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To beneft from the social capabilities of a robot math tutor, instead of being distracted by them, a novel approach is needed where the math task and the robot's social behaviors are better intertwined. We present concrete design specifcations of how children can practice math via a personal conversation with a social robot and how the robot can scafold instructions. We evaluated the designs with a three-session experimental user study (g= 130, 8-11 y.o.). Participants got better at math over time when the robot scafolded instructions. Furthermore, the robot felt more as a friend when it personalized the conversation.

Social robots are an interesting piece of technology, especially for children. Children are immediately drawn to them. The social relationship that starts to form between children and robots has a lot of potential to do good. For example, with the research in this dissertation we aim to contribute to the development of a social robot companion for children with cancer to help them cope with stress. However, once the novelty of the robot wears off after the first few encounters, and children notice it has not really anything to offer, they lose interest. This is one of the major challenges in the human-robot interaction community. We need to equip the robot with the right social abilities to keep children engaged and appropriately foster the child-robot relationship. This dissertation has two parts. In part I we designed and studied robot behaviors that could be used in a distractive intervention. For that purpose the interaction must really be meaningful and social for the children. The meaning is provided through storytelling. We developed six interaction design patterns that enable children to co-decide and co-create parts of the story and coordinate their involvement during the co-creation process. By inviting children to interact with a robot that used these patterns we were able to study the effects on the interaction. The results showed that children paid more attention to a robot that uses the patterns, enjoyed the interaction more, and could recall more about the stories. This tells us that the patterns successfully supported children's engagement. Equally important, children also feel more agency and competence to co-regulate the interaction when the patterns are used. The patterns contributed to a more social interaction between the child and the robot. In part II we designed and studied robot behaviors that would foster the child-robot relationship. We started with enabling the children to properly get acquainted with the robot. First impressions matter. We learned that how children initiate relationships with people is similar to how children form relationships with robots. Important is that the children and the robot are enabled to share things about themselves to each other. This is called self-disclosure. We developed five interaction design patterns that enable children to comfortably self-disclose to the robot. We invited more children to talk to the robot and get acquainted. Results showed that the patterns provide the children with a clear and consistent way to talk to the robot. We learned that if the robot likes the same things as the child and positively affirms what they shared with it, children feel more similar to the robot and self-disclose more. We furthermore learned that both introverted and extraverted children self-disclose more and more intimately if the robot's behaviors are a bit toned down. The next step was to successfully enable the robot to have a conversation that spans multiple sessions. That is why we imagined each session as an episode in a serial TV show. The robot and the child are the main actors. The content was co-created with professional writers and it shows. Children were looking forward to talking to the robot each week partly because they wanted to know how the story continued. We developed eight interaction design patterns that store children's self-disclosure and use it to make subsequent sessions with the robot more personal. We called this the memory-based personalization strategy. In a longitudinal study we found that the memory-based personality strategy successfully communicated to the children that the robot remembers them. As a result they keep feeling close the robot. Furthermore, children remained consistently more willing to continue interacting with a robot using memory-based personalization.

Social robots are an interesting piece of technology, especially for children. Children are immediately drawn to them. The social relationship that starts to form between children and robots has a lot of potential to do good. For example, with the research in this dissertation we aim to contribute to the development of a social robot companion for children with cancer to help them cope with stress. However, once the novelty of the robot wears off after the first few encounters, and children notice it has not really anything to offer, they lose interest. This is one of the major challenges in the human-robot interaction community. We need to equip the robot with the right social abilities to keep children engaged and appropriately foster the child-robot relationship. This dissertation has two parts. In part I we designed and studied robot behaviors that could be used in a distractive intervention. For that purpose the interaction must really be meaningful and social for the children. The meaning is provided through storytelling. We developed six interaction design patterns that enable children to co-decide and co-create parts of the story and coordinate their involvement during the co-creation process. By inviting children to interact with a robot that used these patterns we were able to study the effects on the interaction. The results showed that children paid more attention to a robot that uses the patterns, enjoyed the interaction more, and could recall more about the stories. This tells us that the patterns successfully supported children's engagement. Equally important, children also feel more agency and competence to co-regulate the interaction when the patterns are used. The patterns contributed to a more social interaction between the child and the robot. In part II we designed and studied robot behaviors that would foster the child-robot relationship. We started with enabling the children to properly get acquainted with the robot. First impressions matter. We learned that how children initiate relationships with people is similar to how children form relationships with robots. Important is that the children and the robot are enabled to share things about themselves to each other. This is called self-disclosure. We developed five interaction design patterns that enable children to comfortably self-disclose to the robot. We invited more children to talk to the robot and get acquainted. Results showed that the patterns provide the children with a clear and consistent way to talk to the robot. We learned that if the robot likes the same things as the child and positively affirms what they shared with it, children feel more similar to the robot and self-disclose more. We furthermore learned that both introverted and extraverted children self-disclose more and more intimately if the robot's behaviors are a bit toned down. The next step was to successfully enable the robot to have a conversation that spans multiple sessions. That is why we imagined each session as an episode in a serial TV show. The robot and the child are the main actors. The content was co-created with professional writers and it shows. Children were looking forward to talking to the robot each week partly because they wanted to know how the story continued. We developed eight interaction design patterns that store children's self-disclosure and use it to make subsequent sessions with the robot more personal. We called this the memory-based personalization strategy. In a longitudinal study we found that the memory-based personality strategy successfully communicated to the children that the robot remembers them. As a result they keep feeling close the robot. Furthermore, children remained consistently more willing to continue interacting with a robot using memory-based personalization.

Social robots are an interesting piece of technology, especially for children. Children are immediately drawn to them. The social relationship that starts to form between children and robots has a lot of potential to do good. For example, with the research in this dissertation we aim to contribute to the development of a social robot companion for children with cancer to help them cope with stress. However, once the novelty of the robot wears off after the first few encounters, and children notice it has not really anything to offer, they lose interest. This is one of the major challenges in the human-robot interaction community. We need to equip the robot with the right social abilities to keep children engaged and appropriately foster the child-robot relationship. This dissertation has two parts. In part I we designed and studied robot behaviors that could be used in a distractive intervention. For that purpose the interaction must really be meaningful and social for the children. The meaning is provided through storytelling. We developed six interaction design patterns that enable children to co-decide and co-create parts of the story and coordinate their involvement during the co-creation process. By inviting children to interact with a robot that used these patterns we were able to study the effects on the interaction. The results showed that children paid more attention to a robot that uses the patterns, enjoyed the interaction more, and could recall more about the stories. This tells us that the patterns successfully supported children's engagement. Equally important, children also feel more agency and competence to co-regulate the interaction when the patterns are used. The patterns contributed to a more social interaction between the child and the robot. In part II we designed and studied robot behaviors that would foster the child-robot relationship. We started with enabling the children to properly get acquainted with the robot. First impressions matter. We learned that how children initiate relationships with people is similar to how children form relationships with robots. Important is that the children and the robot are enabled to share things about themselves to each other. This is called self-disclosure. We developed five interaction design patterns that enable children to comfortably self-disclose to the robot. We invited more children to talk to the robot and get acquainted. Results showed that the patterns provide the children with a clear and consistent way to talk to the robot. We learned that if the robot likes the same things as the child and positively affirms what they shared with it, children feel more similar to the robot and self-disclose more. We furthermore learned that both introverted and extraverted children self-disclose more and more intimately if the robot's behaviors are a bit toned down. The next step was to successfully enable the robot to have a conversation that spans multiple sessions. That is why we imagined each session as an episode in a serial TV show. The robot and the child are the main actors. The content was co-created with professional writers and it shows. Children were looking forward to talking to the robot each week partly because they wanted to know how the story continued. We developed eight interaction design patterns that store children's self-disclosure and use it to make subsequent sessions with the robot more personal. We called this the memory-based personalization strategy. In a longitudinal study we found that the memory-based personality strategy successfully communicated to the children that the robot remembers them. As a result they keep feeling close the robot. Furthermore, children remained consistently more willing to continue interacting with a robot using memory-based personalization.

Social robots are an interesting piece of technology, especially for children. Children are immediately drawn to them. The social relationship that starts to form between children and robots has a lot of potential to do good. For example, with the research in this dissertation we aim to contribute to the development of a social robot companion for children with cancer to help them cope with stress. However, once the novelty of the robot wears off after the first few encounters, and children notice it has not really anything to offer, they lose interest. This is one of the major challenges in the human-robot interaction community. We need to equip the robot with the right social abilities to keep children engaged and appropriately foster the child-robot relationship. This dissertation has two parts. In part I we designed and studied robot behaviors that could be used in a distractive intervention. For that purpose the interaction must really be meaningful and social for the children. The meaning is provided through storytelling. We developed six interaction design patterns that enable children to co-decide and co-create parts of the story and coordinate their involvement during the co-creation process. By inviting children to interact with a robot that used these patterns we were able to study the effects on the interaction. The results showed that children paid more attention to a robot that uses the patterns, enjoyed the interaction more, and could recall more about the stories. This tells us that the patterns successfully supported children's engagement. Equally important, children also feel more agency and competence to co-regulate the interaction when the patterns are used. The patterns contributed to a more social interaction between the child and the robot. In part II we designed and studied robot behaviors that would foster the child-robot relationship. We started with enabling the children to properly get acquainted with the robot. First impressions matter. We learned that how children initiate relationships with people is similar to how children form relationships with robots. Important is that the children and the robot are enabled to share things about themselves to each other. This is called self-disclosure. We developed five interaction design patterns that enable children to comfortably self-disclose to the robot. We invited more children to talk to the robot and get acquainted. Results showed that the patterns provide the children with a clear and consistent way to talk to the robot. We learned that if the robot likes the same things as the child and positively affirms what they shared with it, children feel more similar to the robot and self-disclose more. We furthermore learned that both introverted and extraverted children self-disclose more and more intimately if the robot's behaviors are a bit toned down. The next step was to successfully enable the robot to have a conversation that spans multiple sessions. That is why we imagined each session as an episode in a serial TV show. The robot and the child are the main actors. The content was co-created with professional writers and it shows. Children were looking forward to talking to the robot each week partly because they wanted to know how the story continued. We developed eight interaction design patterns that store children's self-disclosure and use it to make subsequent sessions with the robot more personal. We called this the memory-based personalization strategy. In a longitudinal study we found that the memory-based personality strategy successfully communicated to the children that the robot remembers them. As a result they keep feeling close the robot. Furthermore, children remained consistently more willing to continue interacting with a robot using memory-based personalization.

YOLO, a social robot for creativity stimulation. This project contains the hardware components for building YOLO.

This paper describes a longitudinal study in which children could interact unsupervised and at their own initiative with a fully autonomous computer aided learning (CAL) system situated in their classroom. The focus of this study was to investigate how the mindset of children is affected when delivering effort-related praise through a social robot. We deployed two versions: a CAL system that delivered praise through headphones only, and an otherwise identical CAL system that was extended with a social robot to deliver the praise. A total of 44 children interacted repeatedly with the CAL system in two consecutive learning tasks over the course of approximately four months. Overall, the results show that the participating children experienced a significant change in mindset. The effort-related praise that was delivered by a social robot seemed to have had a positive effect on children’s mindset, compared to the regular CAL system where we did not see a significant effect.

Studying interactions of children with humanoid robots in familiar spaces in natural contexts has become a key issue for social robotics. To fill this need, we conducted several Child–Robot Interaction (CRI) events with the Pepper robot in Polish and Japanese kindergartens. In this paper, we explore the role of trust and expectations towards the robot in determining the success of CRI. We present several observations from the video recordings of our CRI events and the transcripts of free-format question-answering sessions with the robot using the Wizard-of-Oz (WOZ) methodology. From these observations, we identify children’s behaviors that indicate trust (or lack thereof) towards the robot, e.g., challenging behavior of a robot or physical interactions with it. We also gather insights into children’s expectations, e.g., verifying expectations as a causal process and an agency or expectations concerning the robot’s relationships, preferences and physical and behavioral capabilities. Based on our experiences, we suggest some guidelines for designing more effective CRI scenarios. Finally, we argue for the effectiveness of in-the-wild methodologies for planning and executing qualitative CRI studies.

Young children help others in a range of situations, relatively indiscriminate of the characteristics of those they help. Recent results have suggested that young children’s helping behavior extends even to humanoid robots. However, it has been unclear how characteristics of robots would influence children’s helping behavior. Considering previous findings suggesting that certain robot features influence adults’ perception of and their behavior toward robots, the question arises of whether young children’s behavior and perception would follow the same principles. The current study investigated whether two key characteristics of a humanoid robot (animate autonomy and friendly expressiveness) would affect children’s instrumental helping behavior and their perception of the robot as an animate being. Eighty-two 3-year-old children participated in one of four experimental conditions manipulating a robot’s ostensible animate autonomy (high/low) and friendly expressiveness (friendly/neutral). Helping was assessed in an out-of-reach task and animacy ratings were assessed in a post-test interview. Results suggested that both children’s helping behavior, as well as their perception of the robot as animate, were unaffected by the robot’s characteristics. The findings indicate that young children’s helping behavior extends largely indiscriminately across two important characteristics. These results increase our understanding of the development of children’s altruistic behavior and animate-inanimate distinctions. Our findings also raise important ethical questions for the field of child-robot interaction.

Pediatric oncology patients could benefit from bonding with a social robot and talking about their day in the hospital. With our research we aim to contribute to the development of a robot that is able to facilitate a child-robot bond autonomously and long-term. We propose to use robot-disclosure and a shared interaction history to create a child-robot bond where the child feels comfortable and familiar enough to talk about their day with the robot. © 2018 Authors. ACM SIGAI; ACM SIGCHI; IEEE Robotics and Automation (IEEE RAS)

Pediatric oncology patients could benefit from bonding with a social robot and talking about their day in the hospital. With our research we aim to contribute to the development of a robot that is able to facilitate a child-robot bond autonomously and long-term. We propose to use robot-disclosure and a shared interaction history to create a child-robot bond where the child feels comfortable and familiar enough to talk about their day with the robot. © 2018 Authors. ACM SIGAI; ACM SIGCHI; IEEE Robotics and Automation (IEEE RAS)

This paper presents a study that analyzes the effects of robots’ gaze hints on children's performance in a card-matching game. We conducted a within-subjects study, in which children played a card game “Memory” in the presence of a robot tutor in two sessions. In one session, the robot gave hints to help the child find matching cards by looking at the correct match and, in the other session, the robot only looked at the child and did not give them any help. Our findings show that the use of gaze hints (help condition) made the matching task significantly easier and that children used a significantly fewer number of tries than without help. This study provides guidelines on how to design interactive behaviors for robots taking the role of tutors to elicit help-seeking behavior in children.

For effective child education, playing games with a social robot should be motivating for a longer period of time. One aspect that can affect the motivation of a child is the difficulty of a game. The game should be perceived as challenging, while at the same time, the child should be confident to meet the challenge. We designed a user modelling module that adapts the difficulty of a game to the child’s skill level, in order to provide children with the optimal challenge. This module applies a Bayesian rating method that estimates the child’s skill and game item’s difficulty levels to personalise the game progress. In an experiment with 22 children (aged between 10 and 12 years old), we tested whether the personalisation leads to a higher motivation to play with the robot. Although the personalised system did not challenge the participants optimally, this study shows that the Bayesian rating system is in principle able to measure the skill and performance of children in playing a game with a robot (even without accurate estimates of the difficulty of items). We outline multiple ways in which the rating method and module can be used to further personalise and enhance the child-robot interaction, other than adapting the difficulty of games (e.g. by adapting the dialogue and feedback).

For effective child education, playing games with a social robot should be motivating for a longer period of time. One aspect that can affect the motivation of a child is the difficulty of a game. The game should be perceived as challenging, while at the same time, the child should be confident to meet the challenge. We designed a user modelling module that adapts the difficulty of a game to the child’s skill level, in order to provide children with the optimal challenge. This module applies a Bayesian rating method that estimates the child’s skill and game item’s difficulty levels to personalise the game progress. In an experiment with 22 children (aged between 10 and 12 years old), we tested whether the personalisation leads to a higher motivation to play with the robot. Although the personalised system did not challenge the participants optimally, this study shows that the Bayesian rating system is in principle able to measure the skill and performance of children in playing a game with a robot (even without accurate estimates of the difficulty of items). We outline multiple ways in which the rating method and module can be used to further personalise and enhance the child-robot interaction, other than adapting the difficulty of games (e.g. by adapting the dialogue and feedback).

Research and commercial robots have infiltrated homes, hospitals and schools, becoming attractive and proving impactful for children’s healthcare, therapy, edutainment, and other applications. The focus of this thesis is to investigate a little explored issue of how children’s perception of the robot changes with age, and thus to create such a robot to adapt to these differences. In particular, this research investigates the impact of gender segregation on children’s interactions with a humanoid NAO robot. To this end, a series of experiments was conducted with children aged between 5 and 12 years old. The results suggest that children aged between 9 and 12 years old do not support gender segregation hypothesis with a gendered robot.In order to dynamically adapt to children’s age and gender, a perception module was developed using depth data and a collected depth dataset of 3D body metrics of 428 children aged between 5 and 16 years old. This module is able to successfully determine children’s gender in real-world settings with 60.89% (76.64% offline) accuracy and estimate children’s age with a mean absolute error of only 1.83 (0.77 offline) years. Additionally, a pretend play testbed was designed in order to address the challenges of evaluating child-robot interaction by exploiting the advantages of multi-modal, multi-sensory perception. The pretend play testbed performed successfully at children’s play center, where a humanoid NAO robot was able to dynamically adapt its gender by changing its synthesized voice to match child’s perceived age and gender. By analyzing the free play of children, the results confirm the hypothesis of gender segregation for children aged younger than 8 years old. These findings are important to consider when designing robotic applications for children in order to improve engagement, which is essential for robot’s educational and therapeutic benefits.

We present insights gleaned from a series of child-robot interaction experiments carried out in a hospital paediatric department. Our aim here is to share good practice in experimental design and lessons learned about the implementation of systems for social HRI with child users towards application in "the wild", rather than in tightly controlled and constrained laboratory environments: a trade-off between the structures imposed by experimental design and the desire for removal of such constraints that inhibit interaction depth, and hence engagement, requires a careful balance. © 2011 ACM.

We present insights gleaned from a series of child-robot interaction experiments carried out in a hospital paediatric department. Our aim here is to share good practice in experimental design and lessons learned about the implementation of systems for social HRI with child users towards application in the wild , rather than in tightly controlled and constrained laboratory environments: a trade-off between the structures imposed by experimental design and the desire for removal of such constraints that inhibit interaction depth, and hence engagement, requires a careful balance. © 2011 ACM.

We present the design approach and evaluation of our prototype called “Ranger”. Ranger is a robotic toy box that aims to motivate young children to tidy up their room. We evaluated Ranger in 14 families with 31 children (2-10 years) using the Wizard-of-Oz technique. This case study explores two different robot behaviors (proactive vs. reactive) and their impact on children’s interaction with the robot and the tidying behavior. The analysis of the video recorded scenarios shows that the proactive robot tended to encourage more playful and explorative behavior in children, whereas the reactive robot triggered more tidying behavior. Our findings hold implications for the design of interactive robots for children, and may also serve as an example of evaluating an early version of a prototype in a real-world setting. Categories and Subject Descriptors H.4 [Information Systems Applications]: Miscellaneous; I.2.9 [Artificial Intelligence]: Robotics— Commercial robots and applications