As mobile robots become more common in shared public spaces, understanding how their movements affect pedestrian comfort has become a key challenge in human–robot interaction. Although prior research has investigated the effects of robot lateral distance and speed, findings have been inconsistent, with little attention paid to how these factors interact with pedestrians’ mobility states (e.g., walking vs. stopping). To address this gap, we conducted a virtual reality experiment where participants experienced 30 scenarios combining five lateral distances, three robot velocities, and two pedestrian states. Discomfort was measured using standardized questionnaires and real-time button presses. Results showed that discomfort increased at closer distances and higher speeds, with these effects amplified when pedestrians were walking. Discomfort was minimized when robots-maintained distances over 120 cm and moved slower than typical walking speed. This study offers new insights into how robot proxemics and pedestrian mobility jointly shape discomfort, informing proximity-aware navigation design.

Personal intelligent agents (IAs) are increasingly embedded in everyday life, a trend accelerated by generative AI technologies. Despite their growing presence, these agents often remain fragmented across different life domains and environments. This workshop explores how to design integrated IA ecosystems emphasizing continuity, coordination, and human-centered values. Participants with varied perspectives will collaboratively develop frameworks, scenarios, and guidelines for cohesive personal agent systems that enrich user experiences holistically. By examining factors that shape users' preferences for information integration or separation, we aim to inform the design of coherent, user-aligned multi-agent systems.

This study addresses the challenge of ensuring pedestrian safety in shared spaces during human-robot interactions. As mobile robots become increasingly integrated into public environments, there is a need to better understand how pedestrians perceive robots in proximity. This research explores the effects of robot lateral distance, robot velocity, and pedestrian state on pedestrian perception, with a particular focus on proximity zones during human-robot interactions. Conducted in a virtual reality environment with a within-subject design, the study measured perceived danger through button presses and visualized proximity zones using contour density plots. Findings reveal that increasing lateral distance significantly decreases button press likelihood, particularly beyond 160 cm. Robot velocities of 1 mls and 1.5 mls showed no significant difference, suggesting pedestrians perceive them similarly due to their alignment with average walking speed. Proximity zones were consistently wider and denser on the left than the right, highlighting spatial asymmetry in perception. These results provide quantitative and visual insights for designing mobile robots in shared spaces. Future research should examine environmental and group dynamics and robot design to enhance safety and public acceptance in human-robot interactions.

Users are increasingly exposed to AI-generated language, presenting potential deception and communication risks. This study delved into the rhetorical aspect of AI-generated language influencing users’ truth discernment. We conducted a user study comparing three levels of rhetorical presence and four persuasive rhetorical elements, using interviews to understand users’ truth-detection methods. Results showed that outputs with fewer rhetorical elements posed challenges for users in distinguishing truth from false, while those with more rhetoric often misled users into false truths. Users’ AI expectations influenced truth judgments, with responses meeting expectations perceived as more truthful. Casual, human-like responses were often deemed false, while technical, precise AI responses were preferred. This research emphasizes that rhetorical elements of AI language can significantly bias individuals regardless of a statement’s actual truth. For enhanced transparency in human-AI communication, it is advisable for AI designs to thoughtfully integrate rhetorical elements and establish guiding principles aimed at minimizing the potential for deceptive responses.

This study investigated the effect of attribution and approach movement of the social robot when the user wrongly perceives an error as the robot’s responsibility. The robot’s responsibility attribution and approach movement strategies for the error recovery were examined in a situation where the robot was functioning normally, but the user misunderstood it as robot’s fault. In the experiment participants were exposed to four different verbal and movement interaction scenarios with a social robot and then responded to a survey concerning emotions and trust. Results showed that people no longer trusted the robot that approached while attributing the responsibility to the user. The implication of this study is that a powerful self-serving bias is aroused when a robot attributes the responsibility to the user, and thus, it negatively impacts user experiences even if the event took place due to the user’s misunderstanding. This study suggests empirical guidance for designing a social robot’s attribution strategies and movement interactions.

Artificial intelligence (AI) is beginning to be applied in the field of art, which had hitherto been an area exclusively reserved for human creativity. Using online AI tools, lay people can easily create artworks that imitate the style of famous artists. Consequently, human judgment on the authenticity of artworks has become critical. While many studies have focused on copyright or value of AI-created artworks, we examine whether human beings can distinguish between paintings drawn by artists and fake paintings created using AI tools. We selected the AI’s recommendations for each artwork and prior information about the artists as factors that can affect human judgment and investigated how the two factors affect people’s discriminative abilities. We found that people have difficulty distinguishing authentic from fake artwork and that additional information about artists and artworks can affect people’s criteria for judging paintings. Furthermore, AI recommendations can help discriminate fake paintings, suggesting that AI-assisted decision-making could play an assistive role in human identification of digitized fake paintings.