extension. Notably, the Theory of Reasoned Action (TRA) introduced
by Ajzen and Fishbein [24] is a psychological model that has further evolved into the Theory of Planned Behavior (TPB) [25] and,
subsequently, the Decomposed Theory of Planned Behavior (DTPB). The Technology Acceptance Model (TAM) [26], derived from the
TRA, was introduced in information systems. It has been then expanded into TAM2 [27] and eventually to the Unied Theory of
Acceptance and Use of Technology (UTAUT) [28]. The UTAUT model is a synthesis of other models, incorporating the aforementioned
theories along with Rogers’ Diffusion of Innovations (DOI), Bandura’s Social Cognitive Theory (SCT) [29], and Deci & Ryan’s
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3
Motivational Model [30]. In covering the technology acceptance models, a domain-specic approach has been taken, with a chro-
nological organization within each domain to map the evolution and interconnections between models. Despite each model’s unique
investigative approach to the acceptance process, common threads and themes emerge across these models.
Recent studies have applied these technology adoption models in the context of Metaverse adoption in education. For instance,
Sunardi et al. [31] delved into the acceptance of augmented reality in video conference-based learning during the COVID-19 pandemic
in higher education, utilizing the UTAUT2 framework. Similarly, Yang et al. [32] investigated the intent of college students to utilize
the Metaverse for basketball learning, grounding their study in the UTAUT2 model. Gim et al. [33] examined the interconnectedness
between the quality of VR-based education, self-determination, and learner satisfaction, incorporating the SDT, IS success model, and
TAM into their analysis. Teng et al. [19] undertook an empirical study to scrutinize the factors inuencing learners’ adoption of an
educational Metaverse platform, extending the UTAUT model in their research. Alawadhi et al. [34] explored the determinants
affecting the acceptance of the Metaverse in medical training among medical students, utilizing both TAM and IDT. Kim et al. [35]
probed the impact of students’ perceptions on their intention to engage with the Metaverse learning environment in higher education,
employing the TAM framework. Almarzouqi et al. [36] predicted users’ intentions to utilize the Metaverse in medical education,
incorporating TAM and IDT into their analysis. Akour et al. [37] developed a conceptual framework for evaluating Metaverse adoption
in higher institutions across the Gulf area, underpinned by TAM and IDT, and conducted an empirical study to validate it. Additionally,
Makransky and Mayer [38] scrutinized the advantages of immersive virtual reality for virtual eld trips, applying the
cognitive-affective model (CAM) in their research. These studies demonstrate the application of established technology adoption
models to the emerging eld of Metaverse adoption in education, enriching our understanding of the factors inuencing adoption in
this context.
2.2. Metaverse and education
Employing the Metaverse in education is not recent and has been the focus of scholarly debate for several years. Kemp and Liv-
ingstone (2006) scrutinized the potential of integrating virtual worlds like “Second Life” with learning management systems to
enhance the educational process [39]. Collins (2008) postulated that the Metaverse could evolve into the subsequent platform for
social interaction and suggested that higher education institutions should leverage this technology proactively for instructional
purposes [40]. Moreover, it has been put forward that the immersive 3D digital environment enhances user interaction and
communication through avatars, which augments the sense of presence [41]. In 2006, a collaborative effort was undertaken at the
Stanford Research Institute International to envision the future of Metaverse technology. This summit assembled academics, tech-
nology architects, entrepreneurs, and futurists to forecast the trajectory of the Internet in the upcoming decade.
The Metaverse offers a canvas for innovation across different sectors [42]. The use of immersive technologies, such as VR, MR, AR,
and XR, has increased the popularity of the Metaverse in educational applications. One benet of the Metaverse is that it allows
students to attend virtual classes and interact with teachers and classmates through avatars, providing an immersive learning expe-
rience that can improve motivation [15,16]. Another advantage is that the Metaverse can enhance collaboration among students inside
and outside the classroom and school, leading to an inclusive and interactive learning experience and developing teamwork and
problem-solving skills. Studies have also shown that using Metaverse in subjects such as maintenance and mathematics can enhance
students’ learning outcomes [14]. Researchers have identied different worlds within the educational Metaverse, such as survival,
maze, multi-choice, racing/jump, and escape room [43]. While several Metaverse technologies are being used or proposed in edu-
cation, there are still gaps in their implementation within the education sector. However, the Metaverse can bridge the divide between
the virtual and real world through its immersive environment [44].
2.3. Virtual, augmented, and mixed reality
The Metaverse relies heavily on technological advancements, such as virtual, augmented, and mixed reality, to fully develop and
provide an immersive experience [45–47]. These technologies can help the Metaverse to create a realistic simulation experience in
virtual environments [48,49]. Both virtual and augmented reality offer different levels of immersion [50,51], but share similar
characteristics in immersion, presence, and engagement [52]. Immersion measures the technology’s virtual, augmented, and mixed
reality capability to deliver a realistic environment [53]. Presence is the user’s perception of being in that environment [54,55].
Engagement refers to the increase of interest, concentration, and enjoyment of the learners, further divided into behavioral, emotional,
and cognitive engagement [56]. The use of virtual, augmented, and mixed reality technologies in education has positively impacted
learning outcomes. Due to the immersive nature of these technologies, they allow learners to engage with simulated real-life situations,
improving learning efciency [50]. Additionally, repeating learning scenarios in these technologies improves students’ ability to
absorb and understand new information [56].
Furthermore, the use of virtual reality and augmented reality technologies allows for enhanced experiential learning by providing a
broad range of sensory-motor interactions that might not be possible in real-life scenarios due to high costs or risks [57]. Allcoat et al.
[58] have shown that virtual reality produces a higher sense of presence and immersion than mixed reality, which leads to a better
learning experience. Besides, using virtual, augmented, and mixed reality in education has increased learners’ engagement, motiva-
tion, and dedication. Results of research conducted by Marks and Thomas showed that 71.5% of subjects reported improved learning
performance when using virtual and augmented reality for the rst time [59]. It has also been found that studying physics subjects in a
mixed reality environment can lead to higher levels of engagement and positive learning attitudes [60]. Additionally, mixed reality has
been shown to improve students’ abilities in certain subjects. Virtual reality and mixed reality have been found to lead to higher
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4
engagement levels than traditional methods, with virtual reality producing higher positive emotions [58].
From the Metaverse viewpoint, the Metaverse has the potential to enhance educational and social accessibility for students with
disabilities. Providing an immersive learning environment through virtual reality and augmented reality can help students with
autism, special needs, and social interaction difculties to improve their interpersonal and learning skills [61]. They can engage with
material and instructors safely without feeling overwhelmed. Using virtual and augmented reality visuals in the Metaverse allows
students to practice skills and interact with others in a controlled environment. Additionally, the Metaverse enables students to explore
various worlds through storytelling and visualization, such as virtual tours and 360-degree storytelling on global issues like education,
public health, urban development, climate change, and international trade [61].
2.4. Mirror world
A Mirror World, such as Google Earth or Microsoft Virtual Earth, is a digital representation of real-life where information from
physical space is replicated in virtual form, often with additional simulated elements [62]. The concept of the Metaverse can be traced
back to the 1992 book “Mirror Worlds” by David Gelernter [63]. Mirror Worlds, Metaverse, Multiverse, and Digital Terraforming are
related concepts, but their meanings may differ depending on the context and can overlap in certain aspects [62]. In other words, the
Mirror Worlds Metaverse is described as extending the real-world context through GPS and networking technology to address spatial
and physical limitations in teaching and learning [64]. Only one study was identied as a Mirror World Metaverse type [65]. The study
implemented game-based immersive learning by assembling students in a lecture hall, and the lecture was simultaneously “mirrored”
Fig. 1. PRISMA owchart.
S. Maghaydah et al.
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onto an online platform. Although the study showcased an “efcient expansion” of the real world, it did not fully exploit the potential
of the Mirror World Metaverse. For example, in Mirror World, users can interact with others from remote locations by playing games
and accomplishing meaningful tasks. However, the students in the study, who were convened in the lecture hall, could have engaged in
the game collectively with another group of students at a different university or even another country.
According to Tlili et al. [66], who focused on the studies related to the various types of the Metaverse, they found that most of the
studies reviewed focused on the Virtual Worlds Metaverse. In contrast, fewer studies explored augmented reality, and even fewer
explored Lifelogging and Mirror Worlds. Although the studies discuss the use of 3D technologies in virtual environments, they do not
delve into the technology to a high level of complexity [66]. For example, none of the studies address communication and collabo-
ration with AI characters. Furthermore, the studies do not fully exploit the explicit technology of Lifelogging and Mirror Worlds to a
high level in educational settings. Future studies should focus on exploring these areas with a higher level of sophistication, partic-
ularly in integrating augmented reality with Lifelogging or Mirror Worlds with simulation technologies.
3. Methods
This research aims to identify the key factors inuencing the Metaverse adoption in education through a systematic literature
review. This type of review follows a systematic, open, and repeatable approach to identifying, analyzing, and combining the ndings
of previous studies [67]. The study was carried out using the PRISMA 2020 guidelines [68], which provide a framework for creating a
well-organized and structured report on systematic reviews and analyses of international literature. Furthermore, the formulation of
the research objectives plays a critical role in the systematic review process as it sets the foundation and scope of the research. Fig. 1
illustrates the stages of the review methodology that were used in this study.
3.1. Eligibility criteria
The inclusion and exclusion criteria were used to determine the suitability of the studies to be included in the systematic review.
Studies had to be written in English and that at least answer one potential of the research objectives are eligible for inclusion. In
addition, all papers that discussed Metaverse in education and were published up to December 2022 were considered. Empirical
studies that examined relevant factors were given priority over other types of publications. On the other hand, papers that did not have
any connection to the research objectives, grey papers (i.e., papers excluding Metaverse with no relevance to research objectives or
incomplete papers), publications whose text was not accessible through research engines or via the authors themselves for verication,
and short papers with less than three pages were excluded from the review. This helped ensure the review results were based on high-
quality, relevant, and reliable studies. Table 1 presents the eligibility criteria for the review selection.
3.2. Source of information and search strategy
The empirical studies in this review were obtained by searching databases for information on Metaverse adoption. The literature
was searched using appropriate keywords. We utilize substitute terms and combine them by using Boolean operators. The term
“metaverse” AND (“adoption” OR “acceptance” OR “intention” OR “behavior” OR “behaviour”) AND (“education” OR “learning”)
were used in the search. The Web of Science and Scopus databases, recognized for their vast academic literature collections, were used
in this review to collect studies. These databases were selected based on their high impact, extensive disciplinary coverage, and
established reputation in supporting rigorous systematic literature reviews [69]. These databases encapsulate all high-quality papers
published across numerous esteemed platforms, such as the ACM Digital Library, Emerald, Google Scholar, IEEE, ScienceDirect,
Springer, Taylor and Francis, and Wiley Online Library. This encompassing scope secures a comprehensive and diverse body of
literature relevant to the research topic. The selection strategy mitigates potential bias and oversight by aligning with the inclusion and
exclusion criteria. Given their role in appraising journal productivity and citation impact, reliance on these databases helps incorporate
diverse studies that are pivotal to understanding the Metaverse’s adoption in education while minimizing potential bias and omissions.
Table 1
Eligibility criteria.
Criteria Inclusion Exclusion Rationale
Language The study is written in English. The study is written in a language other
than English.
To ensure the research team can fully comprehend
and analyze the studies.
Relevance The study addresses at least one of the research
objectives and discusses the Metaverse in an
educational context.
The study has no connection to the
research objectives or the Metaverse.
To ensure the paper’s content is directly relevant
to the study’s research objectives.
Type of
study
Empirical studies that examined relevant factors
affecting Metaverse adoption in education were
prioritized.
Theoretical papers, reviews, or opinion
pieces that do not present empirical
ndings.
To prioritize studies that provide concrete
evidence on the factors inuencing Metaverse
adoption in education.
Access The full text of the publication is accessible
through our chosen databases or directly from the
authors.
Full text is not accessible. To ensure the research team can conduct a
thorough analysis of each study.
Paper
length
The study is of standard length (more than three
pages).
The study is too short (less than three
pages).
To exclude studies that do not provide sufcient
data or analysis.
S. Maghaydah et al.
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6
The process of searching is summarized in Fig. 1, showing the number of papers left at each stage. The initial search of the databases
resulted in 100 papers. After eliminating duplicates (n =15), 85 papers were left for the screening process. During the screening
process, the abstracts, introductions, and conclusions were checked, which resulted in the exclusion of 76 articles. Thus, nine papers
remained for the eligibility assessment. There was no exclusion of any articles during the eligibility assessment. A total of nine articles
were nally selected for data extraction and synthesis.
3.3. Data extraction and analysis
We have collected and compiled the data from the studies included in the review into a Microsoft Excel spreadsheet. Information
such as the names of authors, year of publication, sample size, sampling method, study group, data collection methods, country, and
study design were all gathered for each study. We have also collected independent, dependent, and moderator variables. To further
ensure the robustness and reliability of the data extraction and analysis process, an in-depth analysis of each included article was
undertaken by the rst and fourth authors of this review. We adopted a rigorous approach, where any discrepancies identied in the
analyses were promptly addressed through deliberations and supplemental evaluations of the disputed research during the screening
process. This methodological approach underscores our commitment to presenting a comprehensive and reliable literature review.
During the screening process, both reviewers consistently and unanimously concurred on whether to include or exclude articles,
adhering to the predened eligibility criteria. In every instance, complete consensus was reached regarding the suitability of each
article for inclusion in the systematic review.
4. Results and discussion
4.1. Characteristics of included studies
This section describes the characteristics of the studies included in the nal systematic review. The review included nine studies
investigating the adoption of Metaverse, virtual reality, and augmented reality in education [19,31–38]. These studies were conducted
between 2021 and 2022, and participants primarily consisted of students and learners from various countries. Most of the studies (6
out of 9) employed either the Technology Acceptance Model (TAM) or the Unied Theory of Acceptance and Use of Technology
(UTAUT/UTAUT2) as their theoretical framework. Other theories included the Self-Determination Theory (SDT), the Information
Systems (IS) Success Model, and the Cognitive-Affective Model (CAM). All nine studies utilized surveys as their primary data collection
method, with seven of them employing online surveys. The study populations were mainly students and learners, focusing on
individual-level analysis in various countries, including Indonesia, China, South Korea, the United Arab Emirates (UAE), Saudi Arabia
(KSA), and Oman.
4.2. Key factors affecting the adoption of metaverse in education
This section summarizes the primary research ndings related to the key factors inuencing Metaverse adoption in education, as
identied through a systematic literature review. This review aims to consolidate existing knowledge and provide a foundation for
developing the Metaverse adoption framework in education. The identied key factors were categorized into four main groups: 1)
psychological and motivational factors, 2) quality factors, 3) social factors, and 4) inhibiting factors. Each category is presented
separately in the following subsections.
Table 2
Summary of psychological and motivational factors.
Variable Frequency Studies
Perceived ease of use/Effort expectancy 4 [33–36]
Facilitating conditions/Compatibility 5 [19,31,32,36,37]
Perceived trialability 2 [36,37]
Perceived usefulness/Performance expectancy 8 [19,31–37]
Habit 2 [31,32]
Attitude 1 [32]
Hedonic motivation/Enjoyment 4 [31,32,34,38]
Immediate retention 1 [38]
Immersion 1 [38]
Interest 1 [38]
Satisfaction 3 [19,36,37]
Perceived autonomy 1 [33]
Perceived competence 1 [33]
Perceived ow 1 [33]
Perceived relatedness 1 [33]
Presence 1 [38]
Personal innovativeness 3 [34,36,37]
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4.2.1. Psychological and motivational factors
The examined studies included a variety of factors relating to individuals’ psychological and motivational states that inuence the
Metaverse adoption or acceptance in education. Psychological factors refer to the mental and emotional processes that impact human
behavior [70,71]. These include beliefs, attitudes, personality traits, motivations, thoughts, and emotions. These factors are crucial in
comprehending behavior and can inuence how individuals react to and perceive their surroundings. The factors are listed in Table 2
and described in detail in the following subsections.
4.2.1.1. Perceived ease of use. Perceived ease of use (PEOU) refers to an individual’s perception of the effort required to use a
particular product, process, or system [72]. It is imperative to report that the terms effort expectancy and perceived ease of use have
similar meanings. Both terms refer to the degree of ease or difculty individuals perceive when using new technologies. This includes
aspects like ease of installation, implementation, maintenance, and operation. Studies have shown that ease of use is critical in pre-
dicting technology acceptance and intention to use technologies like the Metaverse in different educational contexts [33–36]. One of
the studies indicated a notable relationship between perceived usefulness and PEOU in adopting the Metaverse for learning purposes
[36]. It was observed that a higher level of perceived usefulness and PEOU was associated with a greater likelihood of adopting the
Metaverse [36].
4.2.1.2. Facilitating conditions. Facilitating conditions (FC) in the context of the Metaverse refer to learners’ perceptions of the
technical and organizational resources available to support their use of the platform [31]. It can be noted that the terms facilitating
conditions and compatibility have similar meanings. Several articles reported the signicant role of facilitating conditions in affecting
the adoption of the Metaverse in the educational sector [19,31,32,36,37]. For instance, a study by Yang et al. [32] investigated the
intention of college students to utilize the Metaverse for basketball education using the UTAUT2 framework. Their ndings revealed
that college students’ attitudes towards learning basketball via Metaverse technology are signicantly impacted by facilitating con-
ditions. These conditions pertain to the accessibility of essential resources and information required for basketball education.
4.2.1.3. Perceived trialability. The perception of trialability is strongly connected to an individual’s intention to use technology.
Multiple studies have supported that trialability positively impacts the adoption of new systems [36,37]. The term trialability pertains
to the ease of experimenting with new technology. It also encompasses other related ideas, such as the degree of effort required and the
potential risks involved, including the ease of undoing and recovering operations if necessary [73]. How a technology is perceived in
terms of its trialability can greatly affect whether an individual is willing to adopt and utilize it effectively. For example, Almarzouqi
et al. [36] suggested that perceived trialability is a signicant predictor of Metaverse adoption among students in medical education.
4.2.1.4. Perceived usefulness. Perceived usefulness refers to an individual’s belief in how much their use of a particular information
technology can enhance their work performance, such as by improving efciency, productivity, or accuracy [32,35]. Usefulness is a
critical factor that affects whether people adopt and continue to use new technologies [31,34]. The selected studies in this review have
used the terms perceived usefulness and performance expectancy interchangeably to refer to the same construct. It is imperative to
mention that this construct was examined by almost all the selected studies [19,31–37]. For example, Akour et al. [37] conducted a
study to examine students’ attitudes toward utilizing the Metaverse for educational applications in the Gulf region. In addition to
TAM’s constructs, the research evaluated user satisfaction and personal innovativeness. Moreover, the study developed a conceptual
model linking personal characteristics with technology-based features. The study’s ndings revealed that perceived usefulness
emerged as a signicant predictor of users’ intention to engage with the Metaverse platform.
4.2.1.5. Habit. A habit is a learned behavior that becomes progressively automatic and active in an individual’s learning process [74].
The extent of one’s interaction and familiarity with a technology determines the development of habits, which can occur at varying
degrees over time [75]. The results of the study [32] suggested that there is a positive cause-and-effect relationship between habit and
the behavioral intention of college students who are using the Metaverse to learn basketball. The results also indicated that habit has
the strongest inuence on behavioral intention, which suggests that it is a crucial factor for using the Metaverse platform to learn
basketball. Sunardi et al. [31] also discovered that habit is a crucial variable signicantly affecting behavioral intention to use
augmented reality in video conference-based learning in higher education. This indicates that the use of augmented reality in the
learning process has the potential to become habitual for the participants. In addition, the notable inuence of habit on behavioral
intention implies that individuals are more inclined to use augmented reality regularly and include it in their learning procedures.
4.2.1.6. Attitude. Attitude is the extent to which a person has a positive or negative evaluation or appraisal of the behavior in question
[25]. Another denition refers to the attitude as an interaction in memory between a particular subject and a summary evaluation of
the subject [76]. When an individual assesses the consequences of using a product or adopting a particular behavior positively, it
inspires them to have a favorable attitude and engage in that behavior [77]. The ndings of a study conducted by Yang et al. [32]
suggested that attitude is one of the critical variables that signicantly impacts the usage behavior and behavioral intention of college
students using the Metaverse to learn basketball. When students have a positive attitude towards using the Metaverse to learn
basketball, it improves their learning outcomes and motivates them to use the platform in other courses.
4.2.1.7. Hedonic motivation. Hedonic motivation is an individual’s feeling of pleasure or happiness when using new technology [78].
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8
It refers to how learners perceive that the Metaverse can impact their emotional feelings and responses. The desire for pleasure and
enjoyment can impact a consumer’s purchase intent for products advertised on social media. Marketers, for example, can design more
creative and engaging advertisements that increase their intrinsic effectiveness and interactivity, ultimately promoting consumers’
hedonic motivation [79]. The terms hedonic motivation and enjoyment are used interchangeably in the included studies and have
similar meanings [31,32,34,38]. Several studies underscore the role of hedonic motivation in the domain of Metaverse adoption in
education. For instance, Sunardi et al. [31] discovered that hedonic motivation substantially inuences the acceptance of augmented
reality in video conferences among university students. Complementing this, another investigation established that enjoyment
signicantly contributes to the efcacy of immersive multimedia learning [38].
4.2.1.8. Immediate retention. The immediate retention factor represents a learner’s capacity to recall and retain information shortly
after its presentation [80]. This factor is frequently employed to assess the short-term effectiveness of learning materials or teaching
methods by measuring the degree to which information is assimilated and preserved in the learner’s memory immediately following
exposure. Various factors can inuence immediate retention, such as enjoyment and interest, as well as the quality and organization of
learning materials [38]. Evaluating immediate retention enables educators and instructional designers to identify areas for
improvement in their teaching strategies or learning materials, ultimately aiming to enhance the overall learning experience and
outcomes [81].
4.2.1.9. Immersion. Immersion indicates how well a system creates a realistic virtual environment and blocks out the physical world
[82]. In the context of Metaverse gaming, immersion can be used as a metric to evaluate technology-enabled extended reality contexts,
and it is not based solely on the game’s aesthetics [83]. Immersion in Metaverse gaming can positively impact a player’s sense of
usefulness [83]. Makransky and Mayer [38] conducted a study examining and exploring the immersion principle in multimedia
learning. The research indicated that students who used a head-mounted display (HMD) for a virtual eld trip had higher immediate
and delayed post-test scores than those who used an onscreen video with lower immersion. Moreover, the same student group with
HMD expressed greater levels of presence, interest, and enjoyment, which supports the immersion principle in multimedia learning.
4.2.1.10. Interest. The interest factor in learning refers to the level of curiosity, fascination, and enjoyment a learner feels when
participating in a learning activity [84]. Learners interested in a topic or activity are likelier to interact with it, remember information
better, and perform better on tests. Makransky and Mayer’s study examines whether higher-immersion environments promote greater
interest levels among students and the potential correlation between interest and presence [38]. According to their results, the use of
HMD in a virtual eld trip proved to be more benecial than a 2D video version in terms of presence, enjoyment, interest, short-term,
and long-term retention. The ndings also indicated that immediate post-test results were affected by enjoyment, while delayed
post-test results were inuenced by interest.
4.2.1.11. Satisfaction. Satisfaction is essential in motivating users to use specic technologies, products, or brands. Users’ satisfaction
refers to the afrmative emotions that users associate with using new technology, as it aligns with their expectations and anticipated
uses [37]. Teng et al. [19] explored the factors that affect the adoption of an educational Metaverse platform called “Eduverse” using
an extended UTAUT model. The results demonstrated that performance expectancy, facilitating conditions, social inuence, and effort
expectancy had a signicant positive impact on learners’ satisfaction with the Eduverse. Furthermore, learners’ satisfaction was
positively associated with their continued usage intention, but their intention to use the Eduverse decreased when they perceived risks
[19]. Another study [36] found that user satisfaction is an essential determinant of users’ intention to use the Metaverse in medical
education. Akour et al. [37] also investigated the relationship between adoption-based properties and users’ satisfaction. The results
showed that complexity, observability, compatibility, and trialability strongly inuenced students’ adoption of the Metaverse in higher
education.
4.2.1.12. Perceived autonomy. Perceived autonomy is dened as an individual’s perception of having the ability to make choices and
exert control over their actions and behaviors [85]. Gim et al. [33] developed a research model that combines several theories to
explore variables affecting learner satisfaction in VR education, highlighting the importance of self-directed learning and ow in
achieving optimal educational practices in the Metaverse. Upon examining the obtained results, it can be concluded that the hypothesis
proposing a positive correlation between perceived autonomy and perceived ease of use is unsupported. This indicates that the par-
ticipants of the study already had prior experiences using VR and AR-based content autonomously, and therefore perceived autonomy
did not signicantly affect ease of use. The results suggested that people tend to go for VR and AR-based educational courses due to
their convenience in comparison to other online content.
4.2.1.13. Perceived competence. Individuals’ desire for control and leverage when striving for important goals leads to the need for
perceived competence, which is the subjective perception of their ability to accomplish a task or attain a goal [30]. A study [33]
discovered several variables, including perceived competence encompassed by self-determination theory, positively inuenced learner
satisfaction when utilizing Metaverse-based VR content. This impact was particularly evident in perceived usefulness, ease of use, and
ow experience. The results highlighted the importance of self-determination theory as a foundational framework for studies
measuring learner satisfaction in VR education.
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4.2.1.14. Perceived ow. Perceived ow is the personal perception or subjective experience of being in a ow state. It is a psycho-
logical construct that reects how individuals perceive their engagement, enjoyment, and satisfaction while engaging in a specic
activity [86]. In this context, learning ow refers to a state where learners are fully engaged and absorbed in the learning process,
enjoying the content and experiencing optimal learning, which can result in increased motivation, engagement, and focus [33]. Gim
et al. [33] found that self-determination theory signicantly impacts VR-based education in the Metaverse, indicating its importance in
virtual education, similar to online education. Surprisingly, the study found that information and service quality did not affect
learners’ ow, but system quality did. This suggests that the stability of VR-based content in the Metaverse is crucial in determining
learners’ ow.
4.2.1.15. Perceived relatedness. The psychological concept of perceived relatedness is associated with an individual’s sense of
belongingness and subjective connection to others in a social environment [87]. Perceived relatedness is a crucial factor affecting
learner satisfaction in Metaverse-based education [33]. A study indicated that learners who perceive a greater sense of relatedness in
the virtual environment are more likely to experience positive effects in their perceived ow, ease of use, and usefulness of the content
[33].
4.2.1.16. Presence. Presence in the context of virtual simulations refers to the psychological sensation of feeling present or “being
there” in the simulated environment, as described by Slater [88]. In the framework of Metaverse education, social agency theory
suggests that when students have a higher sense of presence, they tend to engage in more profound cognitive processing and put in
more cognitive effort to comprehend material [89]. Makransky and Mayer [38] revealed that generating immersive educational ex-
periences that foster a high level of presence can positively impact learning through cognitive and affective processes, including
enjoyment and interest, which are crucial for capable and enthusiastic learners. Moreover, matching the affordances of immersive
technology with appropriate instructional design is vital to enhancing learning outcomes. The study applied evidence-based
instructional design principles to both the HMD and 2D versions of a virtual eld trip. The immersive HMD experience increased
presence, enjoyment, interest, and immediate and long-term retention.
4.2.1.17. Personal innovativeness. Personal innovativeness is dened as the willingness of users to accept and use new technology,
which includes readiness as an external factor to measure the acceptance of technology [90]. Alawadhi et al. [34] investigated the
determinants inuencing the adoption of Metaverse in medical training. The ndings revealed signicant associations between per-
sonal innovativeness, which is inuenced by perceived ease of use, and perceived usefulness of the technology. Similarly, another
study [36] suggested that personal innovativeness is essential in adopting Metaverse-based medical training. Students more willing to
embrace technological innovations are likelier to have a positive attitude toward Metaverse adoption. Those students tend to perceive
uncertainty positively and view it as an opportunity for learning and growth. Therefore, personal innovativeness can be considered an
essential factor in the adoption of innovative technologies in medical education.
4.2.2. Quality factors
Quality refers to “the measures that determine the perceived quality of an information system (IS) or technology” [91]. In the IS
success model proposed by DeLone and McLean (1992), quality factors are deemed integral predictors of IS success. The model en-
compasses three quality factors most frequently referred to in IS research: information quality, service quality, and system quality. The
acceptance or sustained utilization of new technology hinges upon the user’s perception of its quality. Numerous studies have
underscored the signicant role of quality factors in comprehending users’ continuous usage of various technologies [92]. Table 3
summarizes the quality factors affecting the Metaverse adoption in education. The subsequent subsections describe each of these
factors.
4.2.2.1. Information quality. Information quality pertains to the degree of excellence in the attributes of information that an infor-
mation system produces, such as accuracy, usefulness, and timeliness [33]. Evaluating information quality based solely on content and
accuracy can sometimes lead to conicting evaluations, depending on the intended use and timing of the information. In this context,
information quality refers to the accuracy, reliability, relevance, timeliness, and overall trustworthiness of the information and re-
sources available within the Metaverse environment for educational purposes. As educational institutions and learners begin inte-
grating Metaverse technologies into their learning experiences, ensuring that the information shared and used is of high quality to
facilitate effective learning outcomes is essential.
4.2.2.2. System quality. System quality refers to “the degree of technological excellence of an information system that users evaluate
Table 3
Summary of quality factors.
Variable Frequency Studies
Information quality 1 [33]
Service quality 1 [33]
System quality 1 [33]
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while using the system as it acquires and processes information to facilitate communication” [33]. This encompasses the system’s
hardware, software, and network components’ quality, in addition to the accessibility of the system, the degree to which it fullls user
requirements, its response time, and its capacity for exibility and adaptability with user needs. System quality in this context refers to
the effectiveness, efciency, and overall performance of the Metaverse platforms used for educational purposes. As educators and
institutions consider implementing Metaverse technology for learning and teaching, assessing the system’s quality is essential to
ensure a positive impact on the educational experience.
4.2.2.3. Service quality. Service quality refers to the level of excellence in an information system’s services or the extent to which it
satises user needs concerning its benets [93]. Gim et al. [33] found that system quality is a crucial factor in learners’ ow or their
state of immersive engagement and enjoyment. In contrast, neither information quality nor service quality had a signicant impact on
ow. This unexpected nding underscores the signicance of system stability in VR-based education within the Metaverse. It implies
that guaranteeing superior quality and stability in the technical components of virtual content is essential for fostering an immersive
and productive learning experience.
4.2.3. Social factors
Social factors refer to how individuals within a social group impact each other’s actions and decisions regarding using and
accepting new technologies [94]. Social factors can greatly inuence the adoption of a specic technology. The social context in which
technology is introduced can impact its adoption, particularly in communities with limited access to digital tools and education. In this
review, two social factors were found to inuence the adoption of Metaverse in education, as specied in Table 4. The following
subsections describe each of these factors.
4.2.3.1. Perceived observability. Perceived observability is a term used in the context of innovation diffusion, which refers to the extent
to which the results of an innovation are visible to others [95]. It is the degree to which individuals can observe and understand the
benets of new technology or innovation by observing others who have already adopted it. For example, it was found by Almarzouqi
et al. [36] that perceived observability positively inuences students’ satisfaction in adopting the Metaverse for medical educational
purposes. Similarly, Akour et al. [37] stated that the more positive perceived observability is, the higher the learners’ satisfaction in
adopting the Metaverse in educational systems.
4.2.3.2. Social inuence. Social inuence is how other individuals inuence a person’s attitudes, beliefs, and actions. It encompasses
how someone is swayed by the views, conduct, or prospects of others and can be either through personal interactions or through
various forms of media. When it comes to using the Metaverse in education, social inuence pertains to the extent to which an in-
dividual’s choice to adopt or employ the Metaverse is swayed by the viewpoints or judgments of others, like teachers, relatives, or
peers. Research has revealed that social inuence considerably impacts learners’ satisfaction, thereby inuencing their continuous
engagement with the Metaverse platform for educational objectives [19]. On the other hand, Sunardi et al. [31] studied the acceptance
of augmented reality in video conferences to motivate and inspire learners. They found that social inuence is positive but has little
signicance since this technology is still new and requires time to persuade relevant inuence partners [31]. Furthermore, the use of
the Metaverse for learning basketball by college students is not signicantly impacted by social inuence [32].
4.2.4. Inhibiting factors
Inhibiting factors hinder the adoption or implementation of a particular technology, process, or innovation [19]. Adopting the
Metaverse in education can be impeded by two inhibiting factors, as found in this systematic review. The inhibiting factors are listed in
Table 5 and described in the following subsections.
4.2.4.1. Perceived risk. Perceived risk is the subjective assessment of the potential loss or harm associated with a decision or action
[96]. In electronic services, perceived risk often involves concerns about the safety and security of personal information, which can
lead to reduced adoption rates, lower levels of engagement, and reduced trust in service providers [97]. Even if users are satised with
a product or service, their trust in the provider may be compromised if they perceive the risk of harm or loss as high, leading to a
reluctance to continue using the service [98]. Therefore, addressing user concerns about safety and security is crucial for building trust,
increasing adoption rates, and promoting positive user experiences in electronic services. Teng et al. [19] found that learner’s intention
to use the Eduverse platform was decreased signicantly after they perceived risks, such as providing personal information (e.g.,
mobile phone numbers) upon registration on the platform. Therefore, addressing user concerns and minimizing perceived risk are
pressing issues that require a solution.
Table 4
Summary of social factors.
Variable Frequency Studies
Perceived observability 2 [36,37]
Social inuence 3 [19,31,32]
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4.2.4.2. Perceived complexity. Perceived complexity refers to an individual’s subjective assessment of the difculty and technical
expertise required to use a particular technology [99]. The more complex a technology appears, the more difcult it may be to adopt or
integrate into an organization [100]. Perceived complexity can create a sense of uncertainty and lead to a lack of condence in the
effectiveness of the technology [100]. As an emerging technology like the Metaverse, its complexity is an essential factor to be
addressed to ensure learner satisfaction. Akour et al. [37] studied the students’ perceptions of adopting the Metaverse for educational
purposes in the Gulf area. The results showed that students evaluate the importance of less complexity as a positive and signicant
factor in adopting the Metaverse for instructional purposes. Therefore, simplicity is the most effective means of promoting Metaverse
adoption.
4.3. Proposed Metaverse adoption framework in education
The objective of this study was to conduct a systematic and in-depth review of the literature to identify the main factors that
contribute to predicting the adoption of the Metaverse in the education sector. The study’s outcomes are not geographically con-
strained and can be applied on a worldwide scale. The literature review involved analyzing various studies to identify the critical
factors to consider when adopting the Metaverse in education. This review identied 23 factors, grouped into four main categories,
including psychological and motivational factors, quality factors, social factors, and inhibiting factors. Based on these four categories
and their interrelationships in the analyzed literature, we proposed a Metaverse adoption framework that includes the intention to use,
learner stratication, long-term retention, and usage behavior as target variables. The proposed adoption framework is depicted in
Fig. 2. This provides a comprehensive framework for understanding the complex relationships between various factors and their effects
Table 5
Summary of inhibiting factors.
Variable Frequency Studies
Perceived risk 1 [19]
Perceived complexity 1 [37]
Fig. 2. Proposed Metaverse adoption framework.
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on successfully integrating the Metaverse environment in educational settings.
This study highlights the critical target variable found in the literature review: the intention to use the Metaverse. The intention to
use the Metaverse is a crucial factor in higher education and has been identied as the dependent variable in six studies [19,31,34–37].
Several independent variables have been identied to inuence the intention to use the Metaverse, including perceived usefulness,
facilitating conditions, perceived ease of use, and hedonic motivation. These factors signicantly affect the intention to use the
Metaverse in different aspects of education [19,31,34–37]. The intention to use the Metaverse in education is determined by the
perceived value and benets students can gain. This result is similar to Ref. [50]’s ndings, which suggest that perceived usefulness
signicantly impacts users’ intention to use. Students are more inclined to adopt the Metaverse if they believe it is benecial, simple,
and entertaining. External variables like the availability of technology and technical help can also inuence the intent to use [31]. In
other words, students are more inclined to adopt the Metaverse if they believe it will assist them in accomplishing their objectives and
improve their learning experience. To ensure the success and acceptance of Metaverse in educational contexts, educators must consider
these elements when developing and implementing it.
Additionally, perceived trialability, personal innovativeness, and learner satisfaction are other psychological factors inuencing
the intention to use the Metaverse in education. However, their impact may be less important than those of other variables. Although
perceived trialability can inuence students’ eagerness to experiment with new technology, personal innovativeness may not be as
important in deciding intent to employ the Metaverse, and learner satisfaction may not be as effective as other variables. Two
inhibiting variables that can have a detrimental impact on the intention to use the Metaverse in education are perceived risk and
perceived complexity. Students may be less inclined to adopt the Metaverse if they believe that using it would result in undesirable
effects or that it is too complicated to operate [19]. This can reduce people’s desire to use the Metaverse environment and reduce its
usefulness in educational contexts.
On the other side, perceived observability and social inuence are two social factors inuencing the desire to employ the Metaverse
positively. When students perceive that using the Metaverse is encouraged by social norms, they are likelier to use it [31,36]. As a
result, while adopting the Metaverse in education, educators need to consider these factors. By lowering perceived risk and complexity,
educators may foster a more welcoming atmosphere that increases student involvement and technology adoption. Furthermore, ed-
ucators can increase perceived observability and social inuence by emphasizing the benets of technology and building a culture of
support and encouragement for its use. By addressing these social and inhibiting aspects, educators can boost the intention to use the
Metaverse environment in education and optimize its potential to improve student performance.
Learner’s satisfaction is another target variable found in this study. Various psychological factors related to how students perceive
and interact with the Metaverse environment can affect this dependent variable. These variables include how easily students can use
the technology, how valuable they believe it to be for their learning goals, how much control they have when using it, how competent
they perceive when using it, and how easily they can reach a state of immersive concentration when using the Metaverse [33].
Therefore, when developing educational experiences that incorporate the Metaverse environment, educators need to consider these
psychological factors and strive to create environments that render the technology easy to use, useful, autonomous,
competence-enhancing, and capable of fostering ow, which can lead to higher learner’s satisfaction and acceptance of the technology.
In addition to psychological factors, quality factors of information, system, and service are other critical variables that affect the
learner’s satisfaction in adopting the Metaverse in education. High-quality information, system performance, and supportive services
are required to maximize learner satisfaction and, as a result, technology adoption [33]. Thus, maintaining these quality factors re-
quires ongoing monitoring and development and providing students with appropriate training and assistance to improve their
experience.
Usage behavior is another outcome variable identied in this review. It can be inuenced by some psychological and social factors.
Only one study examined the usage behavior of the Metaverse in learning [32]. This systematic review showed that psychological and
motivational factors are important in determining usage behavior. For example, learners are more likely to adopt and continue using
the Metaverse if they nd it simple to use. Moreover, access to necessary resources and support, for example, is important in deter-
mining usage behavior. Students are more inclined to utilize technology effectively and continue to use it if they have access to the
required tools and support. Another key aspect that can inuence usage behavior is perceived usefulness. Students are more willing to
use technology regularly if they believe it will improve their learning outcomes. Hedonic motivation can also inuence user behavior
[32]. Students have a greater opportunity to continue using technology if they enjoy it. Offering engaging and exciting learning op-
portunities via the Metaverse platform can improve hedonic motivation.
While not equally important as the previously mentioned factors, habits and attitudes towards the Metaverse can also impact usage
behavior. If students habitually use the Metaverse in their lessons or other educational activities, they are more likely to continue using
it in the future. In addition to psychological factors, only one of the two social factors was found to affect the usage behavior, which is
the social inuence. According to Ref. [32], students who believe their colleagues are utilizing the Metaverse platforms are more
inclined to use them, and those who receive good feedback from their peer group are likelier to keep using them. Apart from that, none
of the quality and inhibiting factors were found to have any direct or indirect effect on usage behavior. However, quality and inhibiting
factors should be considered in future research as they can increase the probability of adopting the Metaverse in education among
students.
The long-term retention as a target variable is a signicant aspect that can be inuenced by a variety of psychological and
motivational factors. These factors include hedonic motivation, presence, immediate retention, immersion, and interest [38]. Similar
to our ndings, previous research indicated that students with high levels of these variables are more likely to retain the knowledge
they acquired and keep using the Metaverse platforms in their education activities over an extended period [101]. Educators can use
these ndings to create immersive and interesting Metaverse learning experiences that stimulate good feelings, improve presence,
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encourage immediate retention, and include material pertinent to students’ interests. Education professionals can encourage long-term
retention in students who use Metaverse platforms for learning by considering these psychological and motivational variables.
5. Future research agenda
The Metaverse is a rapidly evolving technology with the potential to revolutionize education. However, much research still needs to
be investigated to understand how the Metaverse can be effectively used in the classroom. Table 6 lists key areas that need to be
considered in future research.
6. Conclusion
In conclusion, this systematic review thoroughly examined the current state of Metaverse adoption in education, outlining crucial
inuencing factors categorized into four categories: psychological and motivational factors, quality factors, social factors, and
inhibiting factors. Based on this taxonomy, we have proposed a framework for Metaverse adoption to comprehensively understand
how these factors intertwine within educational settings. The proposed framework also emphasized that educational institutions
should consider not only the technical prerequisites but also the social, psychological, and motivational aspects of the Metaverse. The
aim was to bring these insights to the forefront, thus providing several critical research agendas to enhance our understanding of
Metaverse adoption in education. The following subsections delve deeper into the theoretical contributions, practical implications, and
the current review’s limitations and future work.
6.1. Theoretical contributions
This study makes several noteworthy theoretical contributions to the existing knowledge on Metaverse adoption in education. First,
it proposed a comprehensive framework that identies the key factors inuencing the adoption and use of Metaverse in education,
providing a coherent understanding of the interplay between various factors and their impact on Metaverse adoption. Second, the
study integrates multiple theories and models from technology adoption literature, such as the TAM, UTAUT, and DOI. This integration
allows for a more holistic perspective on Metaverse adoption in education, which can be leveraged in future research and practice.
Third, the systematic review highlights current research on Metaverse adoption in education, identifying gaps that warrant further
investigation. By outlining these gaps, this study encourages researchers to explore under-researched aspects of Metaverse adoption,
contributing to a more comprehensive understanding of the phenomenon.
Fourth, by focusing specically on the education sector, this study contextualizes its ndings, contributing to understanding
Table 6
Key areas for future research agendas.
Key areas Description
Balancing innovation and privacy/
security
Explore how educators, administrators, policymakers, and Metaverse developers can balance fostering innovation
while ensuring user privacy and security in the Metaverse.
Challenges of using the Metaverse in
education
Identify challenges associated with Metaverse implementation in educational settings and propose strategies to
overcome these obstacles.
Effectiveness of Metaverse-based
learning activities
Examine the types of learning activities most effective within the Metaverse and provide insights for teachers on
designing and implementing Metaverse-based learning experiences that maximize student outcomes.
Ethical implications of using the
Metaverse
Address the ethical implications of Metaverse usage in education, such as data privacy, digital divide, and content
moderation, and develop guidelines promoting technology’s responsible and ethical use in educational contexts.
Impact on student learning outcomes Investigate how the Metaverse compares to traditional learning methods in terms of student engagement, motivation,
and achievement, helping educators make informed decisions about incorporating Metaverse environments into their
teaching practices.
Inclusivity, diversity, equity, and
inclusion
Explore how the Metaverse can be designed and managed to promote inclusivity, diversity, equity, and inclusion
among its users.
Interoperability standards Identify standards that facilitate seamless user movement between different Metaverse platforms and investigate
effective implementation strategies.
Regulatory and ethical considerations Investigate potential regulatory and ethical considerations that could impact the development and deployment of the
Metaverse and propose effective ways to address them.
Role of the teacher in the Metaverse Explore how teachers can effectively facilitate learning within the Metaverse and identify the new skills and
knowledge they need to succeed in this novel learning environment.
Social and ethical implications Investigate the social and ethical ramications of the Metaverse, including issues related to inequality, diversity, and
digital citizenship.
User preferences, motivations, and
behavior patterns
Examine what drives users to engage with the Metaverse and identify their preferences and behavior patterns within
this digital environment.
Using Metaverse technologies for
immersive learning
Investigate how Metaverse technologies, such as virtual and augmented reality, can be utilized to create immersive
and interactive learning experiences for students, enhancing their engagement and understanding of complex
concepts.
Integrating Metaverse technology in the
classroom
Explore the most effective ways to integrate Metaverse technology into the classroom and curriculum, considering
factors like technical infrastructure, teacher training, and alignment with learning objectives.
Personalized learning experiences using
Metaverse
Examine how Metaverse platforms can create personalized learning experiences for students, allowing them to learn
at their own pace, explore topics of interest, and receive tailored feedback and support.
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Metaverse adoption in a unique context. These ndings can help researchers and practitioners understand how the Metaverse can be
effectively implemented in educational settings and how it may differ from other contexts. Fifth, this study serves as a foundation for
future research on Metaverse adoption in education. By outlining key factors and their interrelationships, researchers can build upon
these ndings to investigate new research questions, develop novel theoretical models, and advance the eld of Metaverse adoption in
education. Sixth, the proposed framework suggests that institutions need to consider not only the technical requirements but also the
social, psychological, and motivational aspects of adoption.
6.2. Practical implications
The insights from this review provide valuable guidance for various stakeholders, including educators, administrators, policy-
makers, and technology developers, who implement the Metaverse in educational settings. By identifying critical factors affecting the
Metaverse adoption, this study informs pedagogical approaches and enables educators to optimize the potential of the Metaverse in
enhancing student learning outcomes, engagement, and motivation. The insights gained also facilitate the design and delivery of
professional development programs for educators, ensuring they possess the necessary skills, knowledge, and support to integrate
Metaverse platforms effectively into their teaching practices. The ndings also support policymakers and administrators in developing
informed policies and strategic plans for implementing the Metaverse in educational institutions. This ensures that resources are
allocated effectively and potential challenges are addressed. Additionally, the results of this review assist technology developers in
designing and developing Metaverse platforms, tools, and applications tailored to users’ specic needs, preferences, and expectations
in educational settings. Overall, the practical implications of this systematic review extend across multiple dimensions, enabling
stakeholders to make informed decisions, develop effective strategies, and optimize the potential of Metaverse platforms to transform
teaching and learning experiences.
6.3. Limitations and future work
Although the study provides an extensive compilation of current research on the factors inuencing Metaverse adoption, it is not
without limitations. First, the review focuses on literature published up to December 2022, potentially excluding the most recent
studies on Metaverse adoption in education. Given the rapidly evolving Metaverse environment landscape, it is essential for future
research to continually update the ndings to remain current. Second, the study is based on a systematic literature review, inherently
constrained by the search strategy and the selection criteria. The study included only English publications and peer-reviewed liter-
ature, while non-scientic studies, grey literature, and literature in other languages were excluded. The authors searched specic
databases (Scopus and WoS), which may have omitted potentially important sources of information. To address these limitations,
future research should consider studying articles from indexed and non-indexed journals, literature in other languages, and alternative
databases such as Google Scholar to obtain more extensive evidence on Metaverse adoption in education.
Third, the study relies on systematic literature concentrating on empirical studies with theoretical models and results, thus
excluding conceptual and qualitative studies. Future research should consider incorporating conceptual and qualitative studies to
enhance the understanding of Metaverse adoption in education. Such an approach would enable a deeper exploration of the complex
and dynamic nature of the adoption and use, enriching the existing body of knowledge in the eld. Fourth, this review collected studies
related to Metaverse adoption only and discarded studies exploring Metaverse technical perspectives to meet the research objective of
this review. Consequently, future research might also examine the technical aspects of the Metaverse in education to provide a more
holistic understanding of its implementation, challenges, and potential benets. This approach would contribute to the broader
knowledge base and help guide the development of effective strategies for successfully integrating Metaverse environments in
educational settings.
Funding statement
There is no funding received for this study.
Data availability
The data presented in this study are available on request from the authors.
CRediT authorship contribution statement
Safwan Maghaydah: Writing – original draft, Validation, Resources, Methodology, Investigation, Funding acquisition, Formal
analysis, Data curation, Conceptualization. Mostafa Al-Emran: Writing – review & editing, Supervision, Resources, Project admin-
istration, Formal analysis, Conceptualization. Piyush Maheshwari: Writing – review & editing, Supervision, Formal analysis,
Conceptualization. Mohammed A. Al-Shara: Writing – review & editing, Writing – original draft, Validation, Software, Resources,
Methodology, Investigation, Formal analysis, Data curation.
S. Maghaydah et al.
Heliyon 10 (2024) e28602
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Declaration of competing interest
The authors declare that they have no known competing nancial interests or personal relationships that could have appeared to
inuence the work reported in this paper.
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