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.

We present the Robot Bookworm, a multi-session intervention co-designed with children and educators to foster reading motivation through personalized book discussions. The robot assigned each child a personally fitting book and engaged them in pedagogically structured discussions, with personalized book-aligned dialogic content selectively generated offline by a language model and moderated by people to ensure safety. We compared a personalized book discussion condition with a book-neutral control in a four-session, large-scale user study in two primary schools (N = 101, 8-11 y.o.). The intervention significantly increased reader-book relatedness and reading enjoyment, particularly for children with below-ceiling baseline enjoyment, but had no effect on intrinsic motivation. At a one-year follow-up, the quantitative effects were not sustained. However, children reported perceived positive shifts in attitudes towards reading, which they attributed to the Robot Bookworm.

In this paper, we present a participatory design approach to age-based personalization for child-robot interaction. This is an important step towards social robots being effective across age groups. As a testbed for our approach, we used humor. Personalized humor is a powerful social motivator and is uniquely suited to build relatable and sustained child-robot interactions. Through a series of co-design workshops (n = 102 children), we identified humor concepts that fit the specific sense of humor for each of the four age groups (8–9, 9–10, 10–11, 11–12 y.o.), as well as humor concepts that resonated across these age groups. A user study showed that, overall, children found the interaction more amusing and a better fit for both their own sense of humor and that of their peer group when the robot used age-personalized humor compared to age-agnostic humor. The strength of the effects varied by age group, with the oldest group consistently scoring lower on the outcome measures, indicating that the design was not equally effective for all groups.

When the technology encompasses some form of intelligence or agency in the form of robots, virtual agents or artificial intelligence, understanding the reasoning behind their actions and decisions becomes an integral part of the interaction. This challenge extends beyond mere interaction to ensure these technological entities engage with children in an understandable and transparent manner. Given the current emergence of research in explainability and transparency within human-robot interaction, a noticeable gap emerges when the target population shifts to children. Several challenges have contributed to this gap, including the more difficult job of considering children’s unique cognitive and emotional needs or aligning the complexity of the technology and the developmental stages of young users. As we advance the field through generating more effective explanations or transparent behaviours in robots and agents, transitioning these advancements to more child-centric contexts demands a deeper understanding of how children perceive and comprehend technological behaviours. This workshop explores this gap and how we could tackle the critical role of developing technologies, e.g., robots, AI, and toys that are more transparent and express more explainable behaviours. We aim to initiate discussions on the importance of understanding children’s perception of different technologies and approaches to generate and evaluate explainability features that are tailored for child users interacting with autonomous agents and robots. Simultaneously, we address the challenges inherent in this context, including potential biases in explainability and the risks associated with deception in child-technology interaction.

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.

To benefit 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 specifications of how children can practice math via a personal conversation with a social robot and how the robot can scaffold instructions. We evaluated the designs with a three-session experimental user study (n = 130, 8-11 y.o.). Participants got better at math over time when the robot scaffolded instructions. Furthermore, the robot felt more as a friend when it personalized the conversation.

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.

After the novelty effect wears off children need a new motivator to keep interacting with a social robot. Enabling children to build a relationship with the robot is the key for facilitating a sustainable long-term interaction. We designed a memory-based personalization strategy that safeguards the continuity between sessions and tailors the interaction to the child's needs and interests to foster the child-robot relationship. A longitudinal (five sessions in two months) user study (N = 46, 8–10 y.o) showed that the strategy kept children interested longer in the robot, fosters more closeness, elicits more positive social cues, and adds continuity between sessions.

Social robots have been introduced in different fields such as retail, health care and education. Primary education in the Netherlands (and elsewhere) recently faced new challenges because of the COVID-19 pandemic, lockdowns and quarantines including students falling behind and teachers burdened with high workloads. Together with two Dutch municipalities and nine primary schools we are exploring the long-term use of social robots to study how social robots might support teachers in primary education, with a focus on mathematics education. This paper presents an explorative study to define requirements for a social robot math tutor. Multiple focus groups were held with the two main stakeholders, namely teachers and students. During the focus groups the aim was 1) to understand the current situation of mathematics education in the upper primary school level, 2) to identify the problems that teachers and students encounter in mathematics education, and 3) to identify opportunities for deploying a social robot math tutor in primary education from the perspective of both the teachers and students. The results inform the development of social robots and opportunities for pedagogical methods used in math teaching, child-robot interaction and potential support for teachers in the classroom.

In this paper we specify and validate three interaction design patterns for an interactive storytelling experience with an autonomous social robot. The patterns enable the child to make decisions about the story by talking with the robot, reenact parts of the story together with the robot, and recording self-made sound effects. The design patterns successfully support children's engagement and agency. A user study (N = 27, 8-10 y.o.) showed that children paid more attention to the robot, enjoyed the storytelling experience more, and could recall more about the story, when the design patterns were employed by the robot during storytelling. All three aspects are important features of engagement. Children felt more autonomous during storytelling with the design patterns and highly appreciated that the design patterns allowed them to express themselves more freely. Both aspects are important features of children's agency. Important lessons we have learned are that reducing points of confusion and giving the children more time to make themselves heard by the robot will improve the patterns efficiency to support engagement and agency. Allowing children to pick and choose from a diverse set of stories and interaction settings would make the storytelling experience more inclusive for a broader range of children.

Nowadays, off-the-shelf social robots are used more frequently by the HRI community to research social interactions with different types of users across a range of domains such as education, retail, health care, public places and other domains. Everyone doing HRI research with end-users is invited to submit a case study to our workshop. We are particularly interested in case studies where things did not go as planned. Case studies describing research in the lab or in the wild are both welcome. Examples of unplanned experiences could include, but are not limited to, unexpected responses from the user, issues with the experimental setup or simply having challenges with transferring theory to the real world. In this workshop, we focus on off-the-shelf robots. In order to generalize and compare differences across multiple HRI domains and create common solutions, we will provide a template for your case study. We are interested in learning how such unexpected HRI results can be reported. In the workshop, we will discuss and study how failures are reported and be inspired to create a list of good ways to report failures, which can hopefully be inspiring for the HRI community.

Children are eager to anthropomorphize (ascribe human attributes to) social robots. As a consequence they expect a more unconstrained, substantive and useful interaction with the robot than is possible with the current state-of-the art. In this paper we reflect on several of our user studies and investigate the form and role of expectations in child-robot interaction. We have found that the effectiveness of the social assistance of the robot is negatively influenced by misaligned expectations. We propose three strategies that have to be worked out for the management of expectations in child-robot interaction: 1) be aware of and analyze children's expectations, 2) educate children, and 3) acknowledge robots are (perceived as) a new kind of `living' entity besides humans and animals that we need to make responsible for managing expectations.

Selecting the suitable form of a robot, i.e. physical or virtual, for a task is not straightforward. The choice for a physical robot is not self-evident when the task is not physical but entirely social in nature. Results from previous studies comparing robots with different body types are found to be inconclusive. We performed a user study to provide a more sound comparison between a virtual and physical robot operating in a social setting. Besides body type, we manipulated the sociability of the robot. Our results show that 1) user preferences indicate that robot sociability is more important than body type for selecting a robot in a non-physical social setting, and 2) the user's attitude towards robots is an important moderating factor influencing robot preference.