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Advancing Education Through Extended Reality and Internet of Everything Enabled Metaverses: Applications, Challenges, and Open Issues
Senthil Kumar Jagatheesaperumal, Kashif Ahmad, Ala Al-Fuqaha, Junaid Qadir
TL;DR
Metaverse education faces challenges in delivering accurate, high-quality content across incompatible XR and IoT devices. This survey reviews educational applications and their technologies, and identifies domain-specific countermeasures for immersive learning.
Problem
Educational content accuracy and authentication are challenged by substandard or incompatible XR and IoT devices, while robust platforms need cross-border interoperability.
Source: §5 · p. 16Method
The survey reviews XR solutions and associated challenges across education, training, and skill enhancement, and synthesizes goals, challenges, and countermeasures for metaverse-based training.
Source: §2.5 · p. 8, §6 · p. 17Results
The survey identifies countermeasures including cloud services and quantum computing for healthcare education, XR and IoE for online interaction, and collaborative robots for industrial skill enhancement.
Source: §2.5 · p. 8Takeaways & Limitations
Achieving an immersive learning platform through the metaverse requires addressing its identified challenges with appropriate countermeasures.
Source: §2.5 · p. 8XR can create false places and circumstances or mind-altering states, motivating standards and policies to support ethical operation and prevent misuse.
Source: §4.5 · p. 15
Abstract
from arXiv · showhide
Metaverse has evolved as one of the popular research agendas that let the users learn, socialize, and collaborate in a networked 3D immersive virtual world. Due to the rich multimedia streaming capability and immersive user experience with high-speed communication, the metaverse is an ideal model for education, training, and skill development tasks. To facilitate research in this area, we provide a comprehensive review of the various educational use cases and explore how enabling technologies such as Extended reality (XR) and Internet of Everything (IoE) will play a major role in educational services in future metaverses. Secondly, we provide an overview of metaverse-based educational applications focusing on education, training, and skill development and analyze the technologies they are built upon. We identify common research problems and future research directions in the domain. The paper also identifies core ethical considerations of metaverse for education and potential pitfalls. We believe this survey can fully demonstrate the versatility of metaverse-driven education, which could serve as a potential guideline for the researchers.
1. Introduction
Metaverse education is gaining momentum through immersive, location-independent learning, but content creation and learning-performance benefits remain challenges. This section introduces XR and IoE, maps the metaverse’s layers, and frames the paper as a survey of educational applications, technologies, challenges, and research directions.
1. Introduction
- Metaverse education offers location-independent virtual meetings and lesson-based landscapes, but creating engaging content is challenging and some studies report little impact on knowledge-test performance.The paper situates education among application domains where metaverse use is gaining momentum.Source: §1 · p. 1
1.1.1. Extended Reality (XR)
- XR integrates VR, AR, and MR to realize a sustainable digital world, with VR creating simulated environments, AR layering digital objects onto the real world, and MR enabling real-time interaction between physical and virtual objects.The paper positions XR and IoE as key technologies at the crossroads of the metaverse.Source: §1.1 · p. 1, §1.1.1 · p. 2
1.1.2. Internet of Everything (IoE)
- IoE infrastructure combines smart things, data, processes, and people, with Web 3.0 and 5G/6G among the technologies discussed for supporting it.Web 3.0 connects metaverse services with decentralized implementation, while 5G/6G services are described as reducing latency and improving bandwidth and speed.Source: §1.1.2 · p. 2
1.2. Layers of the Metaverse
- The metaverse’s layers span immersive social experience and discovery through creator activity, spatial computing, decentralization, human interfaces, and infrastructure.The infrastructure layer includes computational and communication resources, machine intelligence, blockchain, gaming, and display technologies.Source: §1.2 · pp. 2–3
1.3. Scope of the paper
- The paper examines metaverse applications and supporting technologies for education, skill development, and training, emphasizing XR–IoE convergence, limitations, and research trends.It also analyzes current drawbacks and aspects that need research attention.Source: §1.3 · p. 3
1.4. Related Surveys
- Earlier surveys cover metaverse concepts, techniques, applications, and privacy or security, while this review focuses on XR and IoE convergence for educational applications.The paper presents its synthesis from metaverse fundamentals and technical platforms through immersive educational services.Source: §1.4 · pp. 3–4
1.5. Contribution of this Paper
- The paper’s contributions include a tutorial on metaverse technologies and a survey of opportunities, challenges, adaptive educational applications, ethical issues, and future research directions.Its stated application areas include online classrooms, industrial training, aircraft, maritime, military, and gaming.Source: §1.5 · p. 4
2. Metaverse Applications in Education and Training
This section surveys metaverse applications that connect physical and virtual worlds for education, training, and skill development. It reviews classroom and sector-training uses, then synthesizes challenges and possible countermeasures.
2. Metaverse Applications in Education and Training
- The section frames IoE, XR, and supporting technologies as connecting physical and virtual worlds across education, training, and skill-development applications.Figures 3 and 4 illustrate the interconnection and examples of applications.Source: §2 · p. 5, Figure 3 · p. 6, Figure 4 · p. 7
2.1. The Metaverse Classroom
- Metaverse classrooms support location-independent immersive collaboration, teacher-skills practice with avatars, and close inspection of heritage models.The section also describes immersive remote learning and interactive virtual meeting spaces.Source: §2.1 · pp. 5–6
2.2. Industrial Training
- Industrial uses span risk-free employee practice, collaborative factory simulation, robot training with synthetic imagery, twin-enabled planning, and avatar-based apparel fitting.Domain randomization can generate varied photorealistic images for robot training.Source: §2.2 · p. 7
2.3. Aircraft and Maritime Training
- Aircraft and maritime training uses include XR maintenance practice, virtual aircraft twins, and cybersecurity learning through virtual ships and maritime scenarios.Prior work cited in the section reports XR training as less error-prone, less effortful, and less time-consuming.Source: §2.3 · pp. 7–8
2.4. Military Training
- Military metaverse training can simulate varied environments for shooting, tactical behavior, and observation while reducing travel and exercise risks.The section describes virtual scenarios that let trainees respond to conditions and practice target hitting.Source: §2.4 · p. 8
2.5. Discussion and Summary of Lessons Learned
- Table 4 organizes metaverse training objectives, associated challenges, and countermeasures across application domains.The section argues that addressing these challenges is necessary to achieve an immersive learning platform.Source: §2.5 · p. 8
- Proposed responses span multimodal healthcare data, XR/IoE-enabled online collaboration, industrial robots, and aircraft digital twins.The summary also identifies maritime cybersecurity, military scenario handling, and arts and gaming skills as application-specific needs.Source: §2.5 · p. 8
- Maritime cybersecurity safeguards, dynamic military-situation handling, and accurate object recognition for arts and gaming round out the proposed domain responses.These are presented as countermeasures or capabilities for their respective application areas.Source: §2.5 · p. 8
3. Technology Enablers for Metaverse in Education
The section surveys technologies supporting educational metaverses, from immersive XR and connected IoE systems to analytics, machine intelligence, blockchain, low-code development, and distributed networks.
3.1. XR Technologies for Metaverse
- XR defines the immersive experience and shapes hardware and software choices; cloud support can lower networking costs and enable persistent training.XR also supports remote interaction in virtual meetings and is associated with communication, collaboration, critical thinking, and creativity in learning.Source: §3.1 · pp. 8, 11
3.2. IoE Technologies for Metaverse
- IoE device data connect physical and virtual worlds, while coordinated local devices and edge processing are proposed for AI-powered educational content.Digital twins model physical systems and assets; IoE data streams support immersive interconnectivity.Source: §3.2 · p. 11
3.3. Big Data and Predictive Analytics for Metaverse
- Predictive analytics can extract insights from metaverse data, including XR-device data, while next-generation headsets may gather facial-recognition and stress-level information.The section also notes concerns about handling the large volumes of data generated by spatial computing.Source: §3.3 · p. 11
3.4. Machine Intelligence
- Machine intelligence is proposed as a design advisor for educational services, including gesture- and emotion-informed teaching, but developers must advance virtual education and training features.The section describes machine intelligence working with XR and IoE and mentions possible integration with low-code and no-code platforms.Source: §3.4 · pp. 11–12
3.5. Blockchain Adoption
- Blockchain’s decentralization, immutability, and transparency are presented as potential supports for securing educational data and coordinating content through smart contracts.The survey also discusses blockchain for data acquisition, storage, privacy, interoperability, trust, and on-chain educational data feeds.Source: §3.5 · p. 12
3.6. Low-Code Platforms
- Low-code platforms let non-programmers perform tasks that usually require programming expertise, while the educational sector’s reasons for adopting XR and IoE platforms remain underrepresented in the literature.Scaling the technology to individual educational institutions is described as challenging.Source: §3.6 · pp. 12–13
3.7. Accelerating Distributed Networks
- 5G and 6G can accelerate distributed networks, while edge computing may address resource constraints in metaverse platforms, including those using low-performance IoE devices.The section links faster computation to immersive rendering and lower-cost sharing of user-generated educational content.Source: §3.7 · p. 13
4. Challenges and Issues Related to Metaverse
The section maps deployment challenges from trust and platform design to immersive realism and education-specific ethics. It identifies risks involving virtual interactions, XR authenticity, privacy, security, responsible conduct, and ownership.
4.1. Virtual Mainstreaming
- Trust in virtual relationships and organizations is central to mainstreaming, while online abuse, cheating, and cybercrime are identified as risks requiring education, virtual literacy, and supportive communities.The passage names phishing, online fraud, virus distribution, and ransomware among the cybercrimes to address.Source: §4.1 · p. 13
4.2. Challenges by Open Platforms
- Open platforms can distribute immersive experiences beyond app stores, but their growth creates developer challenges; decentralized identities and zero-knowledge proofs are proposed for user data control.The section also points to open standards and free XR- and IoE-supported applications as opportunities for educational services.Source: §4.2 · pp. 13–14
4.3. Walled Garden Ecosystems
- Walled gardens restrict navigation to a provider’s network, but portable avatars, social networks, and interoperability could connect these ecosystems with open platforms.Creators can collaborate on shared components within walled gardens.Source: §4.3 · p. 14
4.4. Simulating Reality
- Real-time ray tracing and AI are presented as ways to render immersive virtual objects, people, places, and machines using data such as digital twins, traffic sensors, and geospatial sources.These features are described as potentially supporting interactive education.Source: §4.4 · p. 14
4.5. Ethical Considerations and Potential Pitfalls
- The ethics discussion frames human wellbeing, responsible infrastructure, and AI as priorities, and Fig 5 presents a taxonomy of ethical issues in metaverse-based education.XR may create false places and circumstances or mind-altering states, prompting calls for standards and policies to help distinguish authentic from virtual experiences.Source: §4.5 · pp. 14–15, Figure 5 · p. 14
- Educational XR privacy concerns include sensitive data collection and persistent blockchain storage, while HMD attacks can disorient users or alter their view without their knowledge.The cited privacy discussion also describes security and privacy attack-tree analysis for social VR learning environments.Source: §4.5 · pp. 14–15
- Digital citizenship and netiquette are presented as safeguards for responsible online conduct, alongside consent and access policies and blockchain-based copyright management for educational content.The section also discusses blockchain approaches to claiming and protecting intellectual-property rights.Source: §4.5 · p. 15
5. Future Research Directions and Open Issues
The survey’s future directions emphasize quality, access, inclusion, pedagogy, and responsible use, while recognizing device and communication constraints in immersive education.
Content quality and connectivity
- Future platforms need common content-authentication mechanisms and reliable broadband, while heterogeneous radio technologies can affect trust in educational communication services.The text also calls for cross-border interoperability across diverse supporting technologies.Source: §5 · p. 16
Marginalized communities
- Metaverse education should address access for marginalized and impaired learners, with fairness policies and explainable AI approaches proposed for identifying vulnerable groups and interpreting model decisions.The section also flags threats to educational content and contextual exploration.Source: §5 · p. 16
Mobile technologies and pedagogy
- Mobile technologies can support immersive, interactive pedagogy across locations, but power, networking, and coverage constraints must be addressed to balance its demands.The section also discusses educator acceptance of AI tools and XAI platforms for pedagogical decisions.Source: §5 · p. 16
Integrated learning and system constraints
- Integrated learning can combine educational content with games and storylines, but realizing its potential depends on addressing device size, power, and resource management.The section says educational applications may prioritize immersive experience over high throughput and high-speed communication while considering device constraints.Source: §5 · pp. 15–16
Responsible usage
- Responsible use remains a concern despite secure communication, with learners, teachers, developers, and service providers all implicated in supporting responsible educational services.The section calls for values alongside security and privacy measures such as robust encryption.Source: §5 · pp. 16–17
6. Conclusions
The conclusion reviews XR and IoE applications for education, training, and skill enhancement through metaverse platforms. It highlights capabilities and open issues for developing immersive education.
Survey scope
- The survey reviews XR solutions and challenges in education, training, and skill enhancement through IoE applications from a metaverse perspective.It also discusses how IoE data are analyzed by digital models to provide abstraction and insight into data streams from IoT devices.Source: §6 · p. 17
Open issues
- The paper identifies open issues for sustaining XR-based training supported through IoE devices and extending services toward immersive education.The conclusion notes that deployment of immersive education was beyond the survey’s scope.Source: §6 · p. 17
Conclusion
- The authors present the survey as a resource for understanding XR and IoE capabilities and future research prospects for metaverse-based training.Source: §6 · p. 17