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This research explored if a social robot would play a role in facilitating the development of friendship between young children while they engage in playful learning. Grounded in child–robot interaction and child development literature, we instantiated four sessions of triadic interaction activities among two children and a robot, where the robot mediated the children's collaborative interactions. There were two types of robot-mediated activities (Conversational and Tablet assisted) with each type having two sessions and each session taking approx. 20 minutes, depending on the children. The activities were deployed with ten children (aged five to six) in an after-school program in a rural public school in the U.S. twice a week for two weeks. We video-recorded the sessions and later annotated these recordings for analysis. The friendship development between the children in a pair was observed in terms of five behavioral categories (liking, togetherness, parity, agreement, and co-construction). The results showed that both conversational and tablet-assisted robot mediation contributed complementarily to friendship development among the children. [ABSTRACT FROM AUTHOR]

Child-robot interaction (CRI) has been mostly studied in labs and classroom settings. In this work, we share a CRI language processing study carried out in children's homes, analyzing how each language processing layer performs with children at home with no researcher present. We carried out an experiment in two phases: a first iteration with 7 families in Spain cohabiting with a simulated robot for 2 weeks and a second iteration with 31 families: 11 in Spain, 9 in Ukraine and 11 in the USA completing 4 cohabitation weeks at each country. The total number of children involved in the experiments was 55, ranging from 5 to 13 years old. Our goal in this study is to evaluate the performance of voice recognition, language understanding and dialogue management when children interact with a robot at home. Our results indicate that dialogue management capabilities are becoming the key element in the language processing pipeline; they also denote that the dialogue engine should include mixed-initiative capabilities and show the relative usage of different common built-in intents. [ABSTRACT FROM AUTHOR]

The presence of social robots in children's daily environments has steadily increased. With the advancement of artificial intelligence (AI), social robots have influenced children's learning and development. This study innovatively utilized the Web of Science database and conducted a bibliometric analysis of 517 publications on social robots supporting children's learning and development before September 2022. Unlike most existing reviews, this study employed a synergistic combination of two complementary visualization tools, VOSviewer and CiteSpace, to map the intellectual structure and analyze the knowledge evolution path in this emerging interdisciplinary field. Specifically, VOSviewer generated visualizations depicting collaboration networks, research hotspots, and trends based on co-occurrences. CiteSpace enabled quantitative measurements of node centrality and burstness to reveal pivotal entities and emerging topics. Combining visual mapping and quantitative analysis by VOSviewer and CiteSpace allowed comprehensive landscape mapping for an in-depth investigation into the development of this field. This study proposes future research directions, including children's perceptions of social robots, social robots enhancing children's learning, social robots supporting children's social and emotional development, and social robots for children with special needs. The findings also inform the design and application of child-friendly social robots equipped with generative AI techniques. [ABSTRACT FROM AUTHOR]

Social robots, owing to their embodied physical presence in human spaces and the ability to directly interact with the users and their environment, have a great potential to support children in various activities in education, healthcare and daily life. Child–Robot Interaction (CRI), as any domain involving children, inevitably faces the major challenge of designing generalized strategies to work with unique, turbulent and very diverse individuals. Addressing this challenging endeavor requires to combine the standpoint of the robot-centered perspective, i.e. what robots technically can and are best positioned to do, with that of the child-centered perspective, i.e. what children may gain from the robot and how the robot should act to best support them in reaching the goals of the interaction. This article aims to help researchers bridge the two perspectives and proposes to address the development of CRI scenarios with insights from child psychology and child development theories. To that end, we review the outcomes of the CRI studies, outline common trends and challenges, and identify two key factors from child psychology that impact child-robot interactions, especially in a long-term perspective: developmental stage and individual characteristics. For both of them we discuss prospective experiment designs which support building naturally engaging and sustainable interactions. [ABSTRACT FROM AUTHOR]

Research on children's anthropomorphism of social robots is mostly cross-sectional and based on a single measurement. However, because social robots are new type of technology with which children have little experience, children's initial responses to social robots may be biased by a novelty effect. Accordingly, a single measurement of anthropomorphism may not accurately reflect how children anthropomorphize social robots over time. Thus, we used data from a six-wave panel study to investigate longitudinal changes in 8- to 9-year-old children's anthropomorphism of a social robot. Latent class growth analyses revealed that anthropomorphism peaked after the first interaction with the social robot, remained stable for a brief period of time, and then decreased. Moreover, two distinct longitudinal trajectories of anthropomorphism could be identified: one with moderate to high anthropomorphism and one with low to moderate anthropomorphism. Previous media exposure to non-fictional robots increased the probability that children experienced higher levels of anthropomorphism. [ABSTRACT FROM AUTHOR]

This paper contributes to the understanding of child-robot interaction through the investigation of child interactions with and anthropomorphization of humanoid robots when manipulating robot-related variables such as behavior and gender. In this study, children observe a robot demonstration in a classroom setting, during which the robot showcases either assertive or submissive behavior and is attributed a gender, either robot-female or robot-male. Afterwards, participant anthropomorphization is measured using the Attributed Mental States Questionnaire (AMS-Q). Results suggest that when prompted to select a response directed at the robot, children used significantly more commanding phrases when addressing the assertively behaving robot when compared to the submissively behaving robot. Further, younger children ages 7–9 anthropomorphize robots at a higher degree than older children 10–12 and assertive behavior from the robot lead to higher rates of anthropomorphization. Results also suggest that children are more likely to respond to female robots in an imperative way than male robots. This widened understanding of child perception of and interaction with humanoid robots can contribute to the design of acceptable robot interaction patterns in various settings. [ABSTRACT FROM AUTHOR]

Little is known about children's long-term acceptance of social robots; whether different types of users exist; and what reasons children have not to use a robot. Moreover, the literature is inconclusive about how the measurement of children's robot acceptance (i.e., self-report or observational) affects the findings. We relied on both self-report and observational data from a six-wave panel study among 321 children aged eight to nine, who were given a Cozmo robot to play with at home over the course of 8 weeks. Children's robot acceptance decreased over time, with the strongest drop after 2–4 weeks. Children rarely rejected the robot (i.e., they did not stop using it already prior to actual adoption). They rather discontinued its use after initial adoption or alternated between using and not using the robot. The competition of other toys and lacking motivation to play with Cozmo emerged as the strongest reasons for not using the robot. Self-report measures captured patterns of robot acceptance well but seemed suboptimal for precise assessments of robot use. [ABSTRACT FROM AUTHOR]

For a period of 3 weeks in June 2021, we embedded a social robot (Softbank Pepper) in a Special Educational Needs (SEN) school for autistic children. The robot's behaviours and integration into the school were co-designed with the children and teachers, with a focus on improving the well-being of the pupils. Using a mix-method approach, we studied the robot's adoption over the course of the study, and the impact of the robot's presence on the children well-being and the school ecosystem. We found that the robot successfully integrated within the school; it fostered and maintained a steady level of interactions (330 interactions, 16 h of continuous use over 3 weeks) with a small yet meaningful group of children with a positive impact on their well-being; and it led to a nuanced conversation with the students and school staff about the role and impact of such a social technology in a SEN school. [ABSTRACT FROM AUTHOR]

According to the World Health Organization, the early identification of mental wellbeing issues in children is extremely important for children's growth and development. However, the available health services are not sufficient to address children's needs in this area. Literature suggests that robots can provide the support needed to promote mental wellbeing in children, but how robots can help with the assessment of mental wellbeing is relatively unexplored. Hence, this work analyses multiple data modalities collected in an exploratory study involving 41 children (8–13 years old, 21 females and 20 males) who interacted with a Nao robot for about 30–45 min. During this session, the robot delivered four tasks: (1) happy and sad memory recall, (2) the Short Moods and Feelings Questionnaire (SMFQ), (3) the picture-based task inspired by the Children Appreciation Test (CAT), and (4) the Revised Children Anxiety and Depression Scale (RCADS). We clustered the participants into three groups based on their SMFQ scores as follows: low tertile (16 participants), med tertile (12 participants), and high tertile (13 participants). Then, we described and analysed the data collected from multiple sources (i.e., questionnaires responses, audio-visual recordings, and speech transcriptions) to gather multiple perspectives for understanding how children's responses and behaviours differ across the three clusters (low, med, vs high) and their gender (boys vs girls) for robot-assisted assessment of mental wellbeing. Our results show that: (i) the robotised mode is the most effective in the identification of wellbeing-related concerns with respect to standardised modes of administration (self-report and parent-report); (ii) children less likely to have mental wellbeing concerns displayed more expressive responses than children who are more likely to have mental wellbeing concerns; and (iii) girls who are more likely to have mental wellbeing concerns displayed more expressive responses than boys, while boys who are less likely to have mental wellbeing concerns displayed more expressive responses than girls. Findings from this work are promising for paving the way towards automatic assessment of mental wellbeing in children via robot-assisted interactions. [ABSTRACT FROM AUTHOR]

Behavioral analyses of in-the-wild HRI studies generally rely on interviews or visual information from videos. This can be very limiting in settings where video recordings are not allowed or limited. We designed and tested a vocalization-based protocol to analyze in-the-wild child–robot interactions based upon a behavioral coding scheme utilized in wildlife biology, specifically in studies of wild dolphin populations. The audio of a video or audio recording is converted into a transcript, which is then analyzed using a behavioral coding protocol consisting of 5–6 categories (one indicating non-robot-related behavior, and 4–5 categories of robot-related behavior). Refining the code categories and training coders resulted in increased agreement between coders, but only to a level of moderate reliability, leading to our recommendation that it be used with three coders to assess where there is majority consensus, and thereby correct for subjectivity. We discuss lessons learned in the design and implementation of this protocol and the potential for future child–robot experiments analyzed through vocalization behavior. We also perform a few observational behavior analyses from vocalizations alone to demonstrate the potential of this field. [ABSTRACT FROM AUTHOR]

The home robot-based child activity service aims to cultivate children’s social emotions. A design theme was produced by interviewing child development experts and parents. The activity service is composed of 50 plays and 70 conversations. These were developed based on activities from psychomotor therapy and the guidelines of Ministry of Early Childhood Education in South Korea. In the field test, 50 children aged five–seven years participated to experience the activity services at home for 4 days. After completing the 4 days of field testing, we conducted customer satisfaction (CSAT) surveys, Godspeed evaluations and interviews to quantitatively and qualitatively verify the evaluations by the children and parents. As a result, 92% of the children and 80% of the parents evaluated that they were satisfied with the service. In addition, our results revealed that the social robot-based service contributed to improving the relationship between children and families by functioning as a messenger. Finally, the lessons learned from the service development and field tests were discussed to aid service designers and robotics engineers.

Children worldwide are becoming increasingly inactive, leading to significant wellness challenges. Initial findings from our research team indicate that robots could potentially provide a more effective approach (compared to other age-appropriate toys) for encouraging physical activity in children. However, the basis of this past work relied on either interactions with groups of children (making it challenging to isolate specific factors that influenced activity levels) or a preliminary version of results of the present study (which centered on just a single more exploratory method for assessing child movement). This paper delves into more controlled interactions involving a single robot and a child participant, while also considering observations over an extended period to mitigate the influence of novelty on the study outcomes. We discuss the outcomes of a two-month-long deployment, during which $N=8$ participants engaged with our custom robot, GoBot, in weekly sessions. During each session, the children experienced three different conditions: a teleoperated robot mode, a semi-autonomous robot mode, and a control condition in which the robot was present but inactive. Compared to our past related work, the results expanded our findings by confirming with greater clout (based on multiple data streams, including one more robust measure compared to the past related work) that children tended to be more physically active when the robot was active, and interestingly, there were no significant differences between the teleoperated and semi-autonomous modes in terms of our study measures. These insights can inform future applications of assistive robots in child motor interventions, including the guiding of appropriate levels of autonomy for these systems. This study demonstrates that incorporating robotic systems into play environments can boost physical activity in young children, indicating potential implementation in settings crafted to enhance children’s physical movement.

While interacting with a social robot, children have a need to express themselves and have their expressions acknowledged by the robot—a need that is often unaddressed by the robot, due to its limitations in understanding the expressions of children. To keep the child-robot interaction manageable, the robot takes control, undermining children's ability to co-regulate the interaction. Co-regulation is important for having a fulfilling social interaction. We developed a co-creation activity that aims to facilitate more co-regulation. Children are enabled to create sound effects, gestures, and light animations for the robot to use during their conversation. A crucial additional feature is that children are able to coordinate their involvement of the co-creation process. Results from a user study (n = 59 school children, 7–11 years old) showed that the co-creation activity successfully facilitated co-regulation by improving children's agency. It also positively affected the acceptance of the robot. We furthermore identified five distinct profiles detailing the different needs and motivations children have for the level of involvement they chose during the co-creation process. [ABSTRACT FROM AUTHOR]

Recent advancements in socially assistive robotics (SAR) have shown a significant potential of using social robotics to achieve increasing cognitive and affective outcomes in education. However, the deployments of SAR technologies also bring ethical challenges in tandem, to the fore, especially in under-resourced contexts. While previous research has highlighted various ethical challenges that arise in SAR deployment in real-world settings, most of the research has been centered in resource-rich contexts, mainly in developed countries in the 'Global North,' and the work specifically in the educational setting is limited. This research aims to evaluate and reflect upon the potential ethical and pedagogical challenges of deploying a social robot in an under-resourced context. We base our findings on a 5-week in-the-wild user study conducted with 12 kindergarten students at an under-resourced community school in New Delhi, India. We used interaction analysis with the context of learning, education, and ethics to analyze the user study through video recordings. Our findings highlighted four primary ethical considerations that should be taken into account while deploying social robotics technologies in educational settings; (1) language and accent as barriers in pedagogy, (2) effect of malfunctioning, (un)intended harms, (3) trust and deception, and (4) ecological viability of innovation. Overall, our paper argues for assessing the ethical and pedagogical constraints and bridging the gap between non-existent literature from such a context to evaluate better the potential use of such technologies in under-resourced contexts. [ABSTRACT FROM AUTHOR]

Childhood obesity is a major public health challenge which is linked with the occurrence of diseases such as diabetes and cancer. The COVID-19 pandemic has forced changes to the lifestyle behaviors of children, thereby making the risk of developing obesity even greater. Novel preventive tools and approaches are required to fight childhood obesity. We present a social robot-based platform which utilizes an interactive motivational strategy in communication with children, collects self-reports through the touch of tangible objects, and processes behavioral data, aiming to: (a) screen and assess the behaviors of children in the dimensions of physical activity, diet, and education, and (b) recommend individualized goals for health behavior change. The platform was integrated through a microservice architecture within a multi-component system targeting childhood obesity prevention. The platform was evaluated in an experimental study with 30 children aged 9–12 years in a real-life school setting, showing children's acceptance to use it, and an 80% success rate in achieving weekly personal health goals recommended by the social robot-based platform. The results provide preliminary evidence on the implementation feasibility and potential of the social robot-based platform toward the betterment of children's health behaviors in the context of childhood obesity prevention. Further rigorous longer-term studies are required. [ABSTRACT FROM AUTHOR]

Children develop intuitions about fairness relatively early in development. While we know that children believe other humans care about distributional fairness, considerably less is known about whether they believe other agents, such as robots, do as well. In two experiments (N = 273) we investigated 4- to 9-year-old children's intuitions about whether robots would be upset about unfair treatment as human children. Children were told about a scenario in which resources were being split between a human child and a target recipient: either another child or a robot across two conditions. The target recipient (either child or robot) received less than another child. They were then asked to evaluate how fair the distribution was, and whether the target recipient would be upset. Both Experiment 1 and 2 used the same design, but Experiment 2 also included a video demonstrating the robot's mechanistic "robotic" movements. Our results show that children thought it was more fair to share unequally when the disadvantaged recipient was a robot rather than a child (Experiment 1 and 2). Furthermore, children thought that the child would be more upset than the robot (Experiment 2). Finally, we found that this tendency to treat these two conditions differently became stronger with age (Experiment 2). These results suggest that young children treat robots and children similarly in resource allocation tasks, but increasingly differentiate them with age. Specifically, children evaluate inequality as less unfair when the target recipient is a robot, and think that robots will be less angry about inequality. [ABSTRACT FROM AUTHOR]

This retrospective study presents and summarizes our long-term efforts in the popularization of robotics, engineering, and artificial intelligence (STEM) using the NAO humanoid robot. By a conservative estimate, over a span of 8 years, we engaged at least a couple of thousand participants: approximately 70% were preschool children, 15% were elementary school students, and 15% were teenagers and adults. We describe several robot applications that were developed specifically for this task and assess their qualitative performance outside a controlled research setting, catering to various demographics, including those with special needs (ASD, ADHD). Five groups of applications are presented: (1) motor development activities and games, (2) children's games, (3) theatrical performances, (4) artificial intelligence applications, and (5) data harvesting applications. Different cases of human–robot interactions are considered and evaluated according to our experience, and we discuss their weak points and potential improvements. We examine the response of the audience when confronted with a humanoid robot featuring intelligent behavior, such as conversational intelligence and emotion recognition. We consider the importance of the robot's physical appearance, the emotional dynamics of human–robot engagement across age groups, the relevance of non-verbal cues, and analyze drawings crafted by preschool children both before and after their interaction with the NAO robot. [ABSTRACT FROM AUTHOR]

Commensurate with constant technological advances, social robots are increasingly anticipated to enter homes and classrooms; however, little is known about the efficacy of social robots as teaching tools. To investigate children's learning from robots, 1- to 3-year-olds observed either a human or a robot demonstrate two goal-directed object manipulation tasks and were then given the opportunity to act on the objects. Children exhibited less imitation from robotic models that varied with task complexity and age, a phenomenon we term the "robot deficit." In addition, the more children engaged with the robot prior to administration of the imitation task, the more likely they were to replicate the robot's actions. These findings document how children are able to learn from robots but that ongoing design of robotic platforms needs to be oriented to developing more socially engaging means of interacting.

More robots interact remotely with people and teleoperated robots are also being used in childcare. Effective interaction cannot be achieved unless knowing how to present the touch interaction that children perform on a daily basis for the operator. Toward future goal of this research that implementation of the technology that communicates social touch information on telepresence childcare robots, this paper reports investigation how to present touched information to an operator by evaluating brain activity. As a result, texts describing the touch situation was the most acceptable presentation method with the least brain load on the operator. [ABSTRACT FROM AUTHOR]

Research suggests children readily treat robots as social actorsand sources of information for learning. Here we ask if childrenuse depictions of gender-counterstereotypic robots (e.g., afemale construction worker robot) and gender-stereotypicrobots (e.g., a female secretary robot) as sources of informationabout cultural gender stereotypes. Forty-five 6- to 8-year-oldchildren participated in a short counterstereotyping task.Children in the counterstereotypical condition viewed videosof cartoon female gendered robots with culturally stereotypedmasculine occupations, interests in activities, and traits.Children in the stereotypical condition viewed videos ofcartoon female gendered robots with culturally stereotypedfeminine attributes. Children completed a measure of genderstereotyping before and after viewing the intervention videos.From pretest to posttest, children’s gender stereotypingdecreased in the counterstereotypical condition and increasedin the stereotypical condition. These finding suggest childrenmay learn from robots as models of cultural gender stereotypes.

Personalized learning has a higher impact on students' progress than traditional approaches. However, current resources required to implement personalization are scarce. This research aims to conceptualize and develop an autonomous robot tutor with personalization policy for preschool children aged between three to five years old. Personalization is performed by automatically adjusting the difficulty level of the lesson delivery and assessment, as well as adjusting the feedback based on the reaction of children. This study explores three child behaviors for the personalization policy: (i) academic knowledge (measured by the correctness of the answer), (ii) executive functioning of attention (measured by the orientation and the gaze direction of child's body), and (iii) working memory or hesitation (measured by the time lag before the answer). Moreover, this study designed lesson content through interviews with teachers and deployed the personalization interaction policy through the NAO robot with five children in a case user study method. We qualitatively analyze the session observations and parent interviews, as well as quantitatively analyze knowledge gain through pre- and posttests and a parent questionnaire. The findings of the study reveal that the personalized interaction with the robot showed a positive potential in increasing the children's learning gains and attracting their engagement. As general guidelines based on this pilot study, we identified additional personalization strategies that could be used for autonomous personalization policies based on each child's behavior, which could have a considerable impact on child learning. [ABSTRACT FROM AUTHOR]

The challenge of long-term interaction between humans and robots is still a bottleneck in service robot research. To gain an understanding of sustained relatedness with robots, this study proposes a conceptual framework for bond formation. More specifically, it addresses the dynamics of children bonding with robotic pets as the basis for certain services in healthcare and education. The framework presented herein offers an integrative approach and draws from theoretical models and empirical research in Human Robot Interaction and also from related disciplines that investigate lasting relationships, such as human-animal affiliation and attachment to everyday objects. The research question is how children's relatedness to personified technologies occurs and evolves and what underpinning processes are involved. The subfield of research is child-robot interaction, within the boundaries of social psychology, where the robot is viewed as a social agent, and human-system interaction, where the robot is regarded as an artificial entity. The proposed framework envisions bonding with pet-robots as a socio-affective process towards lasting connectedness and emotional involvement that evolves through three stages: first encounter, short-term interaction and lasting relationship. The stages are characterized by children's behaviors, cognitions and feelings that can be identified, measured and, maybe more importantly, managed. This model aims to integrate fragmentary and heterogeneous knowledge into a new perspective on the impact of robots in close and enduring proximity to children. [ABSTRACT FROM AUTHOR]

The inclusion of technologies such as telepractice, and virtual reality in the field of communication disorders has transformed the approach to providing healthcare. This research article proposes the employment of similar advanced technology – social robots, by providing a context and scenarios for potential implementation of social robots as supplements to stuttering intervention. The use of social robots has shown potential benefits for all the age group in the field of healthcare. However, such robots have not yet been leveraged to aid people with stuttering. We offer eight scenarios involving social robots that can be adapted for stuttering intervention with children and adults. The scenarios in this article were designed by human–robot interaction (HRI) and stuttering researchers and revised according to feedback from speech-language pathologists (SLPs). The scenarios specify extensive details that are amenable to clinical research. A general overview of stuttering, technologies used in stuttering therapy, and social robots in health care is provided as context for treatment scenarios supported by social robots. We propose that existing stuttering interventions can be enhanced by placing state-of-the-art social robots as tools in the hands of practitioners, caregivers, and clinical scientists.

Abstract This study focuses on dialogue breakdowns that can occur in robot-assisted language learning (RALL). Our aim is to analyse how children use gaze to resolve these breakdowns, that is, interruptions in the interaction caused by the robot’s inability to understand the children and react appropriately. Our corpus consists of 18 video filmed L2 learning situations where 36 primary school children talk pairwise with an educational robot for the first time. Our participants are 10–13 years old mono- and bilingual children from Swedish speaking schools in Finland learning L2 English. After detecting the breakdowns from the data, we use a multimodal analysis to identify most typical gaze patterns during these sequences. Our results show that when breakdowns occur, children’s first gaze is directed most frequently towards the robot, but after that, they shift their gaze most often towards the teacher. This result suggests that the children try first to resolve the breakdowns with the robot, but when the problem persists, they also use gaze to seek assistance from other participants present in the learning situation. We interpret this finding to show that children attempt to treat social robots as human-like conversational partners in RALL, but that they turn to other human participants if the robot does not follow the expected interactional behavior. [ABSTRACT FROM AUTHOR]

The proliferation of screen‐free programmable robotics allows teachers to implement age‐appropriate integrated activities that can promote child learning and development. However, very scarce is known about the challenges of using programmable robots as a tool to empower children in minority communities. This mixed‐methods case study examined a Free Quality Kindergarten situated in a low‐income public housing estate in Hong Kong to explore the affordances and challenges of robot programming (RP) in early childhood education (ECE). Both quantitative and qualitative data were generated through videotaped observations, child assessments and teacher interviews to journey with the teaching team as they managed to implement the newly introduced RP curriculum. Child assessment evidence revealed that there was a statistically significant improvement in CT scores over 6 weeks of RP activities, while the pretest–posttest change of child self‐regulation scores was nonsignificant. Video analysis results further revealed that children's interaction level with the robot kit significantly predicted the change of self‐regulation over time. Qualitative results are presented to explicate the micro, meso and macro conditions that constrain the implementation of RP curriculum, including the following: (a) teachers' limited TPACK in RP education, (b) a disconnect between the RP curriculum and the school‐based curriculum, (c) limited resources and (d) the Confucian tradition and cultural barriers. These illustrate the intricacies involved in the implementation of RP education to empower marginalized children in under‐resourced ECE settings. This study will help bring systemic change to the ECE field by uncovering ecological difficulties in integrating new technologies to promote inclusiveness and sustainability for the future. Practitioner notesWhat is already known about this topic Screen‐free robot programming (RP) is beneficial for preschool children in terms of promoting their computational thinking (CT) and executive functioning.RP can be generally engaging for preschool children from a middle‐class background.What this paper adds The study is addressing the inclusiveness and sustainability according to the learning needs of kindergarten students, especially those from less privileged backgrounds.The story‐based approach to RP can cater to the learning needs and interests of children from a lower socio‐economic background, which is likely to enhance their CT skills.Children with a higher level of interaction with the robot kit benefited more from the RP activities in terms of self‐regulation skills.This study used multiple methods (ie, video analysis, teacher interview and child assessment) to reveal the effects of micro, meso and macro contexts on RP education in an under‐resourced Hong Kong kindergarten.Implications for practice and/or policy RP education should be cautiously prompted in diverse early childhood settings, especially the less privileged institutions, to enhance positive effects.Critical constraints on the use of programmable robots to empower marginalized children should be adequately addressed when trying to integrate RP into early childhood education.Teachers should receive training and continual support in RP‐related pedagogical strategies to facilitate and scaffold children's learning. [ABSTRACT FROM AUTHOR]

This retrospective study presents and summarizes our long-term efforts in the popularization of robotics, engineering, and artificial intelligence (STEM) using the NAO humanoid robot. By a conservative estimate, over a span of 8 years, we engaged at least a couple of thousand participants: approximately 70% were preschool children, 15% were elementary school students, and 15% were teenagers and adults. We describe several robot applications that were developed specifically for this task and assess their qualitative performance outside a controlled research setting, catering to various demographics, including those with special needs (ASD, ADHD). Five groups of applications are presented: (1) motor development activities and games, (2) children’s games, (3) theatrical performances, (4) artificial intelligence applications, and (5) data harvesting applications. Different cases of human–robot interactions are considered and evaluated according to our experience, and we discuss their weak points and potential improvements. We examine the response of the audience when confronted with a humanoid robot featuring intelligent behavior, such as conversational intelligence and emotion recognition. We consider the importance of the robot’s physical appearance, the emotional dynamics of human–robot engagement across age groups, the relevance of non-verbal cues, and analyze drawings crafted by preschool children both before and after their interaction with the NAO robot.