As virtual reality (VR) becomes increasingly common in education, a significant share of students experience cybersickness during immersive sessions. This paper presents a framework for assessing cybersickness and pre-configuring VR applications for specific educational cohorts before deployment. A two-level experimental methodology with twelve control points was developed to evaluate discomfort across different locomotion and rotation methods. Psychometric analysis confirmed the reliability of the measurement scale and revealed a multidimensional factor structure. A hybrid Mamdani fuzzy model with four linguistic terms was constructed from fourteen discomfort indicators and compared with alternative fuzzy inference approaches and classical machine learning methods; the fuzzy model offered the best balance between interpretability and inferential consistency. Analysis of two student cohorts (IT majors and pedagogy majors) revealed substantial group differences in discomfort perception, with pedagogy students showing markedly higher sensitivity. Based on the model outputs, concrete recommendations were formulated for configuring VR applications to reduce cybersickness, including frame rate stabilisation, limiting jump height, simplifying visual effects, and introducing adaptive onboarding modes. The developed methodology provides a practical diagnostic tool for adapting educational VR software to target user groups.
Educational virtual worlds and metaverse environments are increasingly utilized to foster immersive and experiential learning in higher education. However, their instructional efficacy depends not only on visual fidelity and presence, but fundamentally on how effectively learners’ cognitive resources are managed. This study investigates intrinsic (ICL), extraneous (ECL), and germane cognitive load (GCL) alongside NASA Task Load Index (NASA-TLX) workload dimensions in a desktop-based educational virtual world developed in Spatial.io for an undergraduate Computer Networks course. A cohort of 120 second-year Computer Science students (75 males, 45 females) participated in a five-week intervention designed to model the relationships among cognitive load, perceived workload, and meaningful engagement. A multi-method analytical framework was implemented, combining psychometric validation and Partial Least Squares Structural Equation Modeling (PLS-SEM) with machine learning techniques (K-Means clustering, K-Nearest Neighbors (KNN), Random Forest, Decision Trees) and Psychometric Network Analysis. The findings revealed strong internal consistency across the cognitive load scales, moderate overall workload, and significant positive associations between all three cognitive load dimensions and perceived workload, with extraneous cognitive load emerging as the variable most strongly and consistently linked to high-overload cluster membership, frustration, and reduced engagement across the machine learning and network analyses. These results suggest that desktop-based educational virtual worlds may support self-reported germane cognitive engagement, provided that extraneous interface demands are systematically minimized and navigation is scaffolded. The study contributes an empirical and computational perspective to educational metaverse research, offering practical guidelines for designing cognitively sustainable immersive learning environments.
Parents and primary carers often shape whether Autistic people access, trial, continue, or avoid virtual reality (VR), yet parent-focused evidence on family-level VR adoption remains limited. This exploratory, cross-sectional survey examined parent and primary carer perspectives on VR use, including perceived benefits, concerns, support goals, and intentions to use or continue using VR. Forty-one parents or primary carers of Autistic people aged 3–42 years completed this self-selected online survey. Descriptive statistics, Wilcoxon signed-rank tests, and Kendall’s tau-b correlations were used. Thirty participants reported that their child had previously used VR, mainly through home-based, commercial or general-purpose applications. Participants held favourable views of VR, particularly for controlled, predictable, and self-paced engagement. The clearest concerns related to neurodiversity-affirming design, cost, and screen time. Reported sensory support goals were positively associated with the likelihood of using or continuing to use VR (τb = 0.32, p = 0.02), whereas communication/social interaction and transition-related goals were not. Beyond participants’ favourable evaluations of VR, prior experience and access to guidance and resources were the clearest exploratory correlates of intention to use or continue using VR. These patterns need to be treated as hypothesis-generating, and the findings are specific to this small, self-selected and largely VR-exposed sample. Findings suggest a gap between family-led VR use and implementation support. Because the data reflect parent- and primary-carer-reported intentions to use or continue using VR, they do not establish VR effectiveness or represent Autistic users’ own experiences.
Smart-city infrastructure is increasingly operated through cyber–physical systems that combine embedded sensing, edge computing, communication networks, automation platforms, and digital-twin environments. The emerging industrial metaverse extends this paradigm by enabling immersive, real-time interaction with infrastructure assets, mobile agents, and operational data. Existing metaverse-city discussions, however, often remain visualization-oriented, and the literature offers no unifying architectural framework for integrating geospatial modeling, industrial sensing, distributed synchronization, multi-agent simulation, and human-in-the-loop control into a deployable smart-city platform. This paper presents a design framework and reference architecture for industrial metaverse urban digital twins. The framework has not yet been implemented as a software system; its value lies in a principle-driven design blueprint—layers, responsibilities, inter-layer data flows, and design rules—that is intended to guide future implementations. The architecture organizes the system into five layers: physical infrastructure and sensing, geospatial–semantic modeling, edge/cloud digital-twin services, multi-agent simulation and analytics, and immersive human–machine interaction. It links WGS-84 georeferencing, semantic city models, event-driven synchronization, and latency-aware networking into a unified computational pipeline. Boundary conditions, deployment assumptions, and the integration of legacy devices that do not conform to prevailing standards are stated for each layer, and security and data-governance obligations are assigned per layer. An illustrative smart-city scenario and a proposed evaluation protocol show how future implementations can be assessed under different latency and crowd-density conditions; no empirical results are reported in this Perspective. The protocol defines each metric formally, ranks the metrics by priority, documents the conflicts among them, and states test scenarios with acceptance thresholds. Analytical resource budgets, derived from stated assumptions rather than measured, identify interest management as the decisive scaling mechanism. Deployment challenges related to scalability, synchronization accuracy, cybersecurity, accessibility, cross-domain operation under multiple administrative authorities, and standards-based interoperability are also discussed.
Multifunctional material systems increasingly combine several active phases, including photocatalysts, magnetic phases, adsorbents, controlled-release agents, and antimicrobial oxides, within a single encapsulating matrix. This paper presents an augmented reality and interactive 3D mobile app for visualizing a Multifunctional Hybrid Material (MHM-App) that provides a representation of a multifunctional sphere composed of titanium dioxide (TiO2), magnetite (Fe3O4), activated carbon, hypochlorite-loaded clinoptilolite zeolite, and copper (Cu), encapsulated in a sodium alginate matrix formed by ionic gelation and coated with a crosslinked chitosan shell. The MHM-App provides two complementary visualization modes: an AR mode in which the sphere is anchored to a detected horizontal surface through raycasting, and a non-AR mode in which the same geometry is rendered in a virtual scene with a fixed-perspective camera and explicit lighting. We evaluated the user experience using the User Experience Questionnaire and its short version (UEQ and UEQ-S, respectively), obtaining an excellent overall score in the UEQ-S and in five of six categories of the UEQ (except the novelty category, with a “good” score), confirming that MHM-App is perceived as attractive, understandable, efficient, dependable, and stimulating. The MHM-App integrates augmented reality and interactive 3D visualization, enabling students and technical audiences to explore the spatial coexistence of multiple active phases and their functions within a single material structure, overcoming the limitations of conventional static diagrams and representing a technological approach to support scientific communication, interdisciplinary collaboration, education, and technical dissemination in materials science.
