Educational robots are increasingly adopted to support children’s learning through interactive and personalized learning. Social interaction remains a crucial mechanism for effective learning, yet teacher and parental involvement in out-of-class learning activities is minimal or limited to supervisory roles. To support students with socially and intellectually meaningful learning experiences and to augment teachers’ pedagogical strategies outside of a classroom environment, we aim to explore the design of a learning companion robot by building a better understanding of the use cases for robot-assisted homework. In this paper, we report on findings from in-home technology probe studies with 10 students (aged 10–12), which revealed student expectations surrounding what support needs to be delivered by the robot and how it should be delivered. We discuss the themes of our findings and their implications for future design of social robots for homework assistance.

The sense of family connectedness may support positive outcomes including individual well-being, resilience, and healthy family functioning. However, as technologies advance, they often replace human-human interactions instead of nurturing them. In this work, we investigate how robot-facilitated communication tools might instead create new opportunities for family connection. We conducted two studies with families with children aged 5-12. We first explored the design space through in-home technology probe sessions with six families. These probes inspired us to explore two key interaction design dimensions: the robot’s behavior strategy (passive, reactive, proactive) and the mode of communication (synchronous, asynchronous). We then conducted a laboratory study with 20 families to examine how the two dimensions shaped parent-child interaction and connection. Our findings characterize how parents and children appropriated robot-mediated exchanges, the tensions they experienced around initiative, timing, and privacy, and the opportunities they envisioned for supporting everyday connectedness.

Meaningful connections formed between people and robots are a key factor in sustaining long-term interaction. Yet while onboarding experiences for social robot products are often carefully designed to cultivate these bonds, offboarding receives far less attention. This imbalance can result in abrupt disruptions in human-robot bonds when products reach end-of-life. In this paper, we examine a case study describing the shutdown of Moxie, a social robot designed to support children’s socio-emotional learning. Through a qualitative analysis of the company’s public communications and users’ online reactions to the shutdown, we identify key missed opportunities to prepare and support users throughout the robot’s final interactions. In the absence of a structured offboarding experience, the emotional, technical, and communicative burdens were shifted to parents. Drawing from these findings, we introduce ethical sunsetting recommendations for social robots and offer a reimagined offboarding experience aimed at supporting healthy emotional detachment during product end-of-life.

This paper examines the community-driven sustenance of Moxie, a social robot that faced discontinuation when its parent company failed to secure funding. Through interviews and investigative digital ethnography, we study how users performed extensive articulation work to transition from corporate support to an open-source platform. Our findings reveal that invisible labor and value negotiation were central to Moxie's continued operation, simultaneously opening access for some users while excluding others. These processes also fundamentally reshaped the robot's desirability and meaning within the user community. This work demonstrates how socio-technical infrastructures, articulation work, and value renegotiation can sustain robots beyond their commercial lifecycles, while revealing the uneven distribution of both labor and access in community-driven technology repair and maintenance.

Human-Robot Interaction (HRI) research in real-world settings may lead to unanticipated, emotionally charged moments. While impacts of these moments on participants are reported in the literature, researchers’ emotions, which can affect participants’ experiences, are often left unreported. Learning from these moments is essential for advancing HRI quality and real-world deployment success. We introduce "Emotional Entanglements" as a lens in HRI to define a researcher's capacity to anticipate, absorb, respond to, and recover from emotionally impactful events. Collecting testimonials using collaborative autoethnography from eleven researchers, we surface recurring emotional entanglements experienced in HRI studies, including tears with mixed meaning, participant attachment and loss upon robot withdrawal, and consequential participant decisions attributed to the robot, as well as how researchers navigated them amidst protocol constraints. This paper provides an actionable guide to "Emotional Sustainability in HRI", raising awareness of these often unreported situations and offering strategies for mitigation.

Working with children in human-robot interaction (HRI) research presents novel challenges that require thoughtful preparation and reflection.However, given that HRI is still an emerging field of research, there is limited guidance for early-career HRI researchers focusing on child-robot interaction.We present a practical guide to working with children and robots, tailored for early-career researchers whome we refer to throughout as 'roboticists.'We pose several questions to encourage reflection and consideration for working with children, and other vulnerable populations, when designing and testing robots.The goal of this guide is to provide a resource for early-career roboticists when designing human-robot interaction studies that include children.We structure our guide to reflect on three research phases-pre-study, study, and post-studyand address critical questions regarding ethics, logistics, and inclusive communication.With this WiP report, we hope to receive feedback from the IDC community and establish collaborations to improve the proposed guide.In future work, we plan to collect testimonials from roboticists to diversify and expand the practical insights into a more comprehensive practical guide.

The increasing presence of Artificial Intelligence (AI) systems geared towards children necessitates those who design and develop these technologies to understand how to address the emerging ethical questions in their development and use while maintaining a playful, child-friendly approach.Even more importantly, it is crucial to understand how we can address various tensions that have emerged among ethical principles.In this half-day workshop, keynote talks, poster presentations and interactive, "playful by design" will promote hands-on and rights-based design experiences for researchers and practitioners to ideate the benefits and challenges of designing playful and ethical child-AI systems.

Recent HCI research emphasizes the importance of considering children's care ecosystems in the design of systems that support their well-being, including not only family, but also extended support networks, such as teachers and therapists.While this approach offers opportunities for care and collaboration, it also introduces challenges such as recruitment, appropriate methods, power dynamics, navigating diverse perspectives, and ethical considerations.This workshop 1 seeks to bring together IDC researchers and practitioners to discuss opportunities, challenges, methods, and tools towards a research agenda for designing technologies for and with children and their care ecosystems.It also aims to foster collaboration and strengthen the community by facilitating networking among researchers towards conducting multi-stakeholder research.

Machine learning models deployed locally on social media applications are used for features, such as face filters which read faces in-real time, and they expose sensitive attributes to the apps. However, the deployment of machine learning models, e.g., when, where, and how they are used, in social media applications is opaque to users. We aim to address this inconsistency and investigate how social media user perceptions and behaviors change once exposed to these models. We conducted user studies (N=21) and found that participants were unaware to both what the models output and when the models were used in Instagram and TikTok, two major social media platforms. In response to being exposed to the models' functionality, we observed long term behavior changes in 8 participants. Our analysis uncovers the challenges and opportunities in providing transparency for machine learning models that interact with local user data.

In this late-breaking report, we present our design process motivated to build tangible, cost-effective, child- and family-friendly social robot prototypes aimed to (1) support the practical needs of families, as well as (2) serve as a feasible research platform to mitigate technical and logistical challenges faced within conducting inhome HRI studies. We apply a research through design approach, combined with participatory design methods, to produce functional prototypes. In future work, we are preparing to continue improving prototype design by conducting (1) iterative co-design sessions with artists and product designers focusing on the hardware design, (2) iterative co-design sessions with families focusing on the interaction design, and (3) long-term technology probe studies in homes to evaluate the effectiveness of the technological platform. We aim to solicit feedback from the HRI community on our design process and prototype research platform.

Traditional approaches to human-robot interaction design typically examine robot behaviors in controlled environments and narrow tasks. These methods are impractical for designing robots that interact with diverse user groups in complex human environments. Drawing from the field of theater, we present the construct of scenes—individual environments consisting of specific people, objects, spatial arrangements, and social norms—and tangible scenography, as a holistic design approach for human-robot interactions. We created a design tool, Tangible Scenography Kit (TaSK), with physical props to aid in design brainstorming. We conducted design sessions with eight professional designers to generate exploratory designs. Designers used tangible scenography and TaSK components to create multiple scenes with specific interaction goals, characterize each scene’s social environment, and design scene-specific robot behaviors. From these sessions, we found that this method can encourage designers to think beyond a robot’s narrow capabilities and consider how they can facilitate complex social interactions.

As robotic products become more integrated into daily life, there is a greater need to understand authentic and real-world human-robot interactions to inform product design. Across many domestic, educational, and public settings, robots interact with not only individuals and groups of users, but also families, including children, parents, relatives, and even pets. However, products developed to date and research in human-robot and child-robot interactions have focused on the interaction with their primary users, neglecting the complex and multifaceted interactions between family members and with the robot. There is a significant gap in knowledge, methods, and theories for how to design robots to support these interactions. To inform the design of robots that can support and enhance family life, this paper provides (1) a narrative review exemplifying the research gap and opportunities for family-robot interactions and (2) an actionable family-centered framework for research and practices in human-robot and child-robot interaction.

Despite advances in areas such as the personalization of robots, sustaining adoption of robots for long-term use in families remains a challenge. Recent studies have identified integrating robots into families’ routines and rituals as a promising approach to support long-term adoption. However, few studies explored the integration of robots into family routines and there is a gap in systematic measures to capture family preferences for robot integration. Building upon existing routine inventories, we developed Family-Robot Routines Inventory (FRRI), with 24 family routines and 24 child routine items, to capture parents’ attitudes toward and expectations from the integration of robotic technology into their family routines. Using this inventory, we collected data from 150 parents through an online survey. Our analysis indicates that parents had varying perceptions for the utility of integrating robots into their routines. For example, parents found robot integration to be more helpful in children’s individual routines, than to the collective routines of their families. We discuss the design implications of these preliminary findings, and how they may serve as a first step toward understanding the diverse challenges and demands of designing and integrating household robots for families.

The goal of this workshop is to have interdisciplinary discussions on family-centered interaction design of technology as an extension to child-centered design. The workshop will discuss the potential benefits of a family-centered approach to design, as well as the challenges and open questions that designers may face when adopting this approach. Through discussions and interactive activities, participants will have the opportunity to discuss and share ideas on how to effectively incorporate a family-centered perspective into their own design processes. A family-centered approach to design has the potential to create more meaningful and contextual experiences for children and their families.

In this work, we discuss a theoretically motivated family-centered design approach for child-robot interactions, adapted by Family Systems Theory (FST) and Family Ecological Model (FEM). Long-term engagement and acceptance of robots in the home is influenced by factors that surround the child and the family, such as child-sibling-parent relationships and family routines, rituals, and values. A family-centered approach to interaction design is essential when developing in-home technology for children, especially for social agents like robots with which they can form connections and relationships. We review related literature in family theories and connect it with child-robot interaction and child-computer interaction research. We present two case studies that exemplify how family theories, FST and FEM, can inform the integration of robots into homes, particularly research into child-robot and family-robot interaction. Finally, we pose five overarching recommendations for a family-centered design approach in child-robot interactions.

We aim to design robotic educational support systems that can promote socially and intellectually meaningful learning experiences for students while they complete school work outside of class. To pursue this goal, we conducted participatory design studies with 10 children (aged 10–12) to explore their design needs for robot-assisted homework. We investigated children’s current ways of doing homework, the type of support they receive while doing homework, and co-designed the speech and expressiveness of a homework companion robot. Children and parents attending our design sessions explained that an emotionally expressive social robot as a homework aid can support students’ motivation and engagement, as well as their affective state. Children primarily perceived the robot as a dedicated assistant at home, capable of forming meaningful friendships, or a shared classroom learning resource. We present key design recommendations to support students’ homework experiences with a learning companion robot.

Social robots are becoming increasingly prevalent in the real world. Unsupervised user interactions in a natural and familiar setting, such as the home, can reveal novel design insights and opportunities. This paper presents an analysis and key design insights from family-robot interactions, captured via on-robot recordings during an unsupervised four-week in-home deployment of an autonomous reading companion robot for children. We analyzed interviews and 160 interaction videos involving six families who regularly interacted with a robot for four weeks. Throughout these interactions, we observed how the robot's expressions facilitated unique interactions with the child, as well as how family members interacted with the robot. In conclusion, we discuss five design opportunities derived from our analysis of natural interactions in the wild.

Research in child-robot interactions suggests that engaging in “care-taking” of a social robot, such as tucking the robot in at night, can strengthen relationships formed between children and robots. In this work, we aim to better understand and explore the design space of caretaking activities with 10 children, aged 8–12 from eight families, involving an exploratory design session followed by a preliminary feasibility testing of robot caretaking activities. The design sessions provided insight into children’s current caretaking tasks, how they would take care of a social robot, and how these new caretaking activities could be integrated into their daily routines. The feasibility study tested two different types of robot caretaking tasks, which we call connection and utility, and measured their short term effects on children’s perceptions of and closeness to the social robot. We discuss the themes and present interaction design guidelines of robot caretaking activities for children.

Social robots are emerging as learning companions for children, and research shows that they facilitate the development of interest and learning even through brief interactions. However, little is known about how such technologies might support these goals in authentic environments over long-term periods of use and interaction. We designed a learning companion robot capable of supporting children reading popular-science books by expressing social and informational commentaries. We deployed the robot in homes of 14 families with children aged 10–12 for four weeks during the summer. Our analysis revealed critical factors that affected children’s long-term engagement and adoption of the robot, including external factors such as vacations, family visits, and extracurricular activities; family/parental involvement; and children’s individual interests. We present four in-depth cases that illustrate these factors and demonstrate their impact on children’s reading experiences and discuss the implications of our findings for robot design.

Social robots are increasingly introduced into children’s lives as educational and social companions, yet little is known about how these products might best be introduced to their environments. The emergence of the “unboxing” phenomenon in media suggests that introduction is key to technology adoption where initial impressions are made. To better understand this phenomenon toward designing a positive unboxing experience in the context of social robots for children, we conducted three field studies with families of children aged 8 to 13: (1) an exploratory free-play activity (n = 12); (2) a co-design session (n = 11) that informed the development of a prototype box and a curated unboxing experience; and (3) a user study (n = 9) that evaluated children’s experiences. Our findings suggest the unboxing experience of social robots can be improved through the design of a creative aesthetic experience that engages the child socially to guide initial interactions and foster a positive child-robot relationship.

As social robots become increasingly prevalent in day-to-day environments, they will participate in conversations and appropriately manage the information shared with them. However, little is known about how robots might appropriately discern the sensitivity of information, which has major implications for human-robot trust. As a first step to address a part of this issue, we designed a privacy controller, Confidant, for conversational social robots, capable of using contextual metadata (e.g., sentiment, relationships, topic) from conversations to model privacy boundaries. Afterwards, we conducted two crowdsourced user studies. The first study ($n=174$) focused on whether a variety of human-human interaction scenarios were perceived as either private/sensitive or non-private/non-sensitive. The findings from our first study were used to generate association rules. Our second study ($n=95$) evaluated the effectiveness and accuracy of the privacy controller in human-robot interaction scenarios by comparing a robot that used our privacy controller against a baseline robot with no privacy controls. Our results demonstrate that the robot with the privacy controller outperforms the robot without the privacy controller in privacy-awareness, trustworthiness, and social-awareness. We conclude that the integration of privacy controllers in authentic human-robot conversations can allow for more trustworthy robots. This initial privacy controller will serve as a foundation for more complex solutions.

The increasing pervasiveness of inclusive educational environments poses an urgent need to implement research methodologies and practices that could shed light on how children's social interactions unfold in such contexts. Our work explores the use of mobile eye tracking technology in naturalistic, inclusive K12 education settings towards a richer understanding of the children’s interactive behaviours. This paper presents the children’s responses to, experiences with and impressions about the naturalness of using mobile eye tracking glasses during a collaborative group task. Results highlight the importance of understanding the children’s experiences to foster naturalistic research environments that closely reflect real-life complexity. Our work contributes towards the deployment of research designs in naturalistic contexts, providing important clues towards the collection of ecologically valid real-world data.

Children’s early numerical knowledge establishes a foundation for later development of mathematics achievement and playing linear number board games is effective in improving basic numerical abilities. Besides the visuo-spatial cues provided by traditional number board games, learning companion robots can integrate multi-sensory information and offer social cues that can support children’s learning experiences. We explored how young children experience sensory feedback (audio and visual) and social expressions from a robot when playing a linear number board game, “RoboMath.” We present the interaction design of the game and our investigation of children’s (n = 19, aged 4) and parents’ experiences under three conditions: (1) visual-only, (2) audio-visual, and (3) audio-visual-social robot interaction. We report our qualitative analysis, including the themes observed from interviews with families on their perceptions of the game and the interaction with the robot, their child’s experiences, and their design recommendations.

Emotion expression in human-robot interaction has been widely explored, however little is known about how such expressions should be coupled with feelings and opinions expressed by a social robot. We explored how 12 children experienced emotionally expressive social commentaries from a reading companion robot across five interaction styles that differed in their non-verbal emotional expressiveness and opinionated conversational styles (neutral, divergent, or convergent opinions). We found that, while the robot’s opinions and non-verbal emotion expressions affected children’s experiences with the robot, the speech content of the commentaries was the more prominent factor in their experience. Additionally, children differed in their perceptions of social commentary: while some expressed a sense of connection-making with the robot’s self-disclosure commentaries, others felt distracted by them or felt like the robot was off-topic. We recommend designers pay particular attention to the robot’s speech content and consider children’s individual differences in designing emotional and opinionated speech.

Child-robot interactions in educational, developmental, and health domains are widely explored, but little is known about how families perceive the presence of a social robot in their home environment and its participation in day-to-day activities. To close this gap, we conducted a participatory design (PD) study with six families, with children aged 10--12, to examine how families perceive in-home social robots participating in shared activities. Our analysis identified three main themes: (1) the robot can have a range of roles in the home as a companion or as an assistant; (2) family members have different preferences for how they would like to interact with the robot in group or personal interactions; and (3) families have privacy, confidentiality, and ethical concerns regarding a social robot's presence in the home. Based on these themes and existing literature, we provide guidelines for the future interaction design of in-home social robots for children.