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.

In human–robot collaboration, people's perceptions of robot abilities can shape how they interact with it. Drawing on insights from a pilot study, we hypothesized that a robot's backstory and gaze patterns might strengthen perceptions of competence. To examine this, we conducted a 2×2 (backstory x gaze patterns) mixed-method study in a museum-based lab setting, where 66 participants completed two open-ended gift-box assembly tasks (makeup vs. pet-owner items) with a Misty robot. Contrary to our expectations and much of the HRI literature, we found no significant differences across experimental conditions. However, participants' interactions revealed important insights about collaboration dynamics. Two distinct collaboration levels emerged: while a quarter of participants maintained low levels of collaboration, the majority engaged in highly collaborative exchanges with the robot. These collaboration levels were associated with different perceptions of warmth and competence and different interaction patterns. A thematic analysis showed that in low-collaboration cases, participants' self-efficacy (i.e. the confidence they held in their own task competence) did not appear to influence the interaction. However, within high-collaboration cases, self-efficacy shaped how participants engaged with the robot. Those with high self-efficacy critically evaluated the robot's contributions against their own expertise, whereas those with low self-efficacy leaned more heavily on the robot's guidance. Across both groups, several factors seemed to enhance collaboration: the robot's answer justifications, occasional pushback from the robot, and participants' growing familiarity with the interaction. We discuss how future systems should account for users' confidence levels, balance responsiveness with constructive disagreement, and support adaptation to foster more balanced and effective collaboration.

Robots are increasingly entering the daily lives of families, yet their successful integration into domestic life remains a challenge. We explore family routines as a critical entry point for understanding how robots might find a sustainable role in everyday family settings. Together with each of the ten families, we co-designed robot interactions and behaviors, and a plan for the robot to support their chosen routines, accounting for contextual factors such as timing, participants, locations, and the activities in the environment. We then designed, prototyped, and deployed a mobile social robot in a four-day, in-home user study. Families welcomed the robot’s reminders, with parents especially appreciating the offloading of some reminding tasks. At the same time, interviews revealed tensions around timing, authority, and family dynamics, highlighting the complexity of integrating robots into households beyond the immediate task of reminders. Based on these insights, we offer design implications for robot-facilitated contextual reminders and discuss broader considerations for designing robots for family settings.

Despite the soaring rate of mobile device ownership among older adults, a common barrier to their continued mobile use is little to no tech support for learning or troubleshooting the complexities of mobile apps, features, and services. In this paper, using interviews (n = 23) and surveys (n = 259) with older adults, we develop and evaluate the mobile tech support questionnaire (MTSQ). MTSQ measures older adults’ preference for and perceived quality of support during continued mobile tech use. An exploratory factor analysis revealed two dimensions, helpful resources used on one’s own (self-reliant) and help from another person (social). Next, partial least squares structural equation modeling was used to explore the relationship between preference, quality, frequency, and ease of use of mobile tech support. Both preference for and quality of a support type positively influenced how frequently older adults used that type of support and perceived its ease of use.

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.

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.

The application of social robots in education is an emerging field that has the potential to transform the way we teach and learn. In this work, we compare the effects of a physically co-located social robot and a virtual social robot on learning experiences, as students reason about geometry conjectures. Our thematic analysis of interactions and interviews show that the co-located robot was treated more socially and improved learning experiences compared to a virtual robot. Specifically, it increased the perceived effectiveness of the interaction to lower anxiety, provide companionship, and support reasoning and comprehension. These results support the use of co-located, physically present robots to complement learning activities that benefit from social interaction, including reasoning about complex problems and use of gestures. This research contributes to the growing body of literature on the use of social robots in education and highlights the potential for further research on this subject.

Educational technologies can provide students with adaptive feedback and guidance, but these systems lack personal interactions that make social and cultural connections to the student's own classroom and prior experiences. Social or companion robots have a high capacity for these types of interactions, but typically require advanced levels of expertise to program. In this study, we examined teachers use of an authoring tool to enable them to leverage their classroom-based expertise to design robot-assisted homework assignments, and explore how seeing a robot enact their designs influences their work. We found that the tool enabled the teachers to create novel social interactions for homework activities that were similar to their classroom interaction patterns. These interaction designs evolved over time and were shaped by the teacher's emerging mental model of the social robot, their concept of the students' perspective of these interactions, and a shift towards informal classroom-like interaction paradigms, thus transforming their view of what they can achieve with homework. We discuss how these findings demonstrate how the context of the activity can influence initial mental models of social activities and suggest practical guidance on designing authoring tools to best facilitate the creation of computer or robot supported social activities, such as homework.

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.

In this paper, we explore how the physically embodied nature of robots can influence learning through non-verbal communication, such as gesturing. We take an embodied cognition perspective to examine student interactions with a NAO robot that uses gestures while reasoning about geometry conjectures. College aged students (N = 30) were randomly assigned to either a dynamic condition, where the robot uses dynamic gestures that represent and manipulate geometric shapes in the conjectures, or control condition, where the robot uses beat gestures that match the rhythm of speech. Students in the dynamic condition: (1) use more gestures when they reason about geometry conjectures, (2) look more at the robot as it speaks, (3) feel the robot is a better study partner and uses effective gestures, but (4) were not more successful in correctly reasoning about geometry conjectures. We discuss implications for socially supported and embodied learning with a physically present robot.

Computing is rapidly becoming a critical literacy for succeeding in an increasingly technological world. While the proliferation of programs dedicated to broadening participation in computing increases access, computing education research can benefit from more directly drawing on current interest development theory to improve interventions that increase the desire to participate and persist in computing. In this article, we present an overview of current interest development theory and provide guidance to computing education researchers on ways to ground their conceptualization and measurement of interest in contemporary theory and inform ways of interweaving interest theory throughout intervention or curriculum design. The central contribution of this work is presenting the Integrated Interest Development for Computing Education Framework. This framework is organized around three central dimensions of interest: value, knowledge, and belonging. For each of these dimensions, the framework presents key factors that link the dimension to strategies that can be employed in computing education contexts to help develop interest. The article also describes methods of measuring interest in computing that are consistent with interest development theory, and provides examples and resources for validated measures of interest. We conclude with a discussion of the implications and potential for improving the conceptualization and measurement of interest development in computing education and future work needed to advance an understanding of how interest in computing develops that can lead to improving the design of computing educational programs to support interest development.

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.

Collaborative robots, or cobots, represent a breakthrough technology designed for high-level (e.g. collaborative) interactions between workers and robots with capabilities for flexible deployment in industries such as manufacturing. Understanding how workers and companies use and integrate cobots is important to inform the future design of cobot systems and educational technologies that facilitate effective worker-cobot interaction. Yet, little is known about typical training for collaboration and the application of cobots in manufacturing. To close this gap, we interviewed nine experts in manufacturing about their experience with cobots. Our thematic analysis revealed that, contrary to the envisioned use, experts described most cobot applications as only low-level (e.g. pressing start/stop buttons) interactions with little flexible deployment, and experts felt traditional robotics skills were needed for collaborative and flexible interaction with cobots. We conclude with design recommendations for improved future robots, including programming and interface designs, and educational technologies to support collaborative use.

Education research offers strong evidence that social supports, learning interventions situated in meaningful social interaction, during learning can aid in developing interest and promote understanding for the content. However, children are often asked to complete homework tasks in isolation. To address this discrepancy, we build on prior work in social robotics to demonstrate the effectiveness of a socially adept robot, as compared to a socially neutral robot to generate situational interest and improve learning while reading a science textbook. We conducted a randomized controlled experiment (N = 63) of one reading interaction with either the socially adept or socially neutral robot. Our results show that children who read with a socially adept robot found the robot to be friendlier and more attractive, reported a higher level of closeness and mutual-liking for the robot, had higher situational interest, and made more scientifically accurate statements on a concept-map activity. We discuss the practical and theoretical implications of these findings.

The development of literacy and reading proficiency is a building block of lifelong learning that must be supported both in the classroom and at home. While the promise of interactive learning technologies has widely been demonstrated, little is known about how an interactive robot might play a role in this development. We used eight design features based on recommendations from interest-development and human-robot-interaction literatures to design an in-home learning companion robot for children aged 11--12. The robot was used as a technology probe to explore families' (N=8) habits and views about reading, how a reading technology might be used, and how children perceived reading with the robot. Our results indicate reading with the learning companion to be a way to socially engage with reading, which may promote the development of reading interest and ability. We discuss design and research implications based on our findings.