Social robots can support children’s emotional skills development through playful interactions, yet skills like emotional self-disclosure remain underexplored. This study investigates the impact of a social robot designed to encourage emotional self-disclosure during an emotion-identification game with children aged 6-10. In a between-subjects design with 28 participants across two local schools, we compared a Reflective condition, where the robot actively encouraged emotional self-disclosure through question-asking and reciprocal sharing, to a Control condition, where the robot did not. Children in the Reflective condition engaged in emotional self-disclosure when prompted, and showed higher engagement than those in Control. Direct question-asking was more effective than reciprocal self-disclosure. Results suggested that children who perceived the robot as kinder disclosed more, whereas those who viewed it as more real disclosed less. These findings highlight the potential of social robots to foster emotional skills in children and inform the design of future child-robot interaction research.

Study design is critical in Human-Robot Interaction (HRI) due to its highly interdisciplinary nature, that spans a wide range of domains and often presents significant hurdles for novice researchers. Building on the success of the previous years, the fourth edition of the “Workshop Your Study Design” addresses this challenge by pairing early-stage researchers with experienced mentors to refine their study designs and offering mentor-led talks on key HRI topics, tailored to this year's conference sustainability theme. Participants are invited to submit a 2–4 page study proposal focusing on methodology and data analysis. Accepted submissions are discussed in small mentor-led groups, where participants receive targeted feedback on their study designs and can also benefit from engaging in discussions about other participants' submissions. Our expert mentors this year bring expertise in areas such as participatory design, qualitative analysis and longitudinal studies. Thus, the workshop aims to foster a collaborative environment where participants can not only strengthen their study designs, but also gain valuable insights and build lasting connections within the HRI community.

This full-day workshop focuses on advancing research and development in socially-competent agents that care. Many agent platforms and kinds of embodiments of agents that have a primary focus on caring, one way or another, for the end users. In this workshop, we aim to look at value-based design, inclusiveness, empathetic design and long-term human-agent interaction for socially competent agents that care.

People’s ascription of intentional agency to robots necessitates understanding how, when, and why people attribute robot behavior to underlying intentional states. While many studies explored mind attribution to robots including its determinants and consequences, little attention has been given to the attribution of second-order mental states, such as a robot’s beliefs about people’s intentions during interactions. In an online study (n = 155), participants watched a video of a humanoid robot tracking a ball hidden under one of two cups. 19% of participants predicted that the robot could correctly locate the ball after it was displaced twice by a person deliberately distracting the robot, indicating implicit attribution of second-order beliefs to the robot. These implicit attributions influenced participants’ actions in a subsequent interactive game with a virtual counterpart of the robot but did not affect their explicit assessments of the robot’s second-order reasoning. In contrast, observing the robot demonstrate second-order reasoning by correctly identifying the ball’s location affected participants’ explicit attributions but not their behavior in the interactive game. This reveals a complex interplay between implicit and explicit attribution processes in how people interpret robot behavior.

Children encounter high levels of stress and anxiety before receiving medical treatment, such as a vaccination. This paper explores the effect of emotional gesturing in socially assistive robots (SARs) on children's observed and self-reported engagement, as well as self-reported anxiety, fear, and trust during a group vaccination. A total of 249 children interacted with the social robot iPal before and after receiving the vaccine. Our results show an overall positive effect of adding emotional gestures to a SAR's interaction behavior leading to increased engagement and lower anxiety, while increased engagement also resulted in trusting the robot more. Thus, adding emotional gestures during child-robot interaction is a powerful way to improve the child's experience during a group vaccination day.

Social robots can support children in their socio-emotional development [38]. To improve the cooperation between a child and a social robot, a good relationship is vital. Self-disclosure is an essential element for building personal relationships. Yet, knowledge about the effects of self-disclosure in child-robot interactions is still lacking. To investigate effects of robot persona, child personality, and self-disclosure category on self-disclosure in child-robot interaction, we have conducted a field study at a science festival in which children had a conversation with a robot that either behaved human-like or robot-like. The results show a significant difference in the amount of self-disclosure (in conversation duration) between the two robot personas. Additionally, significant relationships were found between conscientiousness and extraversion and amount of self-disclosure (in word count). The participant disclosed significantly more about the category `Attitudes and Opinions’ than about ‘School’. Finally, a thematic analysis shows that the content of the conversations can be categorised in five plus one themes. Between robot personas, the content of the conversations did not differ in terms of conversation themes. However, in both conditions, we found that children generally feel comfortable sharing unpleasant experiences about present themes (such as COVID) in a first encounter with a robot.

HRI is an interdisciplinary field that requires researchers to be knowledgeable in broad areas ranging from social sciences to engineering. Study design is a multifaceted aspect of HRI that is hard to develop and perfect. Thus, the second edition of the “Workshop Your Study Design” workshop aims to improve the quality of future HRI studies by training researchers and boosting the accessibility of HRI as a field. Participants will have the opportunity to receive guidance and feedback on their study from an expert mentor. Researchers from all avenues of HRI will be invited to submit a 2–4 page paper on an HRI study they are currently designing, including a brief introduction and a complete methods section. Accepted submissions will be discussed in small groups led by mentors with relevant expertise. Prior to the workshop, papers will be shared within each group. Participants will be encouraged to read other submissions. During the workshop, attendees will work within their menteementor groups to discuss each paper and provide feedback. There will also be a session where mentors lead mini discussions on topics important to study design, such as balancing qualitative and quantitative design, power analysis, and research ethics. The workshop will end with a session where all participants can share important lessons that they learned with fellow attendees.

Self-disclosures can be valuable and sensitive parts of the human-robot interaction. This paper investigates how far human's tendency to self-disclose depends on the topic of interaction, individual's personality and perceived robot identity (i.e., human-, robot- or animal-like). Robot's (Pepper) identity was shown in its self-disclosure, interaction behaviors (gestures, sound and voice), and ’’clothing". In an"in-the- wild" study at a science festival, 80 visitors interacted with one of these robot identities. When questioned by the robot, they disclosed more about their attitudes and opinions than about other categories. Significant correlations appeared between personality characteristics and the degree of self-disclosure, as well as differences in self-disclosure categories. The different robot identities showed no effects on disclosures.

This extended research abstract for the Doctoral Consortium at IDC 2021 describes a 5-year PhD project, started November 2019, on self-disclosure in child-robot interaction in the field of child and family care. The research design embraces a bottom-up participatory design approach including all stakeholders, based on qualitative as well as quantitative methods. This PhD research is guided by Dr. M.M.A. de Graaf and Prof. dr. ir. J.F.M. Masthoff.

Robots will increasingly collaborate with human partners necessitating research into how robots negotiate negative collaborative outcomes. This study investigates the effect of blame attribution on trust assessments in human-robot collaboration. Participants (n = 60) collaboratively played a game with a humanoid robot in one of four conditions in a 2 (blame correctness: correct vs. incorrect) by 2 (blame target: human vs. robot) between-subjects experiment. Results show that people evaluate a robot more positively when it blames itself for collaborative failures, especially, it seems, in the case of incorrect self-blame. Our findings indicate a need to further research on effective communication strategies for robots that need to negotiate collaborative failures without compromising the trust relationships with its human partner.

This paper investigates the experiences of Italian and Dutch children while interacting with a social robot that is designed to support their diabetes self-management. Observations of children's behaviors and analyses of questionnaires at diabetes camps, showed positive experiences with variation (e.g., Italian children seemed to be more open and expressive, and more close to the robot compared to the Dutch children). A culture-aware robot should be sensitive to such differences.