Eileen Roesler

While prior research has demonstrated spontaneous visual perspective-taking (VPT) towards robots using static images or videos of existing robots, little is known about whether VPT occurs with robotic avatars or in immersive environments like virtual reality (VR). The purpose of this research was to extend the existing research to robotic avatars and VR as a medium for interaction. Study 1 confirmed that participants spontaneously adopt the perspective of both robotic and human avatars in static images, with no significant difference in VPT between the two agents. Study 2 compared VPT in video and VR conditions, revealing unexpectedly higher VPT in the video condition, despite VR’s greater sense of copresence. We suspected that the spatial context of stimulus presentation played a critical role. Therefore, Study 3 further explored this by manipulating the placement of stimuli in the robot’s versus the shared environment, revealing that VPT was higher when stimuli were presented exclusively in the robot’s environment. This finding suggests that earlier VPT research may have a critical methodological limitation, as it did not account for spatial context in real human–robot interaction scenarios, which could limit its applicability to real-world robot interactions.

Recent embodiment research has focused on the preference and intention to use physical versus depicted agents. This has overlooked non-embodied agents and failed to explore the underlying mechanisms of embodiment. To address these gaps, we included a non-embodied agent and measured perceived capabilities and social presence as possible mediators for intention to use in our within-subjects laboratory experiment ( N = 36). Regarding participants’ intention, when physical and depicted were compared, a positive effect appeared, which disappeared once mediators were accounted for. When physical and absent were compared, no significant effect on intention to use was found and even became negative once mediators were added. Additional analyses showed positive effects of physical embodiment for perceived physical and communicative capabilities, shared experience, co-presence, and preference. Overall, findings revealed that although physical embodiment versus depicted and absent increased perceived capabilities and social presence, it did not automatically result in higher intention to use.

The “Trust in Automated Systems Scale” by Jian et al. is one of the most used scales in human factors research. However, there is an inconsistency in how researchers interpreted and used the scale, including a common practice of scale modifications. As it remains unclear how and to what extent modifications influence the scale’s factor structure, the current analysis addresses this question by conducting confirmatory factor analyses using publicly available datasets to evaluate and compare the factor structure of the original and modified (word “system” changed to “AI”) scales. The results support a two-factor structure, suggesting trust and distrust as separate factors, and reveal differences in the factor structure between the unmodified and AI-modified scales, suggesting consequences of modification on construct validity. Overall, this work illustrates the risks of modifications and the importance of properly validating scales to ensure the quality and reproducibility of research findings in trust measurement.

Transparency has emerged as a critical construct in human-agent interaction (HAI) research. Given the interdisciplinary field of HAI, researchers from different backgrounds tend to study transparency in different ways, yet there remains no clear consensus on how agent transparency should be defined, measured, or manipulated. Therefore, this systematic literature review takes a crucial step toward addressing this challenge by synthesizing how transparency has been conceptualized and operationalized across HAI research. Through this comprehensive synthesis, we examined how transparency has been defined, measured, and manipulated across studies. Our findings reveal that transparency is most commonly defined as information disclosure, yet definitions, measures, and manipulations of transparency remain highly inconsistent across studies. Notably, most papers that manipulated transparency never verified whether participants actually perceived the system as more transparent, highlighting a critical gap between designed and perceived transparency and the need for a validated measure of perceived transparency.

Air quality threatens health through particulate matter (PM), volatile organic compounds (VOCs), and allergens, yet current monitors of- ten fail to communicate threats properly. We introduce Nose Knows, a robot that uses “universal nasal cues”—such as sneezes—to com- municate. Using participatory design, we conducted an expert inter- view, user focus groups, and iterative prototyping to build a robot that detects PM10, PM2.5, VOCs, and customizable odors. The ro- bot integrates servo-driven movements, sound effects, LEDs, and a web interface. Our findings highlight preferences for customizable aesthetics and show that biologically inspired cues can possibly improve clarity, engagement, and awareness of air quality.