Conventional synchronous online education may be less engaging than face-to-face instruction in physiotherapy education, particularly for practice-oriented content. Metaverse-based environments may offer a more interactive alternative, but comparative evidence in undergraduate physiotherapy students remains limited. This study compared metaverse-based, synchronous online, and face-to-face education for the theoretical component of palpation-related content. This randomized three-arm controlled trial used a pre-test/post-test design. Ninety undergraduate physiotherapy students were randomized to metaverse-based, synchronous online, or face-to-face education; 83 completed the study (metaverse n = 28, online n = 26, face-to-face n = 29). All groups received the same theoretical palpation content, differing only in delivery mode. Motivation was assessed using the Instructional Materials Motivation Survey (IMMS), including its Attention-Relevance and Confidence-Satisfaction subscales, and academic achievement was assessed using a 15-item multiple-choice quiz after the intervention. Motivation increased in all groups after instruction. Significant group-by-time interactions were found for IMMS total (p = 0.001) and Attention-Relevance (p < 0.001), with greater improvement in the metaverse group than in the online group for IMMS total, and greater improvement than both the online and face-to-face groups for Attention-Relevance. Quiz scores also differed across groups (p = 0.048), with significantly higher post-test quiz scores in the metaverse group than in the online group. No significant group-by-time interaction was observed for Confidence-Satisfaction (p > 0.05). Metaverse-based education may provide motivational advantages and higher post-test quiz performance over conventional synchronous online education in undergraduate physiotherapy students. Its clearest benefit was observed in the Attention-Relevance dimension of motivation toward the instructional materials. These findings suggest that more interactive virtual learning environments may enrich online physiotherapy education when theoretical content is delivered remotely.

Cooling feedback has been shown to improve user experience in virtual reality. However, many existing methods face limitations, such as requiring skin contact, relying on airflow, having bulky components, or delivering unstable sensations. These issues limit their applicability to wearable systems, especially in scenarios demanding high responsiveness and perceptual consistency. We propose ChillFactor, a highly responsive, non-contact cooling interface for head-mounted displays (HMDs). It uses ultrasonic atomization of a predetermined alcohol solution to generate evaporative cooling near the skin, reducing airflow-induced artifacts and providing relatively consistent cold sensations. To validate the design, we first evaluated the system's cooling performance. At maximum output, it lowered skin temperature by approximately 2.6 °C within five seconds, a rate sufficient to induce an almost immediate perception of cold. We subsequently integrated ChillFactor into an HMD and conducted a user study with 12 participants in immersive VR scenarios. Participants reported perceiving the cold within 0.66 seconds and noted significantly higher levels of presence and realism compared to conditions without cooling feedback, demonstrating the system's effectiveness in enhancing immersion through rapid and adjustable thermal cues. This work provides a practical foundation for non-contact cooling feedback and expands the design space for multisensory interaction in virtual environments.

Fear plays a crucial role in human behavior and psychological responses. However, accurately quantifying its intensity remains challenging. To address this, we proposed a labeling paradigm in virtual environments: while watching immersive VR videos, users continuously pressed the VR controller trigger to map their subjective fear intensity to a real-time continuous label. Immersive VR videos were utilized as the experimental scenes because they represent a controllable and easily standardized intervention in exposure therapy. To enhance labeling accuracy, haptic vibration cues were provided at preset trigger depth thresholds, serving as physical anchors that allowed subjects to distinguish between fear levels without visual confirmation. Building on this, we designed CTFear, which combined parallel convolutional neural networks with spatial and temporal Transformers and introduced a topology-aware spatial positional encoding to integrate cross-electrode information. Experimental results showed that CTFear achieved average F1 scores of 0.86,0.76, and 0.67 for the two-class, three-class, and four-class classification of fear intensity in cross-trial validation, respectively, and 0.80, 0.64, and 0.55 in cross-subject validation, outperforming various existing methods in multiple classification of fear intensity tasks. This indicated our method can effectively drive EEG decoding of fear intensity and offers a viable pathway for emotion monitoring and interaction in virtual environment.

Remote collaboration systems based on physical environments face several critical challenges, including data-heavy virtual representations and high latencies during data acquisition, reconstruction, rendering, and transmission. Existing approaches often suffer from significant latency, making them unsuitable for real-time collaboration, rely on static scenes that limit interaction, and require multiple specialized hardware, restricting accessibility. To address these challenges, we present Collaborative Interactive Dynamic Environments for eXtended Reality (CIDER)—the first eXtended Reality (XR) platform to integrate Mixed Reality (MR) for co-located users and Virtual Reality (VR) for remote participants through a fully automated pipeline that replicates entire physical environments. CIDER dynamically transforms a user’s physical space into an interactive virtual environment, shareable with remote collaborators within seconds. It employs an efficient approach to represent, render, distribute, and synchronize virtual scenes, achieving interaction latencies of 0.22 seconds, about 10 times lower than comparable systems (2.4 seconds). We evaluate CIDER’s performance quantitatively with collaboration-oriented metrics in scenarios where participants are separated by up to 12,000 km. We also conducted a questionnaire-based user study with 17 participants to evaluate usability and overall user experience. Furthermore, CIDER allows collaborators to participate using a broad range of devices, including personal computers (via Unity emulators, functioning similarly to a MR/VR device), MR devices (e.g., HoloLens 2), and VR devices (e.g., Meta Quest 2 and 3), enhancing accessibility and usability for diverse user groups.

ABSTRACT The metaverse and artificial intelligence (AI) are increasingly intersecting in educational contexts, yet limited empirical research has examined how generative AI avatars function within socially interactive virtual environments. This study investigates the deployment of generative AI avatars within a socio-educational metaverse environment. Using a developmental evaluation approach, data were collected through interviews with seven institutional stakeholders, teacher-generated reflections, internal documentation, embedded user feedback captured through in-platform reporting tools, and longitudinal field memos across an iterative deployment cycle. Findings indicate that the transition from scripted NPCs to generative AI avatars recalibrated users’ attribution of agency, intensified dialogic unpredictability, and elevated social realism beyond visual fidelity. Voice-mediated interaction emerged as a threshold mechanism for co-presence, while algorithmic improvisation exposed tensions between pedagogical intent and stochastic response generation. The deployment further revealed affective frictions, expectation misalignments, and the mediating role of AI literacy in shaping trust, participation, and interpretive coherence. Overall, the study advances a sociotechnical understanding of AI avatars as co-constructors of meaning and interaction, offering implications for the design, implementation, and governance of future AI-enhanced metaverse learning environments.

Collective decision making is a fundamental aspect of group behavior in both animals and humans, and often involves reaching a consensus on the best of n options, using empirical evidence. Although many parallels have been drawn between human and animal collective decisions, collective human behavior is rarely studied in the type of embodied scenarios that animals are often faced with. In this study, we placed human groups in a virtual setup similar to nest site selection in social animals, in which they explored a shared environment and reached a consensus based on their observations of empirical features. In groups of up to 10, participants had to reach consensus on the empirically largest of four candidate sites without verbal communication, instead using movement-based interactions in a custom-developed 3D virtual environment for online multi-participant experiments. The results showed that the speed and accuracy of consensus was importantly modulated by perceptual difficulty and information availability, but that no speed–accuracy trade-off was present. Participants attempted to reach consensus on the empirically largest site by flexibly adapting their use of social information to perceptual difficulty, their spatial position, and the time already spent supporting some option. When a minority of informed individuals were present, these individuals exercised greater independence and influenced the group to faster and more accurate consensus. These results extend previous findings on social decision making strategies in humans to nonverbal scenarios akin to those of social insects.

This presentation explores the operational framework of Distance Education (DE) within the field of Adult Education, focusing on the principles of Andragogy and the necessity for flexible learning environments. It analyzes the transition from traditional instruction to digital convergence, the significance of self-directed learning, and the role of the instructor as a facilitator. Models of synchronous and asynchronous e-learning are examined, alongside challenges of digital readiness and strategies to enhance interaction in virtual learning environments.

This is a thorough literature review analysis of the moral dilemmas surrounding sociovirtualization and striking a balance between social interaction and privacy in virtual environments. By providing digital identities, virtual societies, and virtual experiences, sociovirtual sites alter social relationships and raise difficult moral dilemmas. While social contact issues focus on authenticity, access, emotional well-being, and digital divisions, privacy issues involve data collection, informed permission, anonymity, and unequal powers. Case studies, ethical theories, and governance models are used to investigate conflicts between the preservation of privacy and the promotion of authentic social connection. Important conclusions include the interdependence of privacy and social connection, the necessity of transparency, user-centric design, and regulatory involvement. The article's conclusion highlights the need of interdisciplinary cooperation in creating sociovirtual environments that uphold individual rights and encourage inclusive, sincere social interaction. Future research should focus on developing policies, bridging digital divides, and emerging technology.

This study examines the limitations of efficiency-focused AI education and reinterprets the value of Error-Based Learning (EBL) in multimodal environments. While widespread AI tutoring systems optimize for correctness, this &quotcorrectness obsession&quot constrains learners' metacognitive development and creative thinking. Analysis of four large-scale datasets—OULAD (n=32,593), MIT Open Learning, Stanford AI Index, and UCLA UCUES—using OLS regression (R²=0.542, p<0.001) revealed AI integration increased Virtual Learning Environment interactions by 42.3% and raised course completion by 12.7 percentage points. However, applying a 15% optimal error rate simulation grounded in productive failure theory improved learning outcomes an additional 8.4 points beyond AI-only gains. Notably, creativity indices (strategic diversity, unstructured attempts, retry rates) increased by 58%, while NASA-TLX mental demand paradoxically decreased by 28.4%. Post-error retry rates rose to 67%, suggesting appropriate error experiences simultaneously enhance cognitive efficiency and higher- order thinking. This study proposes the MERGE framework (Metacognition, Engagement, Regulation, Growth, Ethics), integrating pedagogy, HCI, and data science, demonstrating practical applicability with a 121.7% educational ROI. These findings present a paradigm that preserves human creativity and diversity beyond AI-centric uniformity.

Quality medical training is vital for effective healthcare worldwide. In low- and middle-income countries (LMICs), traditional training methods often face significant challenges, including limited resources, logistical barriers, and difficulties in safely replicating high-risk scenarios for infectious diseases like COVID-19 and Ebola. Additionally, medical training demands high costs, significant time, and specialized supervision, limiting its accessibility. Although virtual reality (VR) offers promising solutions to these problems, most evidence comes from high-income settings, leaving limited guidance on implementation in resource-constrained settings. We developed SomaVR, a low-cost VR platform and implementation framework for medical training in LMICs. Built with Unity3D, 'SomaVR' (soma - Swahili/Luganda for "to learn") integrates 360-degree and interactive virtual environments to create customizable training experiences aligned with specific curricula needs. Beyond the software, the framework provides a structured approach covering hardware selection, software architecture, content development workflows, and strategies for local capacity building. The platform prioritizes cross-platform compatibility, offline functionality, and cost-effective deployment. SomaVR's modular components support both high-end VR systems and low-cost solutions such as smartphone-based. The platform and framework were validated through two independent case studies: 1. COVID-19 infection prevention; and 2. Surgical training. In the surgical training, trainers from a high-income country guided Ugandan learners remotely, illustrating SomaVR's potential for long-distance knowledge exchange. In both cases, cohorts trained using SomaVR consistently outperformed those receiving conventional training, with significant improvements in procedural understanding and user engagement. Our findings also highlight that as VR technology costs decline, frugal approaches such as delivering 360-degree video via smartphone can maintain educational effectiveness in low-resource environments. This paper provides a practical blueprint for developing and implementing sustainable VR medical training platforms in resource-limited settings. By detailing the technical framework, development processes, and implementation strategies of SomaVR, we offer a replicable model for institutions seeking to leverage VR technology for medical education in LMICs.

The relevance of the study is due to the growing role of digital communication in the professional training of future specialists and the need to develop skills for effective interaction in virtual environments. The aim of the work is to identify the features of the formation of key components of students’ digital communication and determine the factors that influence the quality of their interaction in online formats. The object of the study is the digital communication skills of future specialists, and the subject is their emotional, cognitive-perceptual and behavioral manifestations in digital environments. The research methodology is based on a structured short questionnaire and the assessment of real behavioral indicators (average reaction time, number of initiated messages, frequency of participation in discussions), which allowed combining subjective and objective data. The results of the study showed uneven development of individual components: the highest values were demonstrated by social-perceptual sensitivity (M = 4.17) and general digital communicative competence (M = 4.09), while emotional regulation (M = 3.84), behavioral activity (M = 9.32) and digital reactivity (M = 12.46 sec.) revealed lower or more variable values. The initiative of students (M = 14.73) significantly exceeds the level of their stable participation in team interactions, which indicates the predominance of impulsive forms of communication over regular ones. Based on the data obtained, a system of practical recommendations was formed aimed at developing emotional self-regulation, increasing behavioral stability, optimizing the pace of digital interaction and creating supportive communication practices in digital learning environments. The results of the study have practical significance for the development of educational programs aimed at developing the digital communicative competence of future specialists. Keywords: digital communication, socio-psychological factors, emotional regulation, social-perceptual sensitivity, digital reactivity, behavioral activity, initiative, communicative competence, virtual educational environment, digital platforms

We study the dynamics of user-agent interactions in virtual environments through mathematical models derived in a Lagrangian formalism. The goal is to optimize the number of agents required to solve a generic predefined task while minimizing the computational cost to achieve it. Specifically, we derived Euler-Lagrange equationsof motion to describe the different ways in which agents may interact with users. The main advantage of this approach lies in its peculiarity to derive a scaling law for any interaction rule one wishes to design, ensuring that the system scales while optimizing the global impact of the interactions. By specifying an action functional over interaction trajectories and enforcing its minimization, the optimal way to allocate agents emerges naturally as Euler-Lagrange equations, embedding the entire optimization strategy directly into the formalism. This intrinsic coupling of dynamics and cost yields a universal procedure to compute the scaling laws for arbitrary interaction rules, ensuring that agent populations and computational expense grow according with environmental complexity. We illustrate the methodology with two specific example. These results also offer a foundation for further research into multi-agent dynamics and their applications in virtual environments.

All-round development, including intellectual, social, physical, ethical, and aesthetic skills, is essential for student growth. Traditional classrooms, using textbooks and direct interaction, often lack immersive experiences. The Metaverse offers interactive virtual environments to enhance these skills, but comparative studies are limited. To compare traditional and metaverse-based education impacts on students' all-round development and provide insights for educators. A comparative study involved 200 university students randomly assigned to traditional or metaverse-based courses (Chinese, Ethics, Art, Physical Education). Outcomes were assessed via short tests and a 45-item Likert-scale questionnaire. Data were analyzed using SmartPLS 4, IBM SPSS Amos 28, Mann-Whitney U tests, and partial least squares regression. Metaverse-based education was associated with significantly higher scores than traditional methods in intellectual, ethical, and aesthetic skills (p < 0.05), due to immersive simulations. Traditional education was associated with significantly higher scores for social and physical skills (p < 0.05), driven by direct interaction and hands-on practice. The model explained 61.4%-76.8% of skill variance. Metaverse education excels in intellectual, ethical, and aesthetic skills, while traditional methods enhance social and physical skills. A hybrid approach is recommended to optimize all-round development.

Purpose This study aims to investigate how different experiential dimensions influence metaverse advertising effectiveness, focusing on the mediating role of avatar parasocial relationships (APRs) and the moderating effect of brand involvement. Design/methodology/approach Drawing on Pine and Gilmore's (1998) and Schmitt's (1999) frameworks, a conceptual model was tested using data from 300 Zepeto users who engaged with a branded virtual automobile experience. Structural equation modeling and moderation analysis were conducted. Findings All five experiential dimensions – entertainment, education, esthetics, escapism and relate – positively influenced APRs, which mediated effects on brand comprehension, attitude and S-WOM intention. The “relate” experience showed the strongest impact. Brand involvement significantly moderated the influence of APRs on outcomes. Originality/value This study extends experiential marketing theory by highlighting the importance of social (i.e. relate) experiences and avatar-based interactions in virtual environments.

This study examines whether auditory perception in audio-visual interactive virtual environments (IVE) is affected by the subjects’ awareness of being in a room with different physical properties and acoustic characteristics compared to the visually rendered room. This could potentially lead to the room divergence effect (RDE). We investigated whether ratings of room-acoustic attributes made within an audio-visual IVE were influenced by the room in which the IVE was used, i.e., the playback room, which was either congruent or divergent from the visually rendered room. The divergent room differed in terms of size, acoustic characteristics, and visual appearance. Participants rated room-acoustic attributes for different head-tracked binaural auralisations. The results show that for several room-acoustic attributes, similar ratings were obtained for both rooms, i.e., we found no effect of the room in which the listening test was performed. The only exception was the perceived source distance, where a small but statistically significant underestimation was found in the divergent room compared to the congruent room. Given the small difference in median scores, this effect may, however, be considered low in relevance. We conclude that audio-visual IVEs can create a convincing illusion of the room that resists influence from the playback room.

&lt;div&gt; &lt;div&gt; &lt;span&gt;Beyond the water cooler: Leading multicultural teams and navigating challenges in a digital workplace—A perspective of SMEs in Singapore&lt;/span&gt; &lt;/div&gt; &lt;/div&gt;

Addressing the common challenges in online education, where the body is present but the mind is disengaged and deep learning is limited, it is crucial to examine how social interactions in virtual environments translate into intrinsic cognitive motivation. This study aimed to explore the mechanism linking social presence (SP) and cognitive engagement (CE) in online learning environments, with perceived interactivity (PI) introduced as a key variable for empirical testing. A total of 500 university students with online learning experience were recruited. Data were collected using the Community of Inquiry scale, the Online Learning Engagement scale, and the PI scale. Correlation and hierarchical regression analyses were conducted using SPSS software. Results indicated that SP positively and robustly predicted CE (β = 0.582, p < 0.001). This finding suggests that, in physically separated learning contexts, learners’ perceptions of interpersonal authenticity and group belonging effectively predict their participation in higher-order cognitive activities. PI was highly correlated with SP but did not show a significant moderating effect between SP and CE. The study confirmed that high-quality interactive experiences and SP were essential conditions for fostering deep learning online. Enhancing multidimensional interaction design to strengthen learners’ SP is a viable strategy to promote deep cognitive processing and improve the quality of online instruction.

VIEW -A Virtual Interactive Web-based Learning Environment for Engineering

The article presents a comprehensive analysis of developing multimodal interface technologies, focusing on force feedback systems for human-machine interaction in robotics, teleoperation, and virtual reality environments; emphasizes the significance of multimodality in reducing the operator’s cognitive load and enhancing the presence in remote or virtual settings. The work focuses on critical ergonomic challenges, including psychophysiological stresses, perception difficulties, latency issues, and safety concerns; reviews current methodologies and technologies, such as adaptive control algorithms, novel tactile devices, and their applications in medical simulators and VR systems. The authors identify key obstacles hindering widespread adoption (latency, hardware limitations), along with promising directions for advancement, including artificial intelligence integration, hybrid multimodal system creation, and energy-efficient interface development utilizing smart materials. The ultimate design goal is to develop an intuitive, safe, and physically comfortable interface seamlessly integrated into the operator’s sensorimotor loop.

At present, animation generation and multi-modal interaction in virtual reality environments still face problems such as low generation quality, poor real-time performance and insufficient fusion between modes, which seriously restrict the authenticity and interaction efficiency of immersive experiences. A real-time synthesis and multi-modal interactive optimization method of generative adversarial network animation for a VR environment is proposed. In animation synthesis, a generation architecture with a spatiotemporal consistency adversarial training mechanism is constructed, and a multi-scale feature fusion strategy is combined to achieve high-quality and low-latency animation generation. Experiments were conducted on the VR (Virtual Reality)-Gesture-Voice dataset (60,000 training samples, 15,000 testing samples) and benchmarked against state-of-the-art (SOTA) models including VideoGAN and StyleGAN3. Key results: For the isolated GAN synthesis module: average rendering frame rate = 23.7 FPS (58% higher than VideoGAN), synthesis delay ≤ 4.5 ms; For the end-to-end VR system: average rendering frame rate = 85–92 FPS (meeting VR’s ≥72 FPS standard), end-to-end latency ≤17 ms (51% lower than StyleGAN3). In the real-time synthesis test of generative adversarial network (GAN) animation in the VR environment, two sets of key metrics are reported to clarify different system scopes: For the isolated GAN animation synthesis module (excluding end-to-end transmission and rendering), the improved algorithm achieved an average rendering frame rate of 23.7 FPS (58% higher than the traditional method) and controlled the synthesis delay within 4.5 Ms. Regarding system resource usage, GPU (Graphics Processing Unit） memory consumption is reduced by 0.6 GB, model reasoning time is reduced by 49.5%, and 85% real-time rendering efficiency can still be maintained at 8K resolution.