Transparency, teleoperation, and children's understanding of social robots

Teleoperation or Wizard-of-Oz control of social robots is commonly used in human-robot interaction (HRI) research. This is especially true for child-robot interactions, where technologies like speech recognition (which can help create autonomous interactions for adults) work less well. We propose to study young children's understanding teleoperation, how they conceptualize social robots in a learning context, and how this affects their interactions. Children will be told about the teleoperator's presence either before or after an interaction with a social robot. We will assess children's behavior, learning, and emotions before, during, and after the interaction. Our goal is to learn whether children's knowledge about the teleoperator matters (e.g., for their trust and for learning outcomes), and if so, how and when it matters most (e.g. at what age)

Young Children Treat Robots as Informants

Children ranging from 3 to 5 years were introduced to two anthropomorphic robots that provided them with information about unfamiliar animals. Children treated the robots as interlocutors. They supplied information to the robots and retained what the robots told them. Children also treated the robots as informants from whom they could seek information. Consistent with studies of children's early sensitivity to an interlocutor's non-verbal signals, children were especially attentive and receptive to whichever robot displayed the greater non-verbal contingency. Such selective information seeking is consistent with recent findings showing that although young children learn from others, they are selective with respect to the informants that they question or endorse

Creating long-term interpersonal interaction, rapport, and relationships with social robots

Thesis: Ph. D., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2019Cataloged from PDF version of thesis.Includes bibliographical references (pages 266-294).Children are now growing up with Al-enabled, socially interactive technology. As such, we need to deeply understand how children perceive, interact, and relate to this kind of technology, especially given the many ethical concerns that arise in the context of human-machine interactions, most of which are most contentious with children. To this end, I explore questions about young children's interactions and relationships with one such technology--social robots-during language learning activities. Language learning is a ripe area for exploring these questions because of the social, interactive, interpersonal nature of the activity. In addition, literacy, language, and interpersonal skills are some of the most important skills any child will learn, as they can greatly impact children's later educational and life success.Through a series of 9 empirical child-robot interaction studies with 347 children and using both teleoperated and autonomous robots, I establish the role of social robots as relational technology-that is, technology that can build long-term, social-emotional relationships with users. I hypothesize that a key aspect of why social robots can benefit children's learning is their social and relational nature. To that end, I demonstrate the capabilities of social robots as learning companions for young children that afford opportunities for social engagement and reciprocal interaction, particularly peer-to-peer mirroring. I discuss how we can understand children's conceptualizations of social robots as relational agents and measure children's relationships over time. I introduce the term relational AI to refer to autonomous relational technologies.I develop a computational relational Al system to examine how using relational Al in a social robot can impact child-robot learning interactions. Through testing the autonomous system in a longitudinal study with 49 children, I explore connections between children's relationship and rapport with the robot and their engagement and learning. I discuss the ethical use and design implications of relational AL. I show that relational AI is a new, powerful educational tool, unlike any other existing technology, that we can leverage to support children's early education and development."Supported by a MIT Media Lab Learning Innovation Fellowship, and by the National Science Foundation (NSF) under Grants CCF-1 3 89 86, IIS-1122886, IIS-11228 4 5, IIS-112308 5 , IIS-1523118, and Graduate Research Fellowship Grant No. 1122374"--Page 6by Jacqueline M. Kory-Westlund.Ph. D.Ph.D. Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Science

A Long-Term Study of Young Children's Rapport, Social Emulation, and Language Learning With a Peer-Like Robot Playmate in Preschool

Prior research has demonstrated the importance of children's peers for their learning and development. In particular, peer interaction, especially with more advanced peers, can enhance preschool children's language growth. In this paper, we explore one factor that may modulate children's language learning with a peer-like social robot: rapport. We explore connections between preschool children's learning, rapport, and emulation of the robot's language during a storytelling intervention. We performed a long-term field study in a preschool with 17 children aged 4–6 years. Children played a storytelling game with a social robot for 8 sessions over two months. For some children, the robot matched the level of its stories to the children's language ability, acting as a slightly more advanced peer (Matched condition); for the others, the robot did not match the story level (Unmatched condition). We examined children's use of target vocabulary words and key phrases used by the robot, children's emulation of the robot's stories during their own storytelling, and children's language style matching (LSM—a measure of overlap in function word use and speaking style associated with rapport and relationship) to see whether they mirrored the robot more over time. We found that not only did children emulate the robot more over time, but also, children who emulated more of the robot's phrases during storytelling scored higher on the vocabulary posttest. Children with higher LSM scores were more likely to emulate the robot's content words in their stories. Furthermore, the robot's personalization in the Matched condition led to increases in both children's emulation and their LSM scores. Together, these results suggest first, that interacting with a more advanced peer is beneficial for children, and second, that children's emulation of the robot's language may be related to their rapport and their learning. This is the first study to empirically support that rapport may be a modulating factor in children's peer learning, and furthermore, that a social robot can serve as an effective intervention for language development by leveraging this insight. Keyword: Children; Language development; Mimicry; Peer modeling; Rapport; Relationship; Social robotics; StorytellingNational Science Foundation (U.S.) (Grant 122886)National Science Foundation (U.S.) (Grant CCF-1138986)National Science Foundation (U.S.). Graduate Research Fellowship Program (Grant 1122374

Lessons from teachers on performing HRI studies with young children in schools

We deployed an autonomous social robotic learning companion in three preschool classrooms at an American public school for two months. Before and after this deployment, we asked the teachers and teaching assistants who worked in the classrooms about their views on the use of social robots in preschool education. We found that teachers' expectations about the experience of having a robot in their classrooms often did not match up with their actual experience. These teachers generally expected the robot to be disruptive, but found that it was not, and furthermore, had numerous positive ideas about the robot's potential as a new educational tool for their classrooms. Based on these interviews, we provide a summary of lessons we learned about running child-robot interaction studies in preschools. We share some advice for future researchers who may wish to engage teachers and schools in the course of their own human-robot interaction work. Understanding the teachers, the classroom environment, and the constraints involved is especially important for microgenetic and longitudinal studies, which require more of the school's time-as well as more of the researchers' time-and is a greater opportunity investment for everyone involved.National Science Foundation (U.S.) (Grant CCF–1138986)National Science Foundation (U.S.) (Graduate Research Fellowship Grant No 1122374