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.

With the rising prevalence of social robots in public spaces, an increasingly heterogeneous audience of people from across different characteristics (such as age or ethnicity) are possible users. In such diverse interactions, there is a risk of algorithmic bias, which results in a discrimination of certain user groups. Human-robot interaction (HRI) research has thus far predominantly focused on homogeneous samples to describe instances and consequences of algorithmic bias, which is prone to leading to evasive findings. We address this gap by conducting thirteen focus group interviews with a total of N = 92 participants and exploring if and how people have already experienced algorithmic bias in their daily lives. Additionally, we contrast the findings from our socially diversified sample with those obtained from a more homogeneous group of people. Our findings uncover various experiences and coping mechanisms regarding algorithmic bias and demonstrate that examining a more diverse sample reveals findings that would otherwise have remained unnoticed.

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.

Recently, the use of smart glasses in healthcare professions has been increasingly discussed, but unlike in established application contexts such as logistics, healthcare professions depend on trusting interactions with patients. Research has mostly focused on the usefulness and usability of smart glasses and neglects the perspective of the interacting partner. In order to examine how patients perceive and evaluate the wearing and use of smart glasses by healthcare professionals, we used a mixed methods approach. First, we conducted an exploratory 7(glasses design) x2(sex) x2(social status) online experiment (N = 415) followed by a focus group study. While the experiment showed that the glasses design is most decisive to impression formation, the focus group study revealed that the smart glasses evoked equal amounts of confusion and curiosity among the observers. It seems that smart glasses are rather perceived as something attached to the body rather than as part of the body.

Past examples have illustrated the potential for virtual reality (VR) in educational settings, allowing students the exploration of complex and otherwise hardly accessible learning objects and content. We introduce a VR-photovoltaics learning application. Replicated from a real photovoltaics(PV)-array, we designed a VR-application where students can inspect PV-modules and explore their distinct characteristics. By using current and historical data from the real PV-array through a network interface, combined with a dynamic weather system, a great versatility in scenarios to experiment and learn with can be created. In its modular conceptualization, the current version of the application offers flexible expandability for diverse learning scenarios with multiple data sources.

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.

In open online learning courses such as MOOCs, peer feedback has been regarded as a powerful method to give elaborated feedback on weekly assignments. Yet motivating students to invest effort in peer feedback on top of existing work load is difficult. Students might give insufficient feedback or do not give feedback at all. Students' hesitation to provide feedback might be related to the lack of visibility of spent effort during feedback provision. Alternatively, students might provide less feedback due to lack of perceived benefits. In this study, we investigated the effect of two incentive types on peer feedback provision on weekly assignments. In total, 91 students enrolled in a semi-open online course were announced to receive either (1) a peer rating on their feedback or (2) open access to assignment solutions or (3) no incentive. Results indicate that the incentive type did not affect feedback provision in general, yet it had an impact on the content of the feedback. Students receiving (1) a rating-feedback incentive wrote longer and more specific feedback in comparison to students receiving (2) an information-access incentive or (3) no incentive. Results contribute to findings from peer assessment research that students are more likely to provide detailed feedback if students feel that feedback is attended to. Furthermore, results inform teachers and practitioners on how to encourage students to provide peer feedback in open learning environments.

The EU project SERA (Social Engagement with Robots and Agents) provided the unique opportunity to collect real field data of people interacting with a robot companion in their homes. In the course of three iterations, altogether six elderly participants took part. Following a multi-methodological approach, the continuous quantitative and qualitative description of user behavior on a very fine-grained level gave us insights into when and how people interacted with the robot companion. Post-trial semi-structured interviews explored how the users perceived the companion and revealed their attitudes. Based on this large data set, conclusions can be drawn on whether people show signs of bonding and how their relation to the robot develops over time. Results indicate large inter-individual differences with regard to interaction behavior and attitudes. Implications for research on companions are discussed.

In this paper we present a communication and discussion toolkit based on sending short messages designed for use in schools. This toolkit is the result of a student's project at the University of Duisburg-Essen in collaboration with a local secondary school. Our approach uses PDAs in a wireless network to build an environment which emulates sending of short messages with mobile phones. The messages created by the students are collected in a database and establish a base for a discussion and analysis later on using Cool Modes, a graph based modeling and discussion system.