Aims: The right to play is a basic human right. However, sport participation is often limited for children with complex motor disabilities such as quadriplegic cerebral palsy. Brain-computer interface systems (BCIs) translate a user’s brain waves into instructions that can be used to control external devices. We have developed a BCI-enabled Boccia system that allows children who cannot move on their own to play independently. Boccia is a Paralympic sport that offers inclusion for individuals with limited mobility, it does not fully accommodate those with severe motor disabilities and communication difficulties. The purpose of this study was to partner with persons with lived experience (PWLE) to improve the Boccia system with the expertise of patients and Boccia Paralympic athletes.

Methods: Following the Strategy for Patient Oriented Research (SPOR) framework, we engaged seven PWLE to participate in two virtual sessions and one in-person session to improve the BCI-Boccia system. After the first virtual session, the engineering team translated the feedback and comments from the PWLE into a list of tasks to develop the new features. The software development approach used an Agile development strategy, after which the second session consisted of a demonstration to gather additional feedback for refinement. After the hardware was developed, two PWLE attended in-person sessions to use the system and provide additional feedback. Engagement was evaluated using the Public and Patient Engagement Evaluation Tool (PPEET).

Results: Comments from the first virtual meeting focused on improving the software controller of the ramp, as well as the mechanical stability of the ramp. A new software controller was designed. The coarse-movement controller is used for initial control to give the player a broad range of selection options. To refine the precision of the shot, a fine-movement controller is provided. From these two visual control schemes, both ball elevation and ramp rotation can be chosen with a single selection. Similarly, a new base for the Boccia ramp was developed to allow for improved stability and faster assembly and disassembly procedures. The results of the PPEET confirmed that the PWLE perceived that their suggestions were taken into account and that the necessary resources were provided for their participation.

Conclusion: We demonstrate that a patient engagement strategy can inform and facilitate improvements to a BCI-enabled Boccia system. The resulting system supports the play of both recreational and competitive users by having a simplified controller and improved hardware. Involving diverse PWLE throughout the design cycle may improve accessibility and user adoption in disability sports. Inclusive participation likely helps ensure that improvement efforts directly address the needs of end users.

Affective Computing aims to build systems that can sense, interpret, and influence human emotions. In parallel, virtual reality (VR) has matured into a powerful solution for immersion, presence, and interaction. When combined, these two technologies enable a new generation of emotionally adaptive systems that can recognize users’ inner states and react in real time. In this article, we explore the opportunities and challenges of emotion recognition and emotion elicitation in VR, focusing on the Virtual Emotion, Elicitation and Recognition Loop, a closed-loop framework where virtual environments adapt based on recognized and targeted emotions. We discuss applications in manufacturing, product design, and education, and highlight key ethical and privacy concerns, especially in light of recent regulations such as the European Commission released the proposed Regulation on Artificial Intelligence (EU AI Act). Finally, we outline open research directions toward responsible, privacy-aware, and scalable affective VR systems, suggesting that the convergence between VR and Affective Computing can become a crucial foundation for the next generation of human-centric interactive technologies.

This perspective reframes human–robot interaction (HRI) through extended reality (XR), arguing that virtual robots powered by large foundation models (FMs) can serve as cognitively grounded, empathic agents. Unlike physical robots, XR-native agents are unbound by hardware constraints and can be instantiated, adapted, and scaled on demand, while still affording embodiment and co-presence. We synthesize work across XR, HRI, and cognitive AI to show how such agents can support safety-critical scenarios, socially and cognitively empathic interaction across domains, and extend physical capabilities with XR and AI integration. We then discuss how multimodal large FMs (e.g., large language models, large vision models, and vision-language models) enable context-aware reasoning, affect-sensitive situations, and long-term adaptation, positioning virtual robots as cognitive and empathic mediators rather than mere simulation assets. At the same time, we highlight challenges and potential risks, including overtrust, cultural and representational bias, privacy concerns around biometric sensing, and data governance and transparency. The paper concludes by outlining a research agenda for human-centered, ethically grounded XR agents, emphasizing multi-layered evaluation frameworks, multi-user ecosystems, mixed virtual–physical embodiment, and societal and ethical design practices to envision XR-based virtual agents powered by FMs as reshaping future HRI into a more efficient and adaptive paradigm.

Aims: Users’ empathy towards artificial agents can be influenced by the agent’s expression of emotion. To date, most studies have used a Wizard of Oz design or manually programmed agents’ expressions. This study investigated whether autonomously animated emotional expression and neural voices could increase user empathy towards a Virtual Human.

Methods: 158 adults participated in an online experiment, where they watched videos of six emotional stories generated by ChatGPT. For each story, participants were randomly assigned to a virtual human (VH) called Carina, telling the story with either (1) autonomous expressive or non-expressive animation, and (2) a neural or standard text-to-speech (TTS) voice. After each story, participants rated how well the animation and voice matched the story, and their cognitive, affective, and subjective empathy towards Carina were evaluated. Qualitative data were collected on how well participants thought Carina expressed emotion.

Results: Autonomous emotional expression enhanced the alignment between the animation and voice, and improved subjective, cognitive, and affective empathy. The standard voice was rated as matching the fear and sad stories better, the sad animation was better, creating greater subjective and cognitive empathy for the sad story. Trait empathy and ratings of how well the animation and voice matched the story predicted subjective empathy. Qualitative analysis revealed that the animation conveyed emotions more effectively than the voice, and emotional expression was associated with increased empathy.

Conclusion: Autonomous emotional animation of VHs can improve empathy towards AI-generated stories. Further research is needed on voices that can dynamically change to express different emotions.

Aims: Sharing a robot’s intentions is crucial for building human confidence and ensuring safety in robot co-located environments. Communicating planned motion or internal state of a robot in a clear and timely manner is challenging, especially when users are occupied with other tasks. Augmented reality (AR) offers an effective medium for delivering visual cues that convey such information. This study evaluates a smartphone-based AR interface designed to communicate a robot’s navigation intentions and enhance users’ situational awareness (SA) in shared human–robot settings.

Methods: We developed a mobile AR application using Unity3D and Google ARCore to display goal locations, planned trajectories, and the real-time motion of a Robot Operating System enabled mobile robot. The system provides three visualization modes: Goal Visible, Path Visible, and Robot Visible, with spatial alignment achieved via a reference image marker. Fifty-eight participants, with varying levels of experience in robotics and AR, took part in an online user study. SA was assessed using a query-based evaluation adapted from the Situational Awareness Global Assessment Technique (SAGAT), examining perception, comprehension, and projection levels. A post-assessment survey captured user opinions on usability and perceived benefits.

Results: Participants achieved an average SAGAT score of 86.5%, indicating improved awareness of the robot mission, spatial positioning, and safe zones. AR visualization was particularly effective for identifying obstacles and predicting unobstructed areas. In the post-assessment survey, over 96.6% of participants agreed that the interface enhanced their confidence and understanding of robot motion intentions.

Conclusion: A mobile AR interface can significantly enhance SA in shared human–robot environments by making robot intentions more transparent and comprehensible. Future work will include in-situ evaluations with physical robots, integration of richer robot-state information such as velocity and sensor data, and the exploration of additional visualization strategies that further strengthen safety, predictability, and trust in human–robot collaborative environments.