This study investigated a seven sessions interaction between a peer-tutor robot and Dutch preschoolers (5 years old) during which the children learned English. We examined whether children’s engagement differed when interacting with a tablet and a robot using iconic gestures, with a tablet and a robot using no iconic gestures and with only a tablet. Two engagement types were annotated (task engagement and robot engagement) using a novel coding scheme based on an existing coding scheme used in kindergartens. The findings revealed that children’s task engagement dropped over time in all three conditions, consistent with the novelty effect. However, there were no differences between the different conditions for task engagement. Interestingly, robot engagement showed a difference between conditions. Children were more robot engaged when interacting with a robot using iconic gestures than without iconic gestures. Finally, when comparing children’s word knowledge with their engagement, we found that both task engagement and robot engagement were positively correlated with children’s word retention.
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Several recent works in human-robot-interaction (HRI) have begun to highlight the importance of the replication crisis and open science practices for our field. Yet, suggestions and recommendations tailored to child-robot-interaction (CRI) research, which poses it's own additional set of challenges, remain limited. There is also an increased need within both HRI and CRI for inter and cross-disciplinary collaborations, where input from multiple different domains can contribute to better research outcomes. Consequently, this workshop aims to facilitate discussions between researchers from diverse disciplines within CRI. The workshop will open with a panel discussion between CRI researchers from different disciplines, followed by 3-minute flash talks of the accepted submissions. The second half of the workshop will consist of breakout group discussions, where both senior and junior academics from different disciplines can share their experiences of conducting CRI research. Through this workshop we hope to create a common ground for addressing shared challenges in CRI, as well as identify a set of possible solutions going forward.
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To benefit from the social capabilities of a robot math tutor, instead of being distracted by them, a novel approach is needed where the math task and the robot's social behaviors are better intertwined. We present concrete design specifications of how children can practice math via a personal conversation with a social robot and how the robot can scaffold instructions. We evaluated the designs with a three-session experimental user study (n = 130, 8-11 y.o.). Participants got better at math over time when the robot scaffolded instructions. Furthermore, the robot felt more as a friend when it personalized the conversation.
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Maintaining the child-robot relationship after a significant break, such as a holiday, is an important step for developing sustainable social robots for education. We ran a four-session user study (n = 113 children) that included a nine-month break between the third and fourth session. During the study, participants practiced math with the help of a social robot math tutor. We found that social personalization is an effective strategy to better sustain the child-robot relationship than the absence of social personalization. To become reacquainted after the long break, the robot summarizes a few pieces of information it had stored about the child. This gives children a feeling of being remembered, which is a key contributor to the effectiveness of social personalization. Enabling the robot to refer to information previously shared by the child is another key contributor to social personalization. Conditional for its effectiveness, however, is that children notice these memory references. Finally, although we found that children's interest in the tutoring content is related to relationship formation, personalizing the topics did not lead to more interest in the content. It seems likely that not all of the memory information that was used to personalize the content was up-to-date or socially relevant.
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Hospitalisation is stressful for children. Play material is often offered for distraction and comfort. Weexplored how contact with social robot PLEO could positively affect a child’s well-being. To this end, we performed a multiple case study on the paediatric ward of two hospitals. Child life specialists offered PLEO as a therapeutic activity to children in a personalised way for a well-being related purpose in three to five play like activity sessions during hospital visits/stay. Robot–child interaction was observed; care professionals, children and parents were interviewed. Applying direct content analysis revealed six categories of interest: interaction with PLEO, role of the adults, preferences for PLEO, PLEO as buddy, attainment of predetermined goal(s) and deployment of PLEO. Four girls and five boys, aged 4–13, had PLEO offered as a relief from stress or boredom or for physical stimulation. All but one started interacting with PLEO and showed behaviours like hugging, caring or technical exploration, promoting relaxation, activation and/or making contact. Interaction with PLEO contributed to achieving the well-being related purpose for six of them. PLEO was perceived as attractive to elicit play. Although data are limited, promising results emerge that the well-being of hospitalised children might be fostered by a personalised PLEO offer.
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The challenges facing primary education are significant: a growing teacher shortage, relatively high administrative burdens that contribute to work-related stress and an increasing diversity of children in the classroom. A promising new technology that can help teachers and children meet these challenges is the social robot. These physical robots often use artificial intelligence and can communicate with children by taking on social roles, such as that of a fellow classmate or teaching assistant. Previous research shows that the use of social robots can lead to better results in several ways than when traditional educational technologies are applied. However, social robots not only bring opportunities but also lead to new ethical questions. In my PhD research, I investigated the moral considerations of different stakeholders, such as parents and teachers, to create the first guideline for the responsible design and use of social robots for primary education. Various research methods were used for this study. First of all, a large, international literature study was carried out on the advantages and disadvantages of social robots, in which 256 studies were ultimately analysed. Focus group sessions were then held with stakeholders: a total of 118 parents of primary school children, representatives of the robotics industry, educational policymakers, government education advisors, teachers and primary school children contributed. Based on the insights from the literature review and the focus group sessions, a questionnaire was drawn up and distributed to all stakeholders. Based on 515 responses, we then classified stakeholder moral considerations. In the last study, based on in-depth interviews with teachers who used robots in their daily teaching and who supervised the child-robot interaction of >2500 unique children, we studied the influence of social robots on children's social-emotional development. Our research shows that social robots can have advantages and disadvantages for primary education. The diversity of disadvantages makes the responsible implementation of robots complex. However, overall, despite their concerns, all stakeholder groups viewed social robots as a potentially valuable tool. Many stakeholders are concerned about the possible negative effect of robots on children's social-emotional development. Our research shows that social robots currently do not seem to harm children's social-emotional development when used responsibly. However, some children seem to be more sensitive to excessive attachment to robots. Our research also shows that how people think about robots is influenced by several factors. For example, low-income stakeholders have a more sceptical attitude towards social robots in education. Other factors, such as age and level of education, were also strong predictors of the moral considerations of stakeholders. This research has resulted in a guideline for the responsible use of social robots as teaching assistants, which can be used by primary schools and robot builders. The guideline provides schools with tools, such as involving parents in advance and using robots to encourage human contact. School administrators are also given insight into possible reactions from parents and other parties involved. The guideline also offers guidelines for safeguarding privacy, such as data minimization and improving the technical infrastructure of schools and robots; which still often leaves much to be desired. In short, the findings from this thesis provide a solid stepping stone for schools, robot designers, programmers and engineers to develop and use social robots in education in a morally responsible manner. This research has thus paved the way for more research into robots as assistive technology in primary education.
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Maintaining the child-robot relationship after a significant break, such as a holiday, is an important step for developing sustainable social robots for education. We ran a four-session user study (n = 113 children) that included a nine-month break between the third and fourth session. During the study, participants practiced math with the help of a social robot math tutor. We found that social personalization is an effective strategy to better sustain the child-robot relationship than the absence of social personalization. To become reacquainted after the long break, the robot summarizes a few pieces of information it had stored about the child. This gives children a feeling of being remembered, which is a key contributor to the effectiveness of social personalization. Enabling the robot to refer to information previously shared by the child is another key contributor to social personalization. Conditional for its effectiveness, however, is that children notice these memory references. Finally, although we found that children's interest in the tutoring content is related to relationship formation, personalizing the topics did not lead to more interest in the content. It seems likely that not all of the memory information that was used to personalize the content was up-to-date or socially relevant.
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In this paper, we examine to what degree children of 3–4 years old engage with a task and with a social robot during a second-language tutoring lesson. We specifically investigated whether children’s task engagement and robot engagement were influenced by three different feedback types by the robot: adult-like feedback, peer-like feedback and no feedback. Additionally, we investigated the relation between children’s eye gaze fixations and their task engagement and robot engagement. Fifty-eight Dutch children participated in an English counting task with a social robot and physical blocks. We found that, overall, children in the three conditions showed similar task engagement and robot engagement; however, within each condition, they showed large individual differences. Additionally, regression analyses revealed that there is a relation between children’s eye-gaze direction and engagement. Our findings showed that although eye gaze plays a significant role in measuring engagement and can be used to model children’s task engagement and robot engagement, it does not account for the full concept and engagement still comprises more than just eye gaze.
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To benefit from the social capabilities of a robot math tutor, instead of being distracted by them, a novel approach is needed where the math task and the robot's social behaviors are better intertwined. We present concrete design specifications of how children can practice math via a personal conversation with a social robot and how the robot can scaffold instructions. We evaluated the designs with a three-session experimental user study (n = 130, 8-11 y.o.). Participants got better at math over time when the robot scaffolded instructions. Furthermore, the robot felt more as a friend when it personalized the conversation.
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In this paper, we examine the process of designing robot-performed iconic hand gestures in the context of a long-term study into second language tutoring with children of approximately 5 years old. We explore four factors that may relate to their efficacy in supporting second language tutoring: the age of participating children; differences between gestures for various semantic categories, e.g. measurement words, such as small, versus counting words, such as five; the quality (comprehensibility) of the robot’s gestures; and spontaneous reenactment or imitation of the gestures. Age was found to relate to children’s learning outcomes, with older children benefiting more from the robot’s iconic gestures than younger children, particularly for measurement words. We found no conclusive evidence that the quality of the gestures or spontaneous reenactment of said gestures related to learning outcomes. We further propose several improvements to the process of designing and implementing a robot’s iconic gesture repertoire.
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