Digital platforms frequently reproduce heteronormative norms and structural biases, limiting inclusive communication between LGBTQ+ and cisgender individuals. The Metaverse, with its affordances for identity fluidity, presence, and community governance, offers a promising site for reimagining such interactions. To investigate this potential, we conducted participatory design workshops involving LGBTQ+ and cisgender participants, situating them in speculative Metaverse contexts to surface barriers and co-create alternative futures. The workshops followed a three-phase process—identifying challenges, speculative problem-solving, and visualizing futures—yielding socio-spatial-technical solutions across four layers: embodied interaction, negotiated visibility, community formation, and reconfigured norms. These findings highlight the importance of spatial cues and power dynamics in shaping digital encounters. We contribute by (1) articulating challenges of cross-group communication in virtual environments, (2) proposing inclusive design opportunities for the Metaverse, and (3) advancing principles for addressing power geometry in digital space. This work demonstrates futuring as a critical strategy for designing equitable, transformative communication infrastructures.

Due to age-related cognitive and physical decline, older adults face numerous difficulties when learning new functions of smartphone applications. However, older adults often struggle to ask questions clearly and follow instructions independently. Through a formative study (N=16), we identified the behaviors and challenges of older adults seeking help independently and analyzed the effective mechanism of in-person instruction. Based on these findings, we proposed GuideMe, an in-situ conversational instruction system for older adults’ application learning. GuideMe utilizes Vision-Language-Models to analyze multimodal context in users’ situations, then assists users in confirming their intentions by asking clarifying questions, and finally provides step-by-step instructions using in-situ highlight and deictic gestures. We conducted a user study (N=18) that demonstrated that GuideMe significantly reduced users’ cognitive load during learning, helped them ask questions and follow instructions efficiently, and achieved performance comparable to that of in-person instruction.

Chinese landscape painting seeks a poetic harmony between humanity and nature, an ideal encapsulated by the concept of “wandering within the painting.” However, its appreciation, whether in traditional museums or through digital reconstructions, often remains a passive, visually oriented experience. In response, Ink Voyage draws on this classical aesthetic to create an immersive and interactive system. Utilizing inertial motion capture, it allows participants to navigate a virtual boat through digitally reimagined ink landscapes. This project transforms passive viewing into embodied exploration, enabling a more natural and active encounter with the spatial poetics of Chinese landscape art.

Virtual reality is increasingly adopted in education for its potential to create immersive, interactive learning experiences. Among various instructional approaches, gamification has emerged as a promising strategy to enhance learner engagement and motivation. While VR naturally provides a platform for game-based learning, current applications often overlook the underlying motivational mechanisms that influence learner behavior in these environments. Building on Self-Determination Theory, we examined how gamification and motivational framing influence VR learning. We designed a VR system featuring both gamified and non-gamified versions of two learning tasks-one culturally expressive (batik-based task) and one technically focused (code-based task)-to reflect different motivational framings. Our findings show that gamification selectively influenced intrinsic motivation components and played a dominant role in shaping learner satisfaction. A trend-level interaction suggested that combining extrinsic framing with gamification may increase satisfaction. Gamification improved overall experience and engagement, even though it elevated perceived workload. These results highlight the importance of aligning gamification strategies with learner motivation and underscore the need for more dynamic approaches to capture motivational processes in VR, offering design insights for more effective and psychologically attuned VR learning environments.

Cybersickness remains a critical barrier to the widespread adoption of Virtual Reality (VR), particularly in scenarios involving intense or artificial motion cues.Among the key contributors is excessive optical flow-perceived visual motion that, when unmatched by vestibular input, leads to sensory conflict and discomfort. While previous efforts have explored geometric or hardware-based mitigation strategies, such methods often rely on predefined scene structures, manual tuning, or intrusive equipment. In this work, we propose U-MAD, a lightweight, real-time, AI-based solution that suppresses perceptually disruptive optical flow directly at the image level. Unlike prior handcrafted approaches, this method learns to attenuate high-intensity motion patterns from rendered frames without requiring mesh-level editing or scene-specific adaptation. Designed as a plug-and-play module, U-MAD integrates seamlessly into existing VR pipelines and generalizes well to procedurally generated environments. The experiments show that U-MAD consistently reduces average optical flow and enhances temporal stability across diverse scenes. A user study further supports the finding that reducing visual motion defects can improve perceptual comfort and alleviate cybersickness symptoms. These findings demonstrate that perceptually guided modulation of optical flow provides an effective and scalable approach to creating more user-friendly immersive experiences.

Cybersickness remains a major obstacle to the widespread adoption of immersive virtual reality (VR), particularly in consumer-grade environments. While prior methods rely on invasive signals such as electroencephalography (EEG) for high predictive accuracy, these approaches require specialized hardware and are impractical for real-world applications. In this work, we propose a scalable, deployable framework for personalized cybersickness prediction leveraging only non-invasive signals readily available from commercial VR headsets, including head motion, eye tracking, and physiological responses. Our model employs a modality-specific graph neural network enhanced with a Difference Attention Module to extract temporal-spatial embeddings capturing dynamic changes across modalities. A cross-modal alignment module jointly trains the video encoder to learn personalized traits by aligning video features with sensor-derived representations. Consequently, the model accurately predicts individual cybersickness using only video input during inference. Experimental results show our model achieves 88.4% accuracy, closely matching EEG-based approaches (89.16%), while reducing deployment complexity. With an average inference latency of 90ms, our framework supports real-time applications, ideal for integration into consumer-grade VR platforms without compromising personalization or performance. The code will be relesed at https://github.com/U235-Aurora/PTGNN.

While digital media provides unprecedented access to information, it also presents challenges such as cognitive fatigue and attentional decline due to information overload. To address these issues, we propose "VR Coffee Break" intervention: a virtual reality experience that uses synchronised audio and visual cues to improve attention. We evaluated the effectiveness of "VR Coffee Break" by administering synchronised audio-visual stimulation in a virtual reality setting to seven healthy adults. The results revealed significant reductions in commission and omission errors, faster reaction times and a 70% increase in neural alpha power. These results demonstrate that rhythmic audiovisual stimulation can quickly improve attention and increase neural entrainment, suggesting potential applications in education, cognitive rehabilitation and productivity support.

In the era of the Internet of Vehicles (IoV), Digital Twins (DTs) serve as a critical bridge between physical vehicles and the digital world, enabling real-time virtualization, simulation, and data-driven decision-making. While Cloud Computing (CC) and Mobile Edge Computing (MEC) provide foundational support for low-latency services, the dynamic nature of IoV environments-characterized by fluctuating resource availability, heterogeneous infrastructures, and stringent quality-of-service requirements-poses significant challenges for efficient DT deployment. To address this, we propose a novel DT system framework tailored for heterogeneous MEC/CC environments, where DTs are dynamically maintained across distributed servers using multisource data collected from vehicular networks. Central to our approach is a DT placement optimization problem that jointly minimizes latency and energy consumption under resource constraints. We design a Distributed Deep Learning (DDL)-based offloading scheme to adaptively optimize DT placement, ensuring scalability and responsiveness to real-time environmental changes. Extensive simulations demonstrate that our solution shows superior performance compared to heuristic benchmarks.

Virtual reality offers the opportunity for immersive exploration, yet it is often undermined by cybersickness. However, how individuals strike a balance between exploration and discomfort remains unclear. Existing method (e.g., reinforcement learning (RL)) often fail to fully capture the complexities of navigation and decision-making patterns. This study investigates how curiosity influences users’ navigation behavior, particularly how users strike a balance between exploration and discomfort. We propose curiosity as a key factor driving irrational decision-making and apply the free energy principle to model the relationship between curiosity and user behavior quantitatively. Our findings indicate that users generally adopt conservative strategies when navigating. Also, curiosity levels tend to rise when the virtual environment changes. These results illustrate the dynamic interplay between exploration and discomfort. In addition, it offers a new perspective on how curiosity drives behavior in immersive environments, providing a foundation for designing adaptive VR environments. Future research will further refine this model by incorporating additional psychological and environmental factors to improve prediction accuracy.

Since the Russian invasion of Ukraine, a large volume of biased and partisan news has been spread via social media platforms. As this may lead to wider societal issues, we argue that understanding how partisan news sharing impacts users' communication is crucial for better governance of online communities. In this paper, we perform a measurement study of partisan news sharing. We aim to characterize the role of such sharing in influencing users' communications. Our analysis covers an eight-month dataset across six Reddit communities related to the Russian invasion. We first perform an analysis of the temporal evolution of partisan news sharing. We confirm that the invasion stimulates discussion in the observed communities, accompanied by an increased volume of partisan news sharing. Next, we characterize users' response to such sharing. We observe that partisan bias plays a role in narrowing its propagation. More biased media is less likely to be spread across multiple subreddits. However, we find that partisan news sharing attracts more users to engage in the discussion, by generating more comments. We then built a predictive model to identify users likely to spread partisan news. The prediction is challenging though, with 61.57% accuracy on average. Our centrality analysis on the commenting network further indicates that the users who disseminate partisan news possess lower network influence in comparison to those who propagate neutral news.

With the recent development of the Virtual Reality (VR) industry, the increasing number of VR users pushes the demand for the massive production of immersive and expressive VR scenes in related industries. However, creating expressive VR scenes involves the reasonable organization of various digital content to express a coherent and logical theme, which is time-consuming and labor-intensive. In recent years, Large Language Models (LLMs) such as ChatGPT 3.5 and generative models such as stable diffusion have emerged as powerful tools for comprehending natural language and generating digital contents such as text, code, images, and 3D objects. In this paper, we have explored how we can generate VR scenes from text by incorporating LLMs and various generative models into an automated system. To achieve this, we first identify the possible limitations of LLMs for an automated system and propose a systematic framework to mitigate them. Subsequently, we developed Text2VRScene, a VR scene generation system, based on our proposed framework with well-designed prompts. To validate the effectiveness of our proposed framework and the designed prompts, we carry out a series of test cases. The results show that the proposed framework contributes to improving the reliability of the system and the quality of the generated VR scenes. The results also illustrate the promising performance of the Text2VRScene in generating satisfying VR scenes with a clear theme regularized by our well-designed prompts. This paper ends with a discussion about the limitations of the current system and the potential of developing similar generation systems based on our framework.

Virtual reality (VR) is an immersive medium that offers users a unique opportunity to experience a digital environment realistically. As the demand for VR content continues to grow, the importance of effective VR editing techniques becomes increasingly apparent. This paper is a pioneering work investigating the effects of jump cuts on the viewer’s sense of presence, viewing experience, and edit quality in cinematic VR. Specifically, this work focuses on using the 30-degree and 180-degree rules in VR editing to minimize the adverse effects of jump cuts. We conducted a user study with thirteen participants, who watched nine different VR edits and completed a survey for each edited video. Our results indicate that employing the 30-degree and 180-degree rules in VR editing can significantly improve the sense of presence, viewing experience, and edit quality while mitigating the negative effects of jump cuts. We provide valuable insights for VR content creators and editors to achieve more effective and immersive VR experiences.

Users would experience individually different sickness symptoms during or after navigating through an immersive virtual environment, generally known as cybersickness. Previous studies have predicted the severity of cybersickness based on physiological and/or kinematic data. However, compared with kinematic data, physiological data rely heavily on biosensors during the collection, which is inconvenient and limited to a few affordable VR devices. In this work, we proposed a deep neural network to predict cybersickness through kinematic data. We introduced the encoded physiological representation to characterize the individual susceptibility; therefore, the predictor could predict cybersickness only based on a user’s kinematic data without counting on biosensors. Fifty-three participants were recruited to attend the user study to collect multimodal data, including kinematic data (navigation speed, head tracking), physiological signals (e.g., electrodermal activity, heart rate), and Simulator Sickness Questionnaire (SSQ). The predictor achieved an accuracy of 97.8% for cybersickness prediction by involving the pre-computed physiological representation to characterize individual differences, providing much convenience for the current cybersickness measurement.

Virtual reality (VR) technology has been used for educational purposes in different learning contents during teaching and training. VR could improve users’ learning efficiency and motivation to study abstract concepts. This work designed a VR environment for chemistry education to support computer-mediated hands-on exercises, including Self-propagating high-temperature synthesis (SHS) and Electrode sheet fabrication (ESF). In our evaluation with 39 participants who wore heart beat measurement wearables, we compared the students’ performances in hands-on chemistry tasks, either with or without score-keeping and time-sensitive conditions. Accordingly, we designed questionnaires reflecting sixteen qualitative aspects (e.g., content, perspicuity, and interaction) and perceived user workloads. The experimental results indicate participants’ preferences and attitudes in terms of efficiency and sense of safety. 94.87% of participants reported that the learning simulator could improve learning efficiency, and 92.31% of the participants indicated that it can improve their sense of safety. The results of the data analysis show that the different learning scenarios we simulated have positive significance. Our findings shed light on the quality and learning performance of operational skills for chemistry education.

Many studies have explored how individual differences can affect users' susceptibility to cybersickness in a VR application. However, the lack of strategy to integrate the influence of each factor on cybersickness makes it difficult to utilize the results of existing research. Based on the fuzzy logic theory that can represent the effect of different factors as a single value containing integrated information, we developed two approaches including the knowledge-based Mamdani-type fuzzy inference system and the data-driven Adaptive neuro-fuzzy inference system (ANFIS) to involve three individual differences (Age, Gaming experience and Ethnicity). We correlated the corresponding outputs with the simulator sickness questionnaire (SSQ) scores in a simple navigation scenario. The correlation coefficients obtained through a 4- fold cross validation were found statistically significant with both fuzzy logic approaches, indicating their effectiveness to influence the occurrence and the level of cybersickness. Our work provides insights to establish customized experiences for VR navigation by involving individual differences.

Travel in a real environment is a common task that human beings conduct easily and subconsciously. However transposing this task in virtual environments (VEs) remains challenging due to input devices and techniques. Considering the well-described sensory conflict theory, we present a semiautomatic travel method based on path planning algorithms and gaze-directed control, aiming at reducing the generation of conflicted signals that may confuse the central nervous system. Since gaze-directed control is user-centered and path planning is goal-oriented, our semiautomatic technique makes up for the deficiencies of each with smoother and less jerky trajectories.

This paper proposes a new objective metric of visually induced motion sickness (VIMS) in the context of navigation in virtual environments (VEs). Similar to motion sickness in physical environments, VIMS can induce many physiological symptoms such as general discomfort, nausea, disorientation, vomiting, dizziness and fatigue. To improve user satisfaction with VR applications, it is of great significance to develop objective metrics for VIMS that can analyze and estimate the level of VR sickness when a user is exposed to VEs. One of the well-known objective metrics is the postural instability. In this paper, we trained a LSTM model for each participant using a normal-state postural signal captured before the exposure, and if the postural sway signal from post-exposure was sufficiently different from the pre-exposure signal, the model would fail at encoding and decoding the signal properly; the jump in the reconstruction error was called loss and was proposed as the proposed objective measure of simulator sickness. The effectiveness of the proposed metric was analyzed and compared with subjective assessment methods based on the simulator sickness questionnaire (SSQ) in a VR environment, achieving a Pearson correlation coefficient of. 89. Finally, we showed that the proposed method had the potential to be deployed within a closed-loop system and get real-time performance to predict VR sickness, opening new insights to develop user-centered and customized VR applications based on physiological feedback.