Rethinking Higher Education/Chapter 8
Virtual Reality and Smart Learning Spaces: Immersive Technologies in Chinese and European Universities
Martin Woesler
Hunan Normal University
Abstract
Immersive technologies — virtual reality (VR), augmented reality (AR), and extended reality (XR) — are transforming higher education from a predominantly text-and-lecture-based enterprise into one that can simulate complex environments, enable experiential learning at scale, and connect students across geographic boundaries. The global VR in education market, valued at USD 14.55 billion in 2023, is projected to reach USD 65.55 billion by 2032, with the Asia Pacific region growing fastest at a compound annual rate of 22 percent. This article provides a systematic comparison of how Chinese and European universities are deploying these technologies. China has developed 215 virtual simulation training bases, launched the iLAB-X platform serving 2,672 universities with over 13 million participants, and won the 2022 UNESCO Prize for ICT in Education for its National Smart Education Platform. European universities have pursued a more distributed approach through Erasmus+ and Horizon-funded projects, with systematic reviews documenting positive learning outcomes across 71 comparative studies and meta-analyses reporting a moderate positive effect size (Hedges’ g = 0.524) for VR-based teacher education. We examine the evidence for learning effectiveness, the emerging Edu-Metaverse concept, infrastructure costs and equity challenges, and the physiological and pedagogical limitations of immersive technologies. We argue that while VR offers genuine pedagogical benefits — particularly for experiential learning in contexts where real-world practice is dangerous, expensive, or logistically impossible — its deployment must be guided by pedagogical purpose rather than technological enthusiasm, and its costs must be weighed against alternative investments in educational quality.
Keywords: virtual reality, smart classrooms, immersive learning, Edu-Metaverse, higher education, China education technology, European universities, VR effectiveness, smart education platform, XR
1. Introduction
The promise of virtual reality in education is as old as VR itself. Since the earliest flight simulators of the 1960s, the intuition that learning by doing — even virtual doing — is superior to learning by reading or listening has driven successive waves of investment in immersive educational technology. What distinguishes the current moment is the convergence of several factors: the dramatic reduction in VR hardware costs, the maturation of software development tools, the COVID-19 pandemic’s normalization of technology-mediated learning, and the entry of both the Chinese government and the European Union as major institutional actors in the deployment of immersive technologies for education.
The global VR in education market reflects this convergence. Valued at USD 14.55 billion in 2023, it is projected to grow to USD 65.55 billion by 2032, representing a compound annual growth rate of 18.2 percent (Fortune Business Insights 2024). The Asia Pacific region is the fastest-growing market, with a projected CAGR of 22.01 percent, driven primarily by Chinese government investment in virtual simulation infrastructure (Mordor Intelligence 2025).
Yet market growth does not automatically translate into educational effectiveness. The history of educational technology is littered with innovations that promised transformation but delivered incremental improvement — or none at all. From the language laboratory of the 1960s to the MOOCs of the 2010s, each wave of educational technology has followed a predictable cycle: enthusiastic adoption driven by techno-optimistic claims, followed by empirical evaluation revealing modest effects, followed by a more measured integration into existing pedagogical practice. VR in education appears to be entering the evaluation phase of this cycle, making this an opportune moment for a comparative assessment.
This article examines the evidence for VR’s pedagogical impact, compares Chinese and European deployment strategies, and assesses the challenges — cost, equity, pedagogy, and health — that both systems must address. Our analysis draws on systematic reviews, meta-analyses, and case studies from both contexts, aiming to move beyond promotional claims toward an evidence-based assessment of what immersive technologies can and cannot contribute to higher education. We organize our analysis around five questions: What VR infrastructure has each system built? What does the evidence say about learning effectiveness? How do the two systems compare in their deployment strategies? What challenges must both address? And what does the future hold — particularly the emerging concept of the Edu-Metaverse?
2. VR in Chinese Universities: Scale and Speed
2.1 The National Virtual Simulation Infrastructure
China‘s approach to VR in education reflects the centralized, state-led model that characterizes its broader digital education strategy. In 2018, the Ministry of Education initiated the National Virtual Simulation Experimental Teaching Project, establishing virtual simulation as a formal category of educational infrastructure alongside traditional laboratories. The 2021 Construction Guidelines for Demonstrative Virtual Simulation Training Bases in Vocational Education set a target of approximately 200 bases; by 2024, 215 had been developed, exceeding the original plan (Ministry of Education 2021).
The flagship platform is iLAB-X, which by December 2022 had integrated laboratories from 2,672 domestic universities with over 13 million participants. The platform hosts 480 virtual simulation experiment courses, of which national and provincial high-quality courses account for 33.5 percent and 35.8 percent respectively (Zhu et al. 2023). Medical education has been a particular focus, reflecting the practical constraint that clinical training requires access to patients and equipment that cannot be scaled through traditional means.
Zhuang, Xu, and Zhang (2025), in a study published in Springer’s Virtual Reality journal, present three case studies from Chinese universities — in telecommunications, civil, and chemical engineering — demonstrating how VR contextualizes abstract theoretical knowledge through simulated environments. The studies show that VR enables situated learning experiences that would be impossible, dangerous, or prohibitively expensive in physical laboratories: students can observe molecular structures from the inside, simulate structural failures without risk, and practice chemical processes without handling hazardous materials.
The „Golden Course“ initiative, proposed by the Ministry of Education in 2018 as one of five course types for quality improvement, has further institutionalized virtual simulation. Wang and colleagues (2023) document the Green Logistics Virtual Simulation Experiment as a case study, demonstrating how virtual simulation addresses practical training limitations including high costs, safety risks, and limited access to real-world logistics facilities.
2.2 The Emerging Edu-Metaverse
Chinese institutions have moved beyond standalone VR applications toward a more comprehensive vision: the Edu-Metaverse. A 2025 study in Interactive Learning Environments proposes a three-layered Edu-Metaverse ecosystem model — hardware, software, and application layers — within a socio-ecological context, reviewing China‘s Edu-Metaverse development across seven aspects. Zhang and colleagues (2022), in an earlier IEEE publication, identified the key technological enablers — digital twins, 5G networks, and AI — for integrating teachers, learners, resources, and teaching environments into a unified immersive ecosystem.
Gray (2025), in an analysis of China‘s national policy agenda for extended reality, documents the strategic importance that Chinese policymakers attach to XR development. The metaverse is not merely an educational experiment but a component of China’s broader technology strategy, with implications for industrial training, cultural heritage preservation, and international soft power.
2.3 The Smart Education Platform
China‘s most recognized achievement in digital education is the National Smart Education Platform, which won the 2022 UNESCO King Hamad Bin Isa Al-Khalifa Prize for ICT in Education. Launched on 28 March 2020 in response to the COVID-19 pandemic, the platform covers basic, vocational, and higher education, with 13.15 million registered users, 27,000 MOOCs for higher education, and training for over 10 million teachers (UNESCO 2023). During the first quarter of 2020 alone, over 950,000 teachers from 1,454 universities taught 942,000 online courses, attracting 1.18 billion student registrations (Xiong et al. 2021).
The platform’s smart classroom component has been the subject of empirical research on learning outcomes. A 2026 study in Acta Psychologica examines the relationship between physical immersive smart-classroom environments and technology-enhanced academic performance among Chinese undergraduates, finding that smart-classroom environments directly predict academic performance and that teacher-directed AI scaffolding boosts the relationship between learning enjoyment and performance outcomes.
3. VR in European Universities: Distributed Innovation
3.1 EU-Funded Projects
The European approach to VR in education is characteristically distributed, operating through competitive funding mechanisms rather than centralized mandates. The Digital Education Action Plan 2021–2027 provides the strategic framework, with immersive technologies identified as part of the broader digital education strategy. The EU’s 2025 report on Virtual Worlds and health and well-being documents that VR transforms education through increased emotional and cognitive engagement, while identifying challenges including cybersickness, eye strain, and accessibility concerns (European Commission 2025).
Several EU-funded projects illustrate the European approach. The VR-intense project (Erasmus+, launched September 2024, EUR 400,000) at Paderborn University develops inclusive VR environments for higher education, with specific attention to accessibility for students with disabilities (Beutner and Schneider 2024). The VReduMED project (Interreg Central Europe) brings together institutions from the Czech Republic, Austria, Slovakia, Hungary, and Germany to develop VR applications for nursing and medical education. The XR4ED platform (Horizon-funded) enables educators to build XR teaching experiences without programming or 3D modeling expertise, including a marketplace for 3D models, avatars, and collaborative VR channels (Liarokapis et al. 2024).
These projects reflect the EU’s emphasis on transnational collaboration, accessibility, and pedagogical innovation. Unlike China‘s centralized platform approach, European VR in education emerges from a competitive ecosystem of research groups, technology companies, and educational institutions, each pursuing distinct approaches within a common strategic framework.
The scale difference is significant. While China‘s iLAB-X integrates 2,672 universities on a single platform, no European initiative approaches this scope. The EU’s strength lies in the quality and rigor of individual projects rather than system-wide deployment — a pattern consistent with the broader comparison of European and Chinese approaches to digital education documented throughout this anthology.
3.2 Evidence of Effectiveness
The European research community has produced substantial evidence on VR’s pedagogical effectiveness. A systematic review published in Computers and Education (2024) analyzed 71 comparative studies of virtual versus traditional learning in higher education. The review found that 67 percent used quantitative methods, over half involved undergraduates (61 percent), and most focused on STEM disciplines, particularly health sciences (45 percent). VR solutions were predominantly immersive (63 percent), interactive (59 percent), and single-user (92 percent). A critical finding was that interactivity — not immersiveness — emerged as the crucial success factor: VR applications that allowed students to manipulate objects and make decisions outperformed those that merely presented immersive visual environments.
Han and colleagues (2025), in a meta-analysis of 52 empirical studies on VR in teacher education, report a positive moderate overall effect with a Hedges’ g of 0.524, with significant variations based on immersion level, equipment type, and learning objectives. Yang and colleagues (2024), in a meta-analysis of VR’s impact on practical skills in science and engineering education, analyzed 37 studies and found a significant moderate positive effect (g = 0.477), with medical students showing the largest improvement.
Cabrera-Duffaut, Pinto-Llorente, and Iglesias-Rodriguez (2024) argue that VR’s value extends beyond knowledge transfer to competency development — the capacity to apply knowledge in practical contexts. Their systematic review finds that VR facilitates the development of procedural skills, spatial reasoning, and collaborative problem-solving in ways that traditional instruction cannot replicate. However, they also document persistent challenges: high costs of VR technology, lack of specialized educational software, and limited accessibility for institutions with constrained budgets.
4. Comparative Analysis: China-Europe Differences
4.1 Institutional Architecture
The most fundamental difference between Chinese and European VR deployment lies in institutional architecture. China‘s top-down approach enables rapid scaling: the transition from policy announcement to 215 virtual simulation training bases took approximately three years. The iLAB-X platform’s integration of 2,672 universities on a single infrastructure would be logistically impossible in the EU’s decentralized system. Xu and colleagues (2024), in a study of Chinese college students’ willingness to continue using virtual simulation learning systems, find that perceived value and teacher recommendations significantly influence adoption — suggesting that institutional mandates and pedagogical integration are mutually reinforcing.
Europe’s distributed approach, by contrast, generates diversity and innovation but at slower scale. The multiplicity of EU-funded projects — each with distinct objectives, partners, and methodologies — creates a rich experimental landscape but also fragmentation. There is no European equivalent of iLAB-X: a single platform integrating virtual simulation resources across hundreds of institutions.
4.2 Disciplinary Focus
Both systems concentrate VR deployment in disciplines where the pedagogical case is strongest. Medical and health sciences education is the leading domain in both contexts, reflecting the universal constraint that clinical training requires access to patients, equipment, and procedures that cannot be scaled through traditional means. Engineering and natural sciences follow closely, with VR enabling visualization of processes that are invisible (molecular structures), dangerous (chemical reactions), or impossible to replicate in physical laboratories (geological formations, astronomical phenomena).
4.2 Cross-Cultural Comparison: The China-Spain Study
The China-Spain comparison by Fernandez-Batanero and colleagues (2023), published in Computers and Education: Artificial Intelligence, provides the most direct cross-cultural evidence available. Surveying 20 teachers per university, the study finds that metaverse use in both countries is in an initial experimentation phase, with Chinese respondents showing greater optimism about its potential for international student connection (100 percent agreement) compared to their Spanish counterparts (90 percent agreement). Faculty training and facilities remain limited in both contexts — a finding that suggests the barriers to VR adoption are as much human and organizational as they are technological.
The study reveals a telling asymmetry: Chinese universities have invested more in VR infrastructure, but Chinese and Spanish faculty report similar levels of uncertainty about pedagogical best practices. Hardware deployment, in other words, has outpaced pedagogical development in both contexts, though at different scales. This finding resonates with the broader pattern documented in the digital literacy chapter (Woesler, this volume): infrastructure investment does not automatically translate into educational effectiveness.
4.3 Disciplinary Focus
Both systems concentrate VR deployment in disciplines where the pedagogical case is strongest. Medical and health sciences education is the leading domain in both contexts, reflecting the universal constraint that clinical training requires access to patients, equipment, and procedures that cannot be scaled through traditional means. Zhu and colleagues’ (2023) analysis of the iLAB-X platform confirms that medical virtual simulation constitutes the largest single category of courses, with 480 courses constructed by December 2022.
Engineering and natural sciences follow closely, with VR enabling visualization of processes that are invisible (molecular structures), dangerous (chemical reactions), or impossible to replicate in physical laboratories (geological formations, astronomical phenomena). The humanities and social sciences remain underrepresented in VR education, reflecting both the difficulty of simulating interpretive and discursive learning activities and the disciplinary culture of fields that have historically been less technology-intensive.
4.4 Learning Outcomes: What the Evidence Shows
The meta-analytic evidence for VR’s effectiveness is consistently positive but moderate. Han and colleagues’ (2025) meta-analysis of teacher education reports Hedges’ g = 0.524; Yang and colleagues’ (2024) meta-analysis of practical skills in STEM reports g = 0.477. These are meaningful effect sizes — roughly equivalent to moving a student from the 50th to the 70th percentile — but they do not justify the transformative claims sometimes made for VR in education.
Critically, the effect sizes are moderated by several factors. Immersion level, equipment type, and learning objectives all influence outcomes. Interactive VR applications consistently outperform passive ones. Short, focused VR experiences integrated into broader pedagogical sequences outperform extended VR sessions used as standalone instruction. And the quality of pedagogical design — the alignment of VR activities with learning objectives and assessment — matters more than the technical sophistication of the VR environment itself.
5. Challenges: Cost, Equity, Pedagogy, and Health
5.1 Infrastructure Costs and the Equity Question
VR deployment in education carries significant costs. Industry estimates suggest that a VR lab for a mid-sized college classroom of 20–25 students requires an investment of USD 20,000 to USD 80,000, depending on hardware, software modules, and infrastructure (IXR Labs 2025). A full metaversity digital twin campus averages approximately USD 50,000. These costs are manageable for well-resourced institutions but prohibitive for many, creating the risk that VR will widen rather than narrow educational inequalities.
In China, the government’s centralized investment mitigates this risk for institutions within the national system, but rural and smaller institutions may still lack the technical support and pedagogical expertise needed to use VR effectively. In Europe, the inter-state variation in digital infrastructure documented in the State of the Digital Decade 2025 report (see Digital Natives chapter, this volume) means that VR deployment is concentrated in wealthier member states and institutions, potentially exacerbating the digital divide that DigComp 2.2 was designed to address.
5.2 Pedagogical Effectiveness: Beyond the Hype
The evidence reviewed in this article supports a moderate positive effect of VR on learning outcomes (Hedges’ g = 0.477–0.524), but the effect is neither universal nor unconditional. The systematic review of 71 studies identified interactivity as the crucial success factor: passive VR experiences that merely present immersive visuals do not outperform traditional instruction in a statistically significant way. This finding has important implications for VR procurement and curriculum design: institutions that invest in VR hardware without corresponding investment in interactive software design and pedagogical integration are unlikely to see meaningful learning gains.
Makela, Harley, and MacArthur (2025), in a CHI 2025 study of large-scale VR deployment in a university design class (30 headsets, 55 students, 12 weeks), report highly positive student engagement but also document the practical challenges of classroom-scale VR: instructors must adapt to in-VR lecturing, safety measures are needed to prevent students from colliding with furniture, and cybersickness must be actively managed.
The widely cited claim that VR-trained learners retain 80 percent of material after one year compared to 20 percent for traditional instruction warrants scrutiny. The PwC (2022) study, which is the most frequently cited source for VR training effectiveness, measured speed of completion (4 times faster than classroom), emotional connection (3.75 times more connected to content), and confidence (275 percent more ready to apply skills). The specific retention figures appear in derivative industry sources rather than the PwC study itself and should be treated as indicative rather than definitive.
5.3 Health and Wellbeing
The physiological effects of VR use present a persistent challenge. Cybersickness — a form of motion sickness triggered by visual-vestibular conflict in immersive environments — affects a significant proportion of users, with symptoms including nausea, disorientation, and headache. The European Commission’s 2025 report on Virtual Worlds and health specifically identifies cybersickness and eye strain as concerns requiring management. Soltani and Rostami (2025), in an ACM study, document that VR systems are constrained by high costs, usability issues including cybersickness, and significant cognitive demands that can negatively impact learning quality.
These health concerns are particularly relevant for extended VR sessions in educational settings. Most studies recommend limiting continuous VR use to 20–30 minutes, which constrains the types of educational activities that can be effectively delivered through VR. The implication is that VR is best deployed as a complement to traditional instruction — for specific, high-value activities where the experiential dimension is pedagogically essential — rather than as a wholesale replacement for classroom teaching.
5.4 The Teacher Training Gap
A persistent finding across both Chinese and European studies is the gap between VR technology availability and teacher preparedness. The Fernandez-Batanero et al. (2023) study documents limited faculty training in both China and Spain. Xu, Zou, and Zhou (2024) find that teacher recommendations significantly influence Chinese students’ willingness to use VR — implying that teachers who are uncertain about VR’s pedagogical value transmit that uncertainty to students. The VReduMED project’s emphasis on Train-the-Trainer workshops reflects the European recognition that technology deployment without teacher preparation is investment wasted.
This finding connects to the broader AI literacy challenge documented in the companion chapters: neither hardware nor software nor content alone determines educational outcomes. The human element — teacher expertise, pedagogical design, institutional support — remains the critical variable.
6. Conclusion
The comparison of Chinese and European approaches to VR in education reveals a characteristic pattern that recurs across the themes of this anthology: China deploys at scale and speed through centralized investment and institutional mandates; Europe innovates through distributed, competitive funding and produces rigorous evidence of effectiveness. China’s 215 virtual simulation training bases, the iLAB-X platform serving 13 million participants, and the UNESCO-recognized Smart Education Platform demonstrate what centralized coordination can achieve. Europe’s systematic reviews, meta-analyses, and pedagogically innovative projects demonstrate the value of evidence-based development and attention to equity, accessibility, and health.
Neither approach is sufficient alone. China‘s scale advantage is undermined if VR is deployed without the interactive pedagogical design that the evidence identifies as the critical success factor. Europe’s evidence advantage is undermined if the insights from systematic reviews and meta-analyses remain confined to research publications rather than informing large-scale deployment. The most promising path forward combines Chinese scale with European rigor: deploying VR at the infrastructure level while ensuring that each deployment is grounded in evidence about what works, for whom, and under what conditions.
Several practical recommendations emerge from this comparison. First, VR investment should be preceded by pedagogical needs assessment: which learning objectives genuinely require immersive, experiential engagement, and which are better served by less costly means? Second, teacher training must accompany — and ideally precede — hardware deployment. Third, VR should be deployed as a complement to traditional instruction, not a replacement: the evidence supports short, focused, interactive VR activities integrated into broader pedagogical sequences. Fourth, equity considerations must be central: if VR widens the gap between well-resourced and under-resourced institutions, its net contribution to educational quality is negative. Fifth, health monitoring should be standard practice: cybersickness screening, session duration limits, and regular breaks are essential safeguards.
The emerging Edu-Metaverse concept represents both the greatest opportunity and the greatest risk. If the metaverse in education means creating genuinely interactive, collaborative learning environments that transcend the limitations of physical space and geography — enabling a Chinese engineering student and a German counterpart to collaborate on a virtual bridge design, for instance — then the investment is justified. If it means replacing effective pedagogies with technologically impressive but pedagogically shallow experiences, the investment is wasted. The evidence reviewed in this article suggests that the difference between these outcomes lies not in the technology itself but in the pedagogical intentionality with which it is deployed — a finding that connects directly to the companion chapters on AI ethics, digital literacy, and the university of the future (Woesler, this volume).
Acknowledgments
This research was conducted within the framework of the Jean Monnet Centre of Excellence „EUSC-DEC“ (EU Grant 101126782, 2023–2026). The author thanks the members of Research Group 4 (Technology and Innovation in Education) for their contributions to the comparative analysis.
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