Aims: The right to play is a basic human right. However, sport participation is often limited for children with complex motor disabilities. We developed a brain-computer interface (BCI)-enabled Boccia system that allows children with severe motor disabilities and communication difficulties to play independently. The purpose of this study was to partner with persons with lived experience (PWLE) to improve the BCI-Boccia system.

Methods: Following the Strategy for Patient-Oriented Research framework, we engaged seven PWLE. In the first session, we gathered comments from the PWLE, which were translated into a list of required features. The software was developed using an Agile approach. The second session involved a demonstration to collect additional feedback. In the third session, two PWLE tested the system in person. Engagement was evaluated using the Public and Patient Engagement Evaluation Tool (PPEET).

Results: Comments from the PWLE focused on improving the software controller and the mechanical stability of the ramp. New software controllers for coarse and fine movements were designed, and a new base was developed to enhance stability while allowing faster assembly and disassembly. The PPEET confirmed that PWLE felt their suggestions were considered and that sufficient resources were provided to support their participation.

Conclusion: We demonstrate that a patient engagement strategy can inform and facilitate improvements to a BCI-enabled Boccia system. 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.