This paper presents a novel virtual reality (VR) authoring tool designed to simplify the creation of human-robot interaction (HRI) studies. The tool was developed to lower the technical barriers in VR development and allows researchers to simulate complex robot interactions without the need for advanced programming skills. It incorporates key HRI methods such as the Wizard of Oz (WoZ) technique, eye-tracking and motion tracking, and provides a comprehensive platform for data collection and study customization. A technical user study with computer scientists was carried out to evaluate the technical applicability of the tool. The participants confirmed the user-friendliness, flexibility and accessibility of the tool and furthermore found the tool efficient and expressed a strong interest in using it for future research. The results underline the tool's potential to expand the use of VR in HRI research, especially for non-technical researchers.

Transparent communication about how social robots collect and process personal data is essential, especially as their presence in public spaces continues to grow and their applications become more widespread. While previous research has primarily focused on the content and creation of explainable transparency, less attention has been given to which modalities robots should use to convey transparency in the first place. To close this gap, we examined different single modality approaches for communicating transparency and compared them to various combined modalities for transparency explanation, since these have the potential to convey information more efficiently through multiple channels. We conducted a virtual reality (VR) two-part laboratory experiment in which N = 106 participants interacted with a virtual Pepper robot and had to disclose personal data to it. The study design consisted of six conditions: a control group without transparency communication, two multimodal conditions where transparency declarations were presented through multiple channels, and three unimodal conditions where a single channel was used for transparency communication. The results show that the unimodal group was more effective than both the multimodal and control groups in delivering clear and understandable transparency declarations. This suggests that unimodal approaches to transparency may be the preferable option. This study provides insights into transparency declarations in HRI and offers key takeaways on how transparency can be communicated most effectively.

Looking at state-of-the-art robots, the majority have a white surface. This raises the questions of why robots are designed predominantly white, and how surface color affects the robot’s social perception. This not only reinforces human stereotypes but also influences the acceptance and usage of robots. Accordingly, this online study with a 2 (black vs. white) x 3 (Pepper robot vs. Temi robot vs. Kuka robot) within-subjects design (N = 100) investigated the effect on racist stereotypical perception and the role of anthropomorphism using implicit (Implicit Association Test) and explicit (Robotic Social Attributes Scale) measures and questionnaires on behavioral intentions. Our results show that robot color influences the implicit perception of robots but rarely affects explicit perception and behavior measures. Moreover, no effects of the robots’ anthropomorphism on racist perceptions were found. As more effects of anthropomorphism on our outcome variables were found than for robot color, we assume that robot color activates stereotypes in terms of color rather than race.

The deployment of social robots in public spaces has received increased interest over the past years. These robots need to process a wide array of personal data to offer services that are tailored to users’ requirements. While much research has been carried out regarding the creation of explainable content, little research has dealt with how data transparency - as a way to address uncertainty and concerns regarding the handling of personal data - is conveyed to users. To examine the impact of different transparency declarations on trust, performance, and robot perception, we conducted a virtual reality (VR) supported laboratory experiment with N = 53 participants who interacted with a robot in a public setting (a library). The interaction between users and robots was accompanied by information on the handling of users’ personal data using three different modalities (via posters, the robot’s tablet, or verbally). The results imply that, while all modalities are understandable and perceived as useful, there is no preference for any modality. Our findings contribute to HRI research by examining different modalities for transparency declarations, in an effort to foster understandable and transparent processing of data.

Conversational agents (CAs) lack of possibilities to enrich the interaction with emotional cues, although this makes the conversation more human-like and enhances user engagement. Thus, the potential of CAs is not fully exploit and possibilities to convey emotional cues are needed. In this work, CAs use colored ambient lights to display moral emotions during the interaction. To evaluate this approach, a between-subject lab experiment $(N=64)$ was conducted. Participants played a cooperation game with Amazon’s Alexa. Depending on the experimental condition participants received different light expressions: no light, neutral light, or morally emotional light (yellow = joy, blue = sorrow, red = anger matching the game decisions). The effect of the light expressions on the perception of the CA, users’ empathy and cooperation behavior was tested. Against our assumptions, the results indicated no positive effect of the emotional light cues. Limitations, next steps, and implications are discussed.

Studying in social isolation is a reality for many students that was further reinforced after the start of the COVID-19 pandemic. Research shows that isolation can lead to decreased learning efficiency and is intensified by the increased asynchronous online teaching during the pandemic. This change is not only challenging for students, but also for teachers, as students do not have a direct communication and feedback channel when learning content is presented in form of pre-recorded videos in a learning management system. In this paper, we present VGather2Learn Analytics, which is an extension to the already existing collaborative learning system VGather2Learn, which makes it possible for teachers to analyse the learning behavior of students in asynchronous video-teaching. The information presented in a dashboard will allow teachers to better understand how students interact while watching learning videos collaboratively and can improve online-teaching.

Street harassment, especially against women, is a prevalent phenomenon also known as catcalling. Numerous campaigns have tried to raise awareness for practices like publicly commenting on women's bodies, whistling or unwanted sexual advances. Recently, activists against catcalling have been especially present on Social Media, outreaching to large international audiences. However, effective ways still have to be found to stimulate an intense reflexion and raise empathy, especially among aggressors and bystanders. Using a virtual reality gallery, including diverse multimedia material and feedback options to stimulate a discussion among visitors, we create an immersive, educating and interactive experience. Rather than moralizing, the gallery promotes a self-paced exploration of the material, combines personal stories with instructive facts to sensitize to street harassment.

The increase of social isolation amongst students due to remote teaching during the COVID-19 pandemic leads to decreased learning efficiency as well as extenuated satisfaction with students' learning experiences. In this paper we present an approach to tackle these problems by providing a tool that helps students organise collaborative learning with videos.