Social robots have predominantly been deployed in resource-rich environments and managed by technical research teams. We propose a socially sustainable framework for deploying social robots in underrepresented, under-resourced communities. This approach emphasizes population-specific social impact, affordable robotic platforms, and “programming“ and operation by local community members. We present a holistic and sociallyecological case study of a unique population: Arabic elementary school children in Israel who face specific linguistic challenges. Learning Hebrew as a second language is crucial for their future, but teaching resources are limited. We developed a unique curriculum designed to teach bilingual morphological awareness in a fun and engaging way, implementing it through social robotic platforms. These platforms were selected based on social sustainability criteria, including affordability, lack of required programming skills by educators, and ease of deployment in schools. In two long-term, in-the-wild studies conducted at two different elementary schools (N=135, N=87), we utilized first Patricc and then Valera, both open-source, 3D-printed robots. These robots were “programmed“ by a non-technical educator and operated by local teachers. After six activities conducted over several weeks, our results show significant positive learning gains in both Arabic and Hebrew morphological awareness. We discuss the implications of our socially sustainable framework for using social robots in underrepresented and under-resourced communities.

Social robots, designed to interact with people through natural communication modes like speech, body motion, gestures, and facial expressions, have been extensively studied in child-robot interaction for educational purposes. Recently, social robots have been explored in triadic parent-child-robot interactions, showing promise due to their interactivity, computational power, and physical presence, which enable multimodal natural communication and cater to toddlers' developmental stages and physical curiosity. However, these have focused only on shared reading experiences and engaged older children, rather than toddlers. We developed two games, one with two levels of robot scaffolding, and another with either structured or unstructured design. We then explored, in two studies, how a social robot's assigned role and behaviors influence the engagement of parents and toddlers with the robot and their interaction with each other. Our results show that parents affectively scaffolded their children less when the robot increased its scaffolding behaviors and that parents provided more scaffolding in a structured game with the robot, whereas in an unstructured game the dyad exhibited more cooperation in which children exhibited more independence. These findings can contribute to a better understanding of interaction design, triadic dynamics, and the role of the robot in parent-toddler-robot scenario.

The role of social robots as advisors for decision making is investigated. It has been consistently shown that when asked to rank options, people often make fallacious judgements. Furthermore, such fallacies can be sensitive to presentation mode. We study whether having social robot advisors presenting options can mitigate and reduce the fallacy rates of participants. For this purpose we explored a novel presentation mode of options with conjunction judgmental fallacy, namely, choosing among different rank-orders, as opposed to rank the options themselves. We first show that the mere presentation mode has a significant mitigating effect on the fallacy rates. We then further show that when social robot advisors present the rank-orders, the fallacy rates of participants significantly decrease even further. Moreover, participants perceive the fallacious robot as more likeable and intelligent, but assign the non-fallacious robot to trustworthy roles, such as jury and analyst. These results suggest that social robot advisors may be used to influence and mitigate human fallacious judgmental decision making.

The COVID-19 pandemic has stressed the importance of efficient and accommodating online educational experiences. In this contribution, we present a novel system for the facilitation of small group online discussions using an avatar during video conferencing. The avatar was programmed with group facilitation best practices, whereas the content for the activities was prepared by the classes' teachers. Groups of tenth grade students interacted with the system, where we compared activities facilitated by the avatar with activities without facilitation. Our results show that students reported the activity with the avatar to be significantly more efficient, more understandable and inducing more participation compared to activities without avatar facilitation. Students also spoke significantly more with avatar facilitation. This system shows promise in future online educational activities as a facilitator of discussions with K-12 students.

Artificial curiosity, based on developmental psychology concepts wherein an agent attempts to maximize its learning progress, has gained much attention in recent years. Similarly, social robots are slowly integrating into our daily lives, in schools, factories, and in our homes. In this contribution, we integrate recent advances in artificial curiosity and social robots into a single expressive cognitive architecture. It is composed of artificial curiosity and social expressivity modules and their unique link, i.e., the robot verbally and non-verbally communicates its internally estimated learning progress, or learnability, to its human companion. We implemented this architecture in an interaction where a fully autonomous robot took turns with a child trying to select and solve tangram puzzles on a tablet. During the curious robot’s turn, it selected its estimated most learnable tangram to play, communicated its selection to the child, and then attempted at solving it. We validated the implemented architecture and showed that the robot learned, estimated its learnability, and improved when its selection was based on its learnability estimation. Moreover, we ran a comparison study between curious and non-curious robots, and showed that the robot’s curiosity-based behavior influenced the child’s selections. Based on the artificial curiosity module of the robot, we have formulated an equation that estimates each child’s moment-by-moment curiosity based on their selections. This analysis revealed an overall significant decrease in estimated curiosity during the interaction. However, this drop in estimated curiosity was significantly larger with the non-curious robot, compared to the curious one. These results suggest that the new architecture is a promising new approach to integrate state-of-the-art curiosity-based algorithms to the growing field of social robots.

While social robots for education are slowly being integrated in many scenarios, ranging from higher-education, through elementary school and kindergarten, the use case of robots for toddlers in their homes has not gained much attention. In this contribution, we introduce Patricc, a robotic platform that is specifically designed for toddler-parent-robot triadic interaction. It addresses the unique challenges of this age group, namely, desire for continuous physical interaction and novelty. Patricc's unique design enables changing characters by using dress-able puppets over a 3D-printed skeleton and the use of physical props. A novel authoring tool enables robot behavior and content creation by non-programmers. We conducted an evaluation study with 18 parent-toddler pairs and compared Patricc to similar tablet-based interactions. Our quantitative and qualitative analyses show that Patricc promotes significantly more triadic interaction, measured by video-coded gaze, compared to the tablet and that parents indeed perceive the interaction as triadic. Furthermore, there was no novelty-induced significant change in task-oriented behaviors, when toddlers interacted with two different characters consecutively. Finally, parents pointed out the benefits of changeable puppet-like characters over tablets and the appropriateness of the platform for the target age-group. These results suggest that Patricc can serve as the first gateway of toddlers to the emerging world of social robots.

Can we find real value for educational social robots in the very near future? We argue that the answer is yes. Specifically, in a classroom we observed, we identified a common gap: the instructor divided the class into small groups to work on a learning activity and could not address all their questions simultaneously. The purpose of this study was to examine whether social robots can assist in this scenario. In particular, we were interested to find whether a physical robot serves this purpose better than other technologies such as tablets. Benefits and drawbacks of the robot facilitator are discussed.

A novel use of social robots is introduced, namely, as an assessment tool for cognitive and social characteristics of human subjects. Social robots convey objectivity, repeatability and robustness which are highly important in assessment tools, and lacking in human and interaction-based methods. Here, we implement a fully autonomous social robot for the purpose of assessing physical curiosity of human subjects, namely, how do people explore novel physical-interaction scenarios. The complex interaction enables us to disambiguate learning, exploration and curiosity-based behaviors, where we show highly significant correlation between our extracted behavioral measures and external self-reported personality traits and Psychometric Entrance Test (PET) scores. Our results suggest that this novel experimental paradigm can be implemented in a host of social and physical assessment tasks.

Mindset has been shown to have a large impact on people's academic, social, and work achievements. A growth mindset, i.e., the belief that success comes from effort and perseverance, is a better indicator of higher achievements as compared to a fixed mindset, i.e., the belief that things are set and cannot be changed. Interventions aimed at promoting a growth mindset in children range from teaching about the brain's ability to learn and change, to playing computer games that grant brain points for effort rather than success. This work explores a novel paradigm to foster a growth mindset in young children where they play a puzzle solving game with a peer-like social robot. The social robot is fully autonomous and programmed with behaviors suggestive of it having either a growth mindset or a neutral mindset as it plays puzzle games with the child. We measure the mindset of children before and after interacting with the peer-like robot, in addition to measuring their problem solving behavior when faced with a challenging puzzle. We found that children who played with a growth-mindset robot 1) self-reported having a stronger growth mindset and 2) tried harder during a challenging task, as compared to children who played with the neutral-mindset robot. These results suggest that interacting with peer-like social robot with a growth mindset can promote the same mindset in children.

Though substantial research has been dedicated towards using technology to improve education, no current methods are as effective as one-on-one tutoring. A critical, though relatively understudied, aspect of effective tutoring is modulating the student's affective state throughout the tutoring session in order to maximize long-term learning gains. We developed an integrated experimental paradigm in which children play a second-language learning game on a tablet, in collaboration with a fully autonomous social robotic learning companion. As part of the system, we measured children's valence and engagement via an automatic facial expression analysis system. These signals were combined into a reward signal that fed into the robot's affective reinforcement learning algorithm. Over several sessions, the robot played the game and personalized its motivational strategies (using verbal and non-verbal actions) to each student. We evaluated this system with 34 children in preschool classrooms for a duration of two months. We saw that (1) children learned new words from the repeated tutoring sessions, (2) the affective policy personalized to students over the duration of the study, and (3) students who interacted with a robot that personalized its affective feedback strategy showed a significant increase in valence, as compared to students who interacted with a non-personalizing robot. This integrated system of tablet-based educational content, affective sensing, affective policy learning, and an autonomous social robot holds great promise for a more comprehensive approach to personalized tutoring.

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.

Tega is a new expressive “squash and stretch”, Android-based social robot platform, designed to enable long-term interactions with children.

Effective tutoring requires personalization of the interaction to each student.Continuous and efficient assessment of the student's skills are a prerequisite for such personalization.We developed a Bayesian active-learning algorithm that continuously and efficiently assesses a child's word-reading skills and implemented it in a social robot.We then developed an integrated experimental paradigm in which a child plays a novel story-creation tablet game with the robot.The robot is portrayed as a younger peer who wishes to learn to read, framing the assessment of the child's word-reading skills as well as empowering the child.We show that our algorithm results in an accurate representation of the child's word-reading skills for a large age range, 4-8 year old children, and large initial reading skill range.We also show that employing child-specific assessment-based tutoring results in an age- and initial reading skill-independent learning, compared to random tutoring.Finally, our integrated system enables us to show that implementing the same learning algorithm on the robot's reading skills results in knowledge that is comparable to what the child thinks the robot has learned.The child's perception of the robot's knowledge is age-dependent and may facilitate an indirect assessment of the development of theory-of-mind.

This half-day IDC 2015 workshop focuses on children's curiosity and how novel digital technologies can help assess and promote it. Our goal is to explore the design, development, use and evaluation of new technologies for this purpose, in terms of: i) challenges in assessing children's curiosity; ii) different evaluation methodologies; iii) design approaches to promotion of curiosity; iv) cognitive and social aspects of curiosity and their interaction with these technologies.

Curiosity is key to learning, yet school children show wide variability in their eagerness to acquire information. Recent research suggests that other people have a strong influence on children's exploratory behavior. Would a curious robot elicit children's exploration and the desire to find out new things? In order to answer this question we designed a novel experimental paradigm in which a child plays an education tablet app with an autonomous social robot, which is portrayed as a younger peer. We manipulated the robot's behavior to be either curiosity-driven or not and measured the child's curiosity after the interaction. We show that some of the child's curiosity measures are significantly higher after interacting with a curious robot, compared to a non-curious one, while others do not. These results suggest that interacting with an autonomous social curious robot can selectively guide and promote children's curiosity.

A curious agent, be it a robot, animal or human, acts so as to learn as much as possible about itself and its environment. Such an agent can also learn without external supervision, but rather actively probe its surrounding and autonomously induce the relations between its action's effects on the environment and the resulting sensory input. We present a model of hierarchical motor-sensory loops for such an autonomous active learning agent, meaning a model that selects the appropriate action in order to optimize the agent's learning. Furthermore, learning one motor-sensory mapping enables the learning of other mappings, thus increasing the extent and diversity of knowledge and skills, usually in hierarchical manner. Each such loop attempts to optimally learn a specific correlation between the agent's available internal information, e.g. sensory signals and motor efference copies, by finding the action that optimizes that learning. We demonstrate this architecture on the well-studied vibrissae system, and show how sensory-motor loops are actively learnt from the bottom-up, starting with the forward and inverse models of whisker motion and then extending them to object localization. The model predicts transition from free-air whisking that optimally learns the self-generated motor-sensory mapping to touch-induced palpation that optimizes object localization, both observed in naturally behaving rats.