We explored how robot-assisted therapy based on smile analysis may facilitate the prosocial behaviors of children with autism spectrum disorder. Prosocial behaviors, which are actions for the benefit of others, are required to belong to society and increase the quality of life. As smiling is a candidate for predicting prosocial behaviors in robot-assisted therapy, we measured smiles by annotating behaviors that were recorded with video cameras and by classifying facial muscle activities recorded with a wearable device. While interacting with a robot, the participants experienced two situations where participants' prosocial behaviors are expected, which were supporting the robot to walk and helping the robot from falling. We first explored the overall smiles at specific timings and prosocial behaviors. Then, we explored the smiles triggered by a robot and behavior changes before engaging in prosocial behaviors. The results show that the specific timing of smiles and prosocial behaviors increased in the second session of children with autism spectrum disorder. Additionally, a smile was followed by a series of behaviors before prosocial behavior. With a proposed Bayesian model, smiling, or heading predicted prosocial behaviors with higher accuracy compared to other variables. Particularly, voluntary prosocial behaviors were observed after smiling. The findings of this exploratory study imply that smiles might be a signal of prosocial behaviors. We also suggest a probabilistic model for predicting prosocial behaviors based on smile analysis, which could be applied to personalized robot-assisted therapy by controlling a robot's movements to arouse smiles and increase the probability that a child with autism spectrum disorder will engage in prosocial behaviors.

This study outlines the development of a platform that enables real-time bio-information sharing between gaming partners regardless of the game of choice. The authors hypothesize that since biosignals contain information about a person’s physiological and psychological state, sharing bio-information with easy-to-understand visual representations can augment the sense of social presence between online gaming partners. The proposed method uses the heart rate as a biosignal detected using a wrist-worn heart rate monitor, uploads these signals to a secure online real-time database, and creates visual representations of the gaming partner’s heart rate on each gamer’s screen. Comparative studies were conducted with 20 gamers. The results showed that sharing bio-information could elicit a sense of social presence similar to sharing real-time face videos in online settings. Furthermore, a synergetic effect was observed when bio-information was combined with face video sharing, leading to an even greater perceived social presence. Similar trends were not observed in co-presence in the participants of this study. However, grouping the participants’ scores by emotional arousal level showed a similar trend in the high-arousal group, suggesting that the impact of bio-information sharing may be modulated by the intensity of the gaming experience. The obtained results underscore the potential of bio-information sharing to augment social presence in online gaming contexts.

This study proposes a dual-level analytical approach to observing human–robot interactions in a real-world public setting, specifically a science museum. Observation plays a crucial role in human–robot interaction research by enabling the capture of nuanced and context-sensitive behaviors that are often missed by post-interaction surveys or controlled laboratory experiments. Public environments such as museums pose particular challenges due to their dynamic and open-ended nature, requiring methodological approaches that balance ecological validity with analytical rigor. To address these challenges, we introduce a dual-level approach for behavioral observation, integrating statistical analysis across demographic groups with time-series modeling of individual engagement dynamics. At the group level, we analyzed engagement patterns based on age and gender, revealing significantly higher interaction levels among children and adolescents compared to adults. At the individual level, we employed temporal behavioral analysis using a Hidden Markov Model to identify sequential engagement states—low, moderate, and high—derived from time-series behavioral patterns. This approach offers both broad and detailed insights into visitor engagement, providing actionable implications for designing adaptive and socially engaging robot behaviors in complex public environments. Furthermore, it can facilitate the analysis of social robot interactions in everyday contexts and contribute to building a practical foundation for their implementation in real-world settings.

Social robots in public cultural venues, such as science museums, must engage diverse visitors through brief, one-off encounters where long-term user modeling is infeasible. This research examines immediately interpretable behavioral cues of a robot that can evoke a sense of personalization without storing or profiling individual users. First, a video-based observational study of expert and novice museum docents identified service strategies that enable socially adaptive communication. Building on these insights, three controlled laboratory studies investigated how specific cues from robots influence user perception. A video-based controlled study examined how recognition accuracy shapes users’ social impressions of the robot’s intelligence. Additional studies based on the Wizard-of-Oz (WoZ) method tested whether explanatory content aligned with participants’ background knowledge and whether explicit preference inquiry and memory-based continuity strengthened perceptions of personalization. Results showed that recognition accuracy improved social impressions, whereas knowledge alignment, explicit preference inquiry, and memory-based continuity cues increased perceived personalization. These findings demonstrate that micro-level personalization cues, interpretable within a short-term encounter, can support user-centered interaction design for social robots in public environments.

A growing number of higher-education institutions are implementing synchronous hybrid delivery, which provides both online and on-campus learners with simultaneous instruction, especially for facilitating discussions in Active Learning (AL) contexts. However, learners face difficulties in picking up social cues and gaining free access to speaking rights due to the geometrical misalignment of individuals mediated through screens. We assume that the cultivation of discussions is allowed by ensuring the spatial localization of learners similar to that in a physical space. This study aims to design a synchronous hybrid learning environment, called Mirror Campus (MC), suitable for the AL scenario that connects physical and cyberspaces by providing spatial localization of learners. We hypothesize that the MC promotes discussion-oriented behaviors, and eventually enhances applied skills for group tasks, related to discussion, creativity, decision-making, and interdependence. We conducted an experiment with five different groups, where four participants in each group were asked to discuss a given topic for fifteen minutes, and clarified that the occurrences of facing behaviors, intervening, and simultaneous utterances in the MC were significantly increased compared to a conventional video conferencing. In conclusion, this study demonstrated the significance of the spatial localization of learners to facilitate discussion-oriented behaviors such as facing and speech.