Artificial intelligence is revolutionizing formal education, fueled by innovations in learning assessment, content generation, and instructional delivery. Informal, lifelong learning settings have been the subject of less attention. We provide a proof-of-concept for an embodied book discussion companion, designed to stimulate conversations with readers about particularly creative metaphors in fiction literature. We collect ratings from 26 participants, each of whom discuss Jane Austen's "Pride and Prejudice" with the robot across one or more sessions, and find that participants rate their interactions highly. This suggests that companion robots could be an interesting entryway for the promotion of lifelong learning and cognitive exercise in future applications.
Educational scenarios to which social robots have been deployed have been primarily formal settings with child learners @cite_9 @cite_11 @cite_8 @cite_6 @cite_24 @cite_13 @cite_27 . Our focus is on a different setting: informal, conversational lifelong learning. is the process of acquiring knowledge and or exercising cognitive faculties across the human lifespan, outside of traditional academic contexts. Research involving social robots in lifelong learning scenarios has been scarce, with most work deploying social robots to adult populations focusing on psychological or physical healthcare needs instead @cite_19 @cite_18 @cite_10 @cite_28 . However, @cite_2 designed a human-robot music guessing game to stimulate cognition in older adults suffering from dementia, and @cite_26 created a social robot to scaffold motivation in adult second language learners. @cite_21 also explored second language learning in adults, although their robot's behaviors were originally designed with children in mind. A common theme across these systems is the absence of open-ended conversation: all cases utilize buttons and multiple-choice answers as their input. The inability to converse naturally limits a robot's potential to engage in cognitively meaningful interactions, particularly when dealing with more subjective topics like literature or metaphor interpretation.
@cite_18 @cite_26 @cite_8 @cite_28 @cite_10 @cite_9 @cite_21 @cite_6 @cite_24 @cite_19 @cite_27 @cite_2 @cite_13 @cite_11
2140527237 2809451070 2192319054 2793990128 2761596890 2809463637 2592042377 2001862949 2809232049 2898938475 2110437072 2096153636 2291350754 2204977270
Human-robot interaction (HRI) is now well enough understood to allow us to build useful systems that can function outside of the laboratory. We are studying long-term interaction in natural user environments and describe the implementation of a robot designed to help individuals effect behavior change while dieting. Our robotic weight loss coach is compared to a standalone computer and a paper log in a controlled study. We describe the software model used to create successful long-term HRI. We summarize the experimental design, analysis, and results of our study, the first where a sociable robot interacts with a user to achieve behavior change. Results show that participants track their calorie consumption and exercise for nearly twice as long when using the robot than with the other methods and develop a closer relationship with the robot. Both are indicators of longer-term success at weight loss and maintenance and show the effectiveness of sociable robots for long-term HRI. 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. In this paper we present the results of a qualitative study with therapists to inform social robotics and human robot interaction (HRI) for engagement in rehabilitative therapies. Our results add to growing evidence that socially assistive robots (SARs) could play a role in addressing patients' low engagement with self-directed exercise programmes. Specifically, we propose how SARs might augment or offer more pro-active assistance over existing technologies such as smartphone applications, computer software and fitness trackers also designed to tackle this issue. In addition, we present a series of design implications for such SARs based on therapists' expert knowledge and best practices extracted from our results. This includes an initial set of SAR requirements and key considerations concerning personalised and adaptive interaction strategies. Lack of companionship, loneliness, and social isolation are often experienced by older adults diagnosed with clinical depression living independently within the community. Socially assistive robots (SARs), a relatively new concept within recreational therapy, may be one treatment modality that can address each one of these concerns. This exploratory study consisted of interviews with community mental health professionals, including a recreational therapist, to determine if they perceived SARs as an appropriate interdisciplinary clinical intervention for older adults diagnosed with clinical depression. Results indicated that SARs, especially those which can provide companionship and social interaction similar to animal assisted therapy, are an appropriate interdisciplinary intervention for this population and may have an impact on improving overall quality of life by decreasing loneliness and social isolation associated with clinical depression. LEGO Mindstorms robots are a popular educational tool for teaching programming concepts to young learners. However, learners working with these robots often lack sufficient feedback on their programs, which makes it difficult for them to reflect on domain concepts and may decrease their motivation. We see an opportunity to introduce feedback into LEGO Mindstorms programming environments by having the robot itself deliver feedback, leveraging research on learning companions to transform the programmable robot into a social actor. Our robot, ROBIN, provides learners with automated reflection prompts based on a domain model and the student’s current program, along with social encouragement based on a theory of instructional immediacy. We hypothesize that by having the robot itself provide cognitive and social feedback, students will both reflect more on their misconceptions and persist more with the activity. This paper describes the design and implementation of ROBIN and discusses how this approach can benefit students. In this paper, we present an approach to adaptive language tutoring in child-robot interaction. The approach is based on a dynamic probabilistic model that represents the interrelations between the learner's skills, her observed behaviour in tutoring interaction, and the tutoring action taken by the system. Being implemented in a robot language tutor, the model enables the robot tutor to trace the learner's knowledge and to decide which skill to teach next and how to address it in a game-like tutoring interaction. Results of an evaluation study are discussed demonstrating how participants in the adaptive tutoring condition successfully learned foreign language words. CCS Concepts. Computing methodologies → Probabilistic reasoning; Cognitive robotics;. Applied computing → Interactive learning environments;. Human-centered computing → Empirical studies in HCI; This article presents a novel robotic partner which children can teach handwriting. The system relies on the learning by teaching paradigm to build an interaction, so as to stimulate meta-cognition, empathy and increased self-esteem in the child user. We hypothesise that use of a humanoid robot in such a system could not just engage an unmotivated student, but could also present the opportunity for children to experience physically-induced benefits encountered during human-led handwriting interventions, such as motor mimicry. By leveraging simulated handwriting on a synchronised tablet display, a NAO humanoid robot with limited fine motor capabilities has been configured as a suitably embodied handwriting partner. Statistical shape models derived from principal component analysis of a dataset of adult-written letter trajectories allow the robot to draw purposefully deformed letters. By incorporating feedback from user demonstrations, the system is then able to learn the optimal parameters for the appropriate shape models. Preliminary in situ studies have been conducted with primary school classes to obtain insight into children's use of the novel system. Children aged 6-8 successfully engaged with the robot and improved its writing to a level which they were satisfied with. The validation of the interaction represents a significant step towards an innovative use for robotics which addresses a widespread and socially meaningful challenge in education. Driven by the latest technologies in artificial intelligence (e.g., natural language processing and emotion recognition), we design a novel robot system, called smart learning partner, to provide a more pleasurable learning experience and better motivate learners. The self-determination theory is used as the guideline to design its human-robot interaction. The large-scale deployment of SLP in local schools and families would bring both research and commercial opportunities. The ability to engage the user in a conversation and the credibility of the system are two fundamental characteristics of virtual coaches. In this paper, we present the architecture of a conversational e-coach for promoting healthy lifestyles in older age, developed in the context of the NESTORE H2020 EU project. The proposed system allows multiple access points to a conversational agent via different interaction modalities: a tangible companion that embodies the virtual coach will leverage voice and other non-verbal cues in the domestic environment, while a mobile app will integrate a text-based chat for a ubiquitous intervention. In both cases, the conversational agent will deliver personalized interventions based on behavior change models and will promote trust by means of emotionally rich conversations. In contrast to conventional teaching agents (including robots) that were designed to play the role of human teachers or caregivers, we propose the opposite scenario in which robots receive instruction or care from children. We hypothesize that by using this care-receiving robot, we may construct a new educational framework whose goal is to promote children's spontaneous learning by teaching through their teaching the robot. In this paper, we describe the introduction of a care-receiving robot into a classroom at an English language school for Japanese children (3--6 years of age) and then conduct an experiment to evaluate if the care-receiving robot can promote their learning using English verbs. The results suggest that the idea of a care-receiving robot is feasible and that the robot can help children learn new English verbs efficiently. In addition, we report on investigations into several forms of teaching performed by children, which were revealed through observations of the children, parent interviews, and other useful knowledge. These can be used to improve the design of care-receiving robots for educational purposes. Currently the 2 percent growth rate for the world's older population exceeds the 1.2 rate for the world's population as a whole. By 2050, the number of individuals over the age 85 is projected to be three times more than there is today. Most of these individuals will need physical, emotional, and cognitive assistance. In this paper, we present a new adaptive robotic system based on the socially assistive robotics (SAR) technology that tries to provide a customized help protocol through motivation, encouragements, and companionship to users suffering from cognitive changes related to aging and or Alzheimer's disease. Our results show that this approach can engage the patients and keep them interested in interacting with the robot, which, in turn, increases their positive behavior. To date, the majority of learning technologies only afford virtual interactions on desktops or tablets, despite evidence that students learn through physical manipulation of their environment. We implemented a tangible system that allows students to solve coordinate geometry problems by interacting in a physical space with digitally augmented devices, using a teachable agent framing. We describe our system and the results from a pilot involving students using our system to teach a virtual agent. Students used a variety of strategies to solve problems that included embodied behaviors, and the majority did feel they were teaching their agent. We discuss the implications of our findings with respect to the design of adaptive tangible teachable systems. Building on existing work on artificial tutors with human-like capabilities, we describe the EMOTE project approach to harnessing benefits of an artificial embodied tutor in a shared physical space. Embodied in robotic platforms or through virtual agents, EMOTE aims to capture some of the empathic and human elements characterising a traditional teacher. As such, empathy and engagement, abilities key to influencing student learning, are at the core of the EMOTE approach. We present non-verbal and adaptive dialogue challenges for such embodied tutors as a foundation for researchers investigating the potential for empathic tutors that will be accepted by students and teachers.