Invasive medical procedures can cause significant anxiety in children, affecting both immediate and long-term healthcare experiences. While social robots have shown promise in reducing anxiety, most interventions rely on short-term distraction. This paper introduces the Dream Robot, a social robot designed to deliver a medical hypnosis intervention to reduce anxiety in pediatric hospitals. We present three design phases of a rigorous interdisciplinary participatory process involving children, healthcare professionals, and roboticists. Through in-situ pilots during blood draws, anesthesia induction, and feeding tube placement, we examined how children, parents, and clinicians engaged with robot-guided medical hypnosis. Our findings indicate that such interventions can support children’s coping, but only when aligned with developmental differences, procedural timing, and social dynamics between child, parent, and healthcare professionals. Parents frequently acted as co-regulators, guiding children’s engagement. These results position robot-guided medical hypnosis as a situated, selective intervention rather than a universal solution for pediatric care.

This study investigates the impact of an educational social storytelling robot on multilingual children aged 4-12 years by telling stories in their native language (Arabic). This work emphasizes the importance of using native languages within schools to stimulate inclusion and learning. Our experiment compared two conditions: one where the NAO robot engaged in interactive storytelling by using gestures and posing questions, and another where it merely told the story. Children enjoyed the sessions and the use of their native language in particular, and there was a trend suggesting that children knew more target words after the storytelling sessions. However, there were no significant differences in learning outcomes, engagement, enjoyment, or story comprehension between the interactive and non-interactive conditions. Together, these findings suggest a promising role for using educational robots in storytelling to support multilingual children.

Healthcare practitioners often use distraction techniques to ease children’s anxiety during blood draws. This study explored the effectiveness of a social robot, Softbank Robotics NAO, in reducing anxiety and pain for children aged 4-12 during blood draws. The robot engaged with children using behaviors tailored to the children’s age. Interestingly, results indicated the robot significantly increased anxiety, but pain levels remained unchanged. We argue that this effect may result from the robot’s novelty and that, over the long term, the robot could have a positive impact on anxiety, consistent with other studies. This research underscores the potential of social robots to alleviate children’s anxiety in medical procedures, enhancing patient experience in pediatric healthcare.

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.

Social robots have been shown effective in pedagogical settings due to their embodiment and social behavior that can improve a learner’s motivation and engagement. In this study, the impact of a social robot’s motivational gestures in robot-assisted language learning (RALL) was investigated. Twenty-five university students participated in a language learning task tutored by a NAO robot under two conditions (within-subjects design); in one condition the robot provided positive and negative feedback on participant’s performance using both verbal and non-verbal behavior (Gesture condition), in another condition the robot only employed verbal feedback (No-Gesture condition). To assess cognitive engagement and learning in each condition, we collected EEG brain activity from the participants during the interaction and evaluated their word knowledge during an immediate and delayed post-test. No significant difference was found with respect to cognitive engagement as quantified by the EEG Engagement Index during the practice phase. Similarly, the word test results indicated an overall high performance in both conditions, suggesting similar learning gain regardless of the robot’s gestures. These findings do not provide evidence in favor of robot’s motivational gestures during language learning tasks but at the same time indicate challenges with respect to the design of effective social behavior for pedagogical robots.

Abstract 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.

The Robots for Learning workshop series aims at advancing the research topics related to the use of social robots in educational contexts. This year's half-day workshop follows on previous events in Human-Robot Interaction conferences focusing on efforts to design, develop and test new robotics systems that help learners. This 5th edition of the workshop will be dealing in particular on the potential use of robots for adaptive learning. Since the past few years, inclusive education have been a key policy in a number of countries, aiming to provide equal changes and common ground to all. In this workshop, we aim to discuss strategies to design robotics system able to adapt to the learners' abilities, to provide assistance and to demonstrate long-term learning effects.

This video illustrates the large-scale experiment of the L2TOR project that will be presented at the HRI 2019 conference. The experiment aimed to investigate how 192 Dutch 5-year-old children could learn 34 English words from a NAO robot in 7 lessons. The experiment compared 4 conditions: 1) robot using iconic gestures, 2) robot without iconic gestures, 3) tablet only, and 4) a control group. The results revealed that children could learn more English words in all experimental conditions compared to the control group. The three experimental conditions did not show any significant differences regarding the learning outcomes.

We present a large-scale study of a series of seven lessons designed to help young children learn English vocabulary as a foreign language using a social robot. The experiment was designed to investigate 1) the effectiveness of a social robot teaching children new words over the course of multiple interactions (supported by a tablet), 2) the added benefit of a robot's iconic gestures on word learning and retention, and 3) the effect of learning from a robot tutor accompanied by a tablet versus learning from a tablet application alone. For reasons of transparency, the research questions, hypotheses and methods were preregistered. With a sample size of 194 children, our study was statistically well-powered. Our findings demonstrate that children are able to acquire and retain English vocabulary words taught by a robot tutor to a similar extent as when they are taught by a tablet application. In addition, we found no beneficial effect of a robot's iconic gestures on learning gains.

This work documents a playful human-robot interaction, in the form of a game of charades, through which a humanoid robot is able to learn how to produce and recognize gestures by interacting with human participants. We describe an extensive dataset of gesture recordings, which can be used for future research into gestures, specifically for human-robot interaction applications.

This paper presents a study in which children, four to six years old, were taught words in a second language by a robot tutor. The goal is to evaluate two ways for a robot to provide scaffolding for students: the use of iconic gestures, combined with adaptively choosing the next learning task based on the child»s past performance. The results show a positive effect on long-term memorization of novel words, and an overall higher level of engagement during the learning activities when gestures are used. The adaptive tutoring strategy reduces the extent to which the level of engagement is diminishing during the later part of the interaction.

In search for better technological solutions for education, we adapted a principle from economic game theory, namely that giving a help will promote collaboration and eventually long-term relations between a robot and a child. This principle has been shown to be effective in games between humans and between humans and computer agents. We compared the social and cognitive engagement of children when playing checkers game combined with a social strategy against a robot or against a computer. We found that by combining the social and game strategy the children (average age of 8.3 years) had more empathy and social engagement with the robot since the children did not want to necessarily win against it. This finding is promising for using social strategies for the creation of long-term relations between robots and children and making educational tasks more engaging. An additional outcome of the study was the significant difference in the perception of the children about the difficulty of the game – the game with the robot was seen as more challenging and the robot – as a smarter opponent. This finding might be due to the higher perceived or expected intelligence from the robot, or because of the higher complexity of seeing patterns in three-dimensional world.