Integrating non-verbal cues into Mixed Reality Agents (MiRAs) enhances their ability to engage users and foster socially rich interactions. This paper investigates the role of locomotion and body posture in shaping user engagement, social presence, and interaction quality through two user studies involving a turn-based Gobang game. From these studies we found that in a competitive context, MiRAs’ locomotion and posture enhanced social presence and engagement, but while in a cooperative context, these behaviors fostered rapport but not trust. By integrating subjective, behavioral, and physiological measures, including EEG, this study provides a holistic understanding of MiRAs’ impact. The findings offer actionable design implications for creating engaging and socially effective virtual agents, advancing the field of human-agent interaction in Mixed Reality. Future research directions include exploring long-term effects, using diverse application domains, and supporting multimodal interactions.

This paper introduces a novel paradigm for building trustworthy autonomous systems through explanation-driven adaptation. We propose a co-adaptive architecture where black-box neural policies, interpretable policy distillation models and explanation engines evolve synergistically through bidirectional feedback loops. The framework fundamentally transforms explainability from passive post-analysis to active system guidance, enabling continuous policy optimization while maintaining human-aligned transparency. By embedding explanation consistency as a core adaptation objective, our approach establishes dynamic equilibrium between environmental responsiveness and operational verifiability. This work advances the design of safety-critical autonomous systems through formalized principles for explainability-guided adaptation, creating new pathways for resilient human-machine collaboration in complex service environments.

Non-verbal cues like locomotion and posture influence users’ perceptions of Mixed Reality Agents (MiRAs). While Electroencephalography (EEG) captures cognitive responses, the influence of MiRAs’ locomotion and postures on brain activity remains underexplored. Additionally, few studies integrate subjective and behavioral measures with EEG to evaluate these cues’ impact on social perception. To address this, we conducted a within-subject study where participants played Gobang against three virtual agents in mixed reality: 1) a speech-only agent (S), 2) an embodied agent with speech and locomotion (S + L), and 3) an embodied agent with speech, locomotion, and posture (S + L + P). Results showed the S + L + P agent had higher engagement measured by the questionnaire but a lower EEG-based engagement index at AF3 than the S + L agent. Besides, the S + L + P was also rated higher in social presence, engagement, and emotional arousal than the S condition; No behavioral differences were observed. We discuss how MiRAs’ locomotion and posture affect users’ social perception and provide design implications for future human-agent interactions.

Technology integration in educational settings has led to the development of novel sensor-based tools that enable students to measure and interact with their environment. Although reports from using such tools can be positive, evaluations are often conducted under controlled conditions and short timeframes. There is a need for longitudinal data collected in realistic classroom settings. However, sustained and authentic classroom use requires technology platforms to be seen by teachers as both easy to use and of value. We describe our development of a sensor-based platform to support science teaching that followed a 14-month design process. We share insights from this design and development approach, and report findings from a six-month large-scale evaluation involving 35 schools and 1245 students. We share lessons learnt, including that technology integration is not an educational goal per se and that technology should be a transparent tool to enable students to achieve their learning goals.

Scientific inquiry involves prediction, observation and explanation (POE) of phenomena and data. Appropriate guidance through these steps is essential for helping students learn and form positive attitudes towards science. Sensor-based education toolkits are becoming a popular way to provide this guidance, but they typically present different interfaces for measurement and learning materials which places a high cognitive demand on learners. To address this challenge, we developed a web application to integrate the scientific inquiry method where students are guided step-by-step, using a scaffolded-learning approach, through slide-based worksheets that provide direct interaction with real-time sensor measurements. We evaluate this approach through a qualitative analysis of data collected from two field studies in classrooms with a total of 42 students. We show that our approach encouraged positivity and further learning in science. Students displayed and expressed interest to conduct science experiments outside of class. We identify design implications for seamless learning, storytelling and integration of POE guided scientific inquiry with sensor-based toolkits.

The conflagration of fire is still a serious problem caused by humans, and houses are at a high risk of fire. Recently, people have used smoke alarms which only have one sensor to detect fire. Smoke is emitted in several forms in daily life. A single sensor is not a reliable way to detect fire. With the rapid advancement in Internet technology, people can monitor their houses remotely to determine the current condition of the house. This paper introduces an intelligent smoke alarm system that uses ZigBee transmission technology to build a wireless network, uses random forest to identify smoke, and uses E‐charts for data visualization. By combining the real‐time dynamic changes of various environmental factors, compared to the traditional smoke alarm, the accuracy and controllability of the fire warning are increased, and the visualization of the data enables users to monitor the room environment more intuitively. The proposed system consists of a smoke detection module, a wireless communication module, and intelligent identification and data visualization module. At present, the collected environmental data can be classified into four statuses, that is, normal air, water mist, kitchen cooking, and fire smoke. Reducing the frequency of miscalculations also means improving the safety of the person and property of the user.