Trust is fundamental in Human-Robot Interaction (HRI) and can be influenced by factors such as the robot’s appearance, and texture. Research suggests that children can develop trust with robots, particularly humanoid ones; however, other studies raise concerns about robotic behaviors and the extent to which they are perceived as trustworthy. In this study, we investigate whether the co-designing of clothes for a Socially Assistive Robot (SAR) affects their trust in the SAR, specifically the Nao robot. Children aged 7-10 in small group workshops created with the needle felting technique clothes for Nao, guided by the robot itself and an expert. Each child made their own piece of clothing. Their a) trust, b) empathy, c) perceived liveliness, and d) negative attitudes before and after making the clothes were assessed with pre-post questionnaires, indicating a significant increase in trust and empathy. After making the clothes, each child interacted alone with 3 Nao robots; one with default appearance, one dressed in the clothes they made, and one with other similar clothes, and were invited to share a secret wish with one of them. Behavioral observations, individual interviews and linguistic analysis of the wishes, provided insights into factors influencing children’s trust and appearance preferences in HRI. Results indicated that most children chose to confide in the robot who wore their crafted outfit, expressing personal desires grammatically with the verb ’wish’ in contrast to the children who shared immediate desires with the verb ’want’ and chose the default robot. Finally, interviews indicated that the primary characteristics that make a SAR trustworthy to children are kindness, intelligence, anthropomorphism, and helpfulness.

Many researchers are working to address the worldwide mental health crisis by developing therapeutic technologies that increase the accessibility of care, including leveraging large language model (LLM) capabilities in chatbots and socially assistive robots (SARs) used for therapeutic applications. Yet, the effects of these technologies over time remain unexplored. In this study, we use a factorial design to assess the impact of embodiment and time spent engaging in therapeutic exercises on participant disclosures. We assessed transcripts gathered from a two-week study in which 26 university student participants completed daily interactive Cognitive Behavioral Therapy (CBT) exercises in their residences using either an LLM-powered SAR or a disembodied chatbot. We evaluated the levels of active engagement and high intimacy of their disclosures (opinions, judgments, and emotions) during each session and over time. Our findings show significant interactions between time and embodiment for both outcome measures: participant engagement and intimacy increased over time in the physical robot condition, while both measures decreased in the chatbot condition.

The use of social robots in service tasks is spreading, showcasing advantages for both consumers and service providers. However, their widespread adoption is hindered by a notable lack of trust. Our study aims to uncover insights into the factors influencing the adoption of social robots in home settings, exploring the factors that lead users to trust and eventually adopt robots. We designed two experimental conditions, presenting the Amazon Astro robot from different perspectives (high-angle and eye-level) and demonstrating its different abilities to 198 people recruited from MTurk. We employed both quantitative (trust, first impressions of warmth and competence as well as usability, familiarity, and attitudes) questionnaires and qualitative (word analysis) assessments, and results showed that participants had higher trust scores when seeing the robot from an eye-level perspective. In addition, usability, familiarity and competence were shown to explain a significant amount of variance in trust. While existing negative attitudes towards robots and the participants’ age were shown to be the strongest predictors for participants’ willingness to purchase a robot, trust was able to significantly affect use intention. We contribute to the broader understanding of the challenges and opportunities in integrating social robots into daily life, shedding light on the dynamics between technological innovation and consumer adoption.

Mindfulness-based therapies have been shown to be effective in improving mental health, and technology-based methods have the potential to expand the accessibility of these therapies. To enable real-time personalized content generation for mindfulness practice in these methods, high-quality computer-synthesized text-to-speech (TTS) voices are needed to provide verbal guidance and respond to user performance and preferences. However, the user-perceived quality of state-of-the-art TTS voices has not yet been evaluated for administering mindfulness meditation, which requires emotional expressiveness. In addition, work has not yet been done to study the effect of physical embodiment and personalization on the user-perceived quality of TTS voices for mindfulness. To that end, we designed a two-phase human subject study. In Phase 1, an online Mechanical Turk between-subject study (N=471) evaluated 3 (feminine, masculine, child-like) state-of-the-art TTS voices with 2 (feminine, masculine) human therapists' voices in 3 different physical embodiment settings (no agent, conversational agent, socially assistive robot) with remote participants. Building on findings from Phase 1, in Phase 2, an in-person within-subject study (N=94), we used a novel framework we developed for personalizing TTS voices based on user preferences, and evaluated user-perceived quality compared to best-rated non-personalized voices from Phase 1. We found that the best-rated human voice was perceived better than all TTS voices; the emotional expressiveness and naturalness of TTS voices were poorly rated, while users were satisfied with the clarity of TTS voices. Surprisingly, by allowing users to fine-tune TTS voice features, the user-personalized TTS voices could perform almost as well as human voices, suggesting user personalization could be a simple and very effective tool to improve user-perceived quality of TTS voice.

Virtual, augmented, and mixed reality for human-robot interaction (VAM-HRI) is a new and rapidly growing field of research. The field of socially assistive robot (SAR) has made impactful advances in educational settings, but has not yet benefited from VAM-HRI advances. We developed MoveToCode - an open-source, embodied (i.e., kinesthetic) learning visual programming language that aims to increase student (ages 8-12) curiosity during programming. MoveToCode uses an augmented reality (AR) autonomous robot tutor named Kuri that models the students’ kinesthetic curiosity and acts to promote their curiosity in programming. MoveToCode design was informed by pilot studies and tested in Los Angeles elementary classrooms $(n =21)$. Results from main study validated our design decisions compared to the pilot study which was conducted in a real elementary school classroom environment $(n =15)$, showing an improvement in perceived robot helpfulness (median $+ \Delta1.25$ out of 5) and number of completed exercises (median $+ \Delta1$, maximum of 11). While no significant changes were found in pre/post student curiosity or intention to program later in life, students wrote more open-ended questions post-study on topics related to robots, programming, research, and if they would like to do the activity again. This work demonstrates the potential of using VAM-HRI in a kinesthetic context for SAR tutors, and highlights the existing conventions and new design considerations for creating AR applications for SAR.

Traffic safety education is the key to safe road behavior and can prevent severe and fatal accidents, and it is suggested starting from an early age. Technologies such as Augmented Reality (AR) and Socially Assistive Robots (SAR) have been successfully used in the last years in the educational field. Therefore, it is essential to identify how social robots and AR educational tools can be optimally used to support traffic safety education. In this paper, we introduce the idea of an educational approach with the combination of SAR with AR technologies to teach traffic safety in kindergarten. We have been inspired by evidence from human-robot interaction studies, suggesting both technologies can enhance gained knowledge using the storytelling method at an early age and trigger positive attitudes in the students after interacting with them. Our educational approach was benchmarked against a single AR application. Our study initially proved, through real-world experiments, that the combination of SAR and AR was successfully used to teach kindergarten students about traffic safety. It managed to sustain their attention for statistically significantly more time than the case of the single AR application. Students taught by the single AR application also managed to gain basic knowledge regarding traffic safety. Moreover, another important outcome of our study is that students improved their attitudes and beliefs regarding using SAR in education after interacting with it, as demonstrated by individual interviews. Hence, our results suggest that both AR applications and SAR are suitable for learning purposes in kindergarten, while the employment of SAR in AR educational applications sustains the students’ attention.

In the coming years, social robots will be widely used in the educational field. Therefore, it is important to identify the future educators’ attitudes towards the use of robots and technological tools. In this paper, we investigate how the exposure of students/pre-service teachers in a robot-tutor can affect their attitudes towards the use of robots in education. We compare a university professor with a robot-tutor in a university class and we demonstrate how each one changes the students’ attitudes towards the use of robots and technology in education as well as the students learning outcome from the course. Furthermore, based on the results of the four experiments we conducted, we also demonstrate the changes in students’ attitudes when they have just a single lesson with a robot-tutor and when they have two courses. Our results demonstrate all students were more positive about the use of robots (and technology) in education after having a course with the robot-tutor than when they had a lecture with a human. Moreover, they had a similar knowledge acquisition level, as indicated by the given knowledge test in both human and robot-tutor conditions. Especially in the case that they had a second lecture with the robot-tutor, they got statistically significantly higher scores in the test in comparison with those who had only one lecture. Our results suggest that a robot-tutor can be an effective tutor, capable of changing students’ attitudes and improving knowledge. Moreover, since the university students in our experiments were pre-service teachers, our paper can act as a reference for teaching, the next generation of teachers, that a robot-tutor can be a useful colleague.

In the coming years tour guide robots will be widely used in museums and exhibitions. Therefore, it is important to identify how these new museum guides can optimally interact with visitors. In this paper, we introduce the idea of two collaborative tour guide robots. We have been inspired by evidence from cognitive studies stating that people remember more when they receive information from two different human speakers. Our collaborative tour guides were benchmarked against single robot guides. Our study initially proved, through real-world experiments, previous proposals stating that the personality of the robot affects the human learning process; our results demonstrate that people remember significantly more information when they are guided by a cheerful robot than when their guide is a serious one. Moreover, another important outcome of our study is that our visitors tend to like more our collaborative robots, than any referenced single robot, as demonstrated by the higher scores in the aesthetic-related questions. Hence our results suggest that a cheerful robot is more suitable for learning purposes while two robots are more suitable for entertainment purposes.