Conversational agents are becoming integrated into virtual reality (VR) applications, enabling voice-based interaction within immersive environments. Yet, how users initiate communication with these agents remains an underexplored aspect of interaction design. We present a comparative study of four activation methods for initiating speech-based interaction with a conversational agent in VR. A custom-built prototype and task-oriented dialogue scenario supported a within-subjects study designed to explore how each method influences user experience. Through qualitative analysis of interview data, we identify key trade-offs related to control, naturalness, privacy, and social appropriateness. Participants’ preferences were shaped by situational factors, task demands, and the spatial positioning of the agent. We offer empirical insights and design recommendations for developing adaptive, multimodal, and inclusive conversational interfaces in VR, with broader implications for spatial computing and human-AI interaction.

Immersive technologies offer advantages for the visualization of and interaction with complex setups within manufacturing maintenance processes. The present work catalogs different applications of AR/VR in manufacturing maintenance practices as an extended version of a workshop paper presented at the International Workshop on eXtended Reality for Industrial and Occupational Supports (XRIOS). Through a scoping review in three computing and engineering digital libraries, we outline the key attributes of immersive solutions ( N p = 115 ) for industrial maintenance, categorizing functional prototypes with ten parameters related to interaction, visualization, and research methods. Moreover, we conducted a workshop with three manufacturing experts discussing the future of maintenance interfaces. By bringing forth their recommendations and insights, we targeted a key training challenge in maintenance. We designed and implemented a situated visualization prototype for a safety-critical procedure with real-time, in-depth, spatially relevant instructions. We compared the effects of 2D labels and 3D ghosts in a VR-simulated AR environment. In a preliminary between-subjects evaluation study ( N u = 24 ), we measured usability, workload, simulator sickness, completion time, and delayed recall. Although we did not find statistically significant differences between conditions, 3D ghosts showed slightly lower perceived workload and discomfort levels, along with shorter completion times. On the other hand, 2D labels produced higher usability. Overall, we contribute by a mapping out the state of the art and discovering knowledge gaps within immersive maintenance, presenting a design and preliminary user study that adheres to our recommendations.

Figure 1: Mixed-reality hand-based interface for interactive manipulation of spatial 3D audio sources.The interface features three types of two-handed interactions: horizontal hand movement changes visualization and audio filter; vertical hand movement selects the notes from a 7-level scale (A to G); and thumbs-up/down gestures control audio volume.Audio is only triggered when the pinch gesture is detected.Images recorded in first-person perspective from a head-mounted display.

Figure 1: A) Participants seeing reality through head-mounted displays.B) Co-located interactive objects.C) Individual contentgeneration panel.D) 3D mid-air drawing.E) A virtual object transitioning to physical space from VR through the portal.

Designing affect-based personalized technology involves dealing with large datasets. Machine learning (ML) algorithms are employed to predict affect and similar human factors from in-game metrics, behavioral patterns, or physiological responses. The classification performance is usually presented as a global point estimate without providing user-specific interpretations. This approach is incompatible with effective personalization in games because it disregards the variability of body responses between players. This paper proposes a methodology to classify subjective human factors from large multimodal data. A public VR dataset (CEAP-360VR) was used to extensively compare three ML classifiers and five feature importance techniques. The produced models could reduce the original feature space by 82% (from 113 to 20 features) without compromising predictive performance (F1 score). A random forest (RF) using forward sequential feature selection (fSFS) yielded the best prediction of binary valence (F1=0.761) and arousal (F1=0.748). Finally, feature importance rankings are discussed with emphasis on global and user-specific patterns that may improve affect recognition. The proposed methodology is envisioned to help game designers and researchers create customized user-centric games and VR experiences inferring possible explanations from multimodal datasets.

Figure 1: The virtual reality (VR) navigation task has three different destination points (left).The experiment employed immersive VR and teleportation to traverse the environment (right).

Biosensing technologies are on their way to becoming ubiquitous in multimedia interaction.These technologies capture physiological data, such as heart rate, breathing, skin conductance, and brain activity.Researchers are exploring biosensing from perspectives including engineering, design, medicine, mental health, consumer products, and interactive art.These technologies can enhance our interactions, allowing us to connect with our bodies and others around us across diverse application areas.However, the integration of biosignals in HCI presents new challenges pertaining to choosing what data we capture, interpreting these data, its representation, application areas, and ethics.There is a need to synthesize knowledge across diverse perspectives of researchers and designers spanning multiple domains and to map a landscape of the challenges and opportunities of this research area.The goal of this workshop is to exchange knowledge in the research community, introduce novices to this emerging field, and build a future research agenda.

When learning programming concepts, beginners face challenges that lead to decreased motivation, Game-Based Learning (GBL) uses game principles to make learning more engaging, and VR has been explored as a way to enhance GBL further. This paper explores the impact of Virtual Reality (VR) on learning programming, and we developed Codeseum to compare whether VR-based learning is perceived as more engaging and usable than a desktop game counterpart. The experiment with ten participants included data from questionnaires, interviews, and structured observations. The quantitative analysis indicated that VR was perceived as inducing higher focused attention, aesthetic appeal, and reward, while the thematic analysis provided discussion elements of seven themes, including interaction, engagement, and physical expressions from the participants. Overall, the desktop application had better accessibility, whereas the immersive interactions from Codeseum in VR induced higher levels of enjoyment and engagement. Our study contributes insights into the potential of VR in education, mainly teaching coding skills in engaging ways, and offers information to adopt immersive technologies in teaching practice.

This work-in-progress presents the design of an XR prototype for the purpose of educating basic cybersecurity concepts. We have designed an experimental virtual reality cyberspace to visualise data traffic over network, enabling the user to interact with VR representations of data packets. Our objective was to help the user better conceptualise abstract cybersecurity topics such as encryption and decryption, firewall and malicious data. Additionally, to better stimuli the sense of immersion we have used Peltier thermoelectric modules and Arduino Uno to experiment with multisensory XR. Furthermore, we reflect on early evaluation of this experimental prototype and present potential paths for future improvements.

Bodily signals can complement subjective and behavioral measures to analyze human factors, such as user engagement or stress, when interacting with virtual reality (VR) environments. Enabling widespread use of (also the real-time analysis) of bodily signals in VR applications could be a powerful method to design more user-centric, personalized VR experiences. However, technical and scientific challenges (e.g., cost of research-grade sensing devices, required coding skills, expert knowledge needed to interpret the data) complicate the integration of bodily data in existing interactive applications. This paper presents the design, development, and evaluation of an open-source software framework named Excite-O-Meter. It allows existing VR applications to integrate, record, analyze, and visualize bodily signals from wearable sensors, with the example of cardiac activity (heart rate and its variability) from the chest strap Polar H10. Survey responses from 58 potential users determined the design requirements for the framework. Two tests evaluated the framework and setup in terms of data acquisition/analysis and data quality. Finally, we present an example experiment that shows how our tool can be an easy-to-use and scientifically validated tool for researchers, hobbyists, or game designers to integrate bodily signals in VR applications.