Social robots in the home bring new privacy risks and concerns for older adults. Yet, current technology privacy mechanisms typically use a one-time and universal consent mechanism (e.g., user agreement checkbox, browser cookie setting, etc.), lacking consideration of how privacy is holistically experienced. Designing for privacy requires a multidimensional approach to support how older adults experience privacy. To investigate older adult-centered privacy mechanisms for social robots, we conducted two participatory design (PD) workshops at local assisted living facilities. Our findings from these workshops suggest that older adults do not treat privacy as static, but as a temporal and situational practice that requires continuous negotiations and revisions. We subsequently conducted a post-PD speculative design (SD) process that extracted three design features for privacy—aware social robots-privacy profiles, real-time privacy feedback, and data ownership tools—that can support older adults’ multidimensional privacy experiences.

As generative AI (GenAI) emerges as a transformative force, clear understanding of high school students' perspectives is essential for GenAI's meaningful integration in high school environments. In this work, we draw insights from a participatory design workshop where we engaged 17 high school students -- a group rarely involved in prior research in this area -- through the design of novel GenAI tools and school policies addressing their key concerns. Students identified challenges and developed solutions outlining their ideal features in GenAI tools, appropriate school use, and regulations. These centered around the problem spaces of combating bias & misinformation, tackling crime & plagiarism, preventing over-reliance on AI, and handling false accusations of academic dishonesty. Building on our participants' underrepresented perspectives, we propose new guidelines targeted at educational technology designers for development of GenAI technologies in high schools. We also argue for further incorporation of student voices in development of AI policies in their schools.

CHI ’25, Yokohama, Japan

Wearable technology has significantly improved the quality of life for older adults, and the emergence of on-body, movable robots presents new opportunities to further enhance well-being. Yet, the interaction design for these robots remains under-explored, particularly from the perspective of older adults. We present findings from a two-phase co-design process involving 13 older adults to uncover design principles for on-body robots for this population. We identify a rich spectrum of potential applications and characterize a design space to inform how on-body robots should be built for older adults. Our findings highlight the importance of considering factors like co-presence, embodiment, and multi-modal communication. Our work offers design insights to facilitate the integration of on-body robots into daily life and underscores the value of involving older adults in the co-design process to promote usability and acceptance of emerging wearable robotic technologies.

Power dynamics through the lens of autonomy in human-robot interaction (HRI) has largely been considered through the robot's persuasion and its impact on users. Power found in hierarchies and relationships between users and robots has been less investigated. Through a co-design workshop with industry employees, we investigate how employees design autonomous agents (AA) for a risk management and safety task, focusing on the designed agent interactions and personalities (robots and chatbots). The qualitative analysis of the storyboards and personalities revealed how employees designed autonomy and power dynamics between the workers and AAs, mirroring power dynamics in social systems. These results revealed how power dynamics are interconnected with anthropomorphization of AAs, demonstrating what has been previously theorized by HRI scholars. Overall, this work considers autonomy and power in design representations and unifies varying conceptualizations of power in HRI to support a holistic perspective of how power is explored through HRI design.

Children are beginning to interact and develop rapport with social robots in their homes. These devices pose new concerns around marketing to children. These include questions of how advertisements can and should be embedded in a robot and the robot's persona and which methods of conveying advertisements to the user are deceptive. In this paper, we engage with 62 children ages 9–12 in an activity to design future robot advertising policies. Results demonstrate that children prefer robots to advertise to them through casual conversations, citing a more positive user experience and the benefit of personalized and conversationally relevant advertising. These findings illuminate a tension between child preferences and more deceptive advertising policies. Overall, the work presented in this paper prompts new design and legal policy questions for how and if robots should advertise to children.

Technology, such as robots, can entrench existing inequities and create new ones. Leveraging design justice, par-ticipatory design, and design fictions, we propose new ways for designing robots and policies around social robots to incorporate more voices and values into design processes. This work critically examines design justice in the context of human-robot interaction (HRI) and suggests a framework to engage multiple stakeholders in participatory design of robots and policies grounded in design justice. Overall, we promote discussion around how we can design more equitable robot technology design and policy design in HRI.

In the past two decades, human-robot interaction (HRI) researchers have increasingly deployed autonomous and reliable robots long-term in various social contexts including the home. Our work provides a mixed-method approach for analyzing older adults' long-term robot usage data patterns combining quantitative data of robot usage logs with qualitative descriptions from participants' own experience. Overall, this provides a fuller picture to how older adults use and experience social robots in their homes. Our work involves a robot hosting period for at least a month (up to 12 months) in older adults' homes with an experience debrief session held a month into the robot hosting time period. We propose reflections on the novelty effect with respect to older adults' usage data and highlight feelings of guilt, the robot's proactivity and movement, meeting (or not meeting) user expectations, and the robot's persona as key aspects of the hosting experience that promoted usage or non-usage. Finally, we provide design guidelines for structuring future mixed-method long-term robot usage studies being mindful of ethical considerations in this space.

Social agents have been mostly designed to engage with children one-on-one as tutors or learning peers. Besides this child-robot dyadic interaction paradigm, they have the potential to empower parents to more actively interact with their children. Robot-assisted parent-child interaction could be a sustainable future approach for promoting children’s in-home learning. Motivated by this new design direction, this work takes an iterative design approach to explore how we design triadic interactions through "lived technology experiences" and interviews. For 3-6 weeks, we deployed and remotely teleoperated a social robot in the homes of 12 families with 3-7-year-old children to engage in a triadic story reading activity with both parent and child for six 25-min sessions. Before and after the deployment, we conducted a semi-structured interview with participants on their triadic interaction experience and desired robot design features. The results of our qualitative analysis show that social robots can improve various aspects of parent-child interaction. We propose design guidelines for robot-assisted parent-child interactions at home, the considerations of participants’ values around technology design, and promotion of their long-term lived technology experiences as critical sources for design knowledge.

As social robots rapidly become mainstream technologies, it is critical for HRI researchers and practitioners to consider their societal and ethical impacts as well as their ability to perpetuate or mitigate intersectional social inequities and hierarchies relating to race, class, gender, disability, and other social axes. Through an equity, ethics, and justice-centered audit of human-robot interaction (HRI) scholarship, we reveal how the HRI community has engaged with these topics over the past two decades. We use the five senses ethical framework that has been proposed specifically for use in HRI contexts to perform the review paired with an analysis of equity and justice. We then expand the Design Justice framework (a framework for analyzing how design impacts society and distributes benefits and burdens to society through the lenses of equity, values, scope, ownership, and accountability) to HRI contexts through the inclusion of HRI-specific topics such as autonomy, transparency, deception, and policies. We invite researchers and practitioners to explore the HRI Equitable Design framework to work towards designing equitable and inclusive HRI research studies and technologies.

The word “robot” frequently conjures unrealistic expectations of utilitarian perfection: tireless, efficient and flawless agents. However, real-world robots are far from perfect—they fail and make mistakes. Thus, roboticists should consider altering their current assumptions and cultivating new perspectives that account for a more complete range of robot roles, behaviors, and interactions. To encourage this, we explore the use of metaphors for generating novel ideas and reframing existing problems, eliciting new perspectives of human-robot interaction. Our work makes two contributions. We (1) surface current assumptions that accompany the term “robots,” and (2) present a collection of alternative perspectives of interaction with robots through metaphors. By identifying assumptions, we provide a comprehensible list of aspects to reconsider regarding robots’ physicality, roles, and behaviors. Through metaphors, we propose new ways of examining how we can use, relate to, and co-exist with the robots that will share our future.

More and more voice-user interfaces (VUIs), such as smart speakers like Amazon Alexa or social robots like Jibo or Cozmo, are entering multi-user environments including homes. VUIs can utilize multi-modal cues such as graphics, expressive sounds, and movement to convey social engagement, affecting how users perceive agents as social others. Reciprocal relationships with VUIs, i.e., relationships with give-and-take between the VUI and user, are of key interest as they are more likely to foster rapport and emotional engagement, and lead to successful collaboration. Through an elicitation study with three commercially available VUIs, we explore small group interactions (n = 33 participants) focused on the behaviors participants display to various VUIs to understand (1) reciprocal interactions between VUIs and participants and among small groups and (2) how participants engage with VUIs as the interface's embodiment becomes more socially capable. The discussion explores (1) theories of sociability applied to the users' behaviors seen with the VUIs, and (2) the group contexts where VUIs that build reciprocal relationships with users can become a powerful persuasive technology and a collaborative companion. We conclude the discussion with recommendations for promoting reciprocity from participants and, therefore, fostering rapport and emotional engagement in VUI interactions.

Users can provide valuable insights for designing new technologies like social robots, with the right tools and methodologies. Challenges in inviting users as co-designers of social robots is due to lack of guidelines or methodologies to (1) organize co-design processes and/or (2) engage with people long-term to develop technologies together. The main contribution of this work is to provide guidelines for long- term co-design for how other researchers can adopt long- term co-design, informed by a 12-month co-design with older adults designing a social social robot. We leveraged human- centered, tactile and experiential design activities, including participatory design, based upon the following design principles: scenario specific exploration, long-term lived experiences, supporting multiple design activities, cultivating relationships, and employing divergent and convergent processes. We present seven different sessions across three stages as examples of this methodology that build on each other to engage users as co- designers, successfully deployed in a co-design project of home social robots with 28 older adults. Lastly, we detail 10 long- term divergent-convergent co-design guidelines for designing social robots. We demonstrate the value of leveraging people's lived technology experiences and co-design activities to generate actionable social robot design guidelines, advocating for more applications of the methodology in broader contexts as well.

Artificial curiosity, based on developmental psychology concepts wherein an agent attempts to maximize its learning progress, has gained much attention in recent years. Similarly, social robots are slowly integrating into our daily lives, in schools, factories, and in our homes. In this contribution, we integrate recent advances in artificial curiosity and social robots into a single expressive cognitive architecture. It is composed of artificial curiosity and social expressivity modules and their unique link, i.e., the robot verbally and non-verbally communicates its internally estimated learning progress, or learnability, to its human companion. We implemented this architecture in an interaction where a fully autonomous robot took turns with a child trying to select and solve tangram puzzles on a tablet. During the curious robot’s turn, it selected its estimated most learnable tangram to play, communicated its selection to the child, and then attempted at solving it. We validated the implemented architecture and showed that the robot learned, estimated its learnability, and improved when its selection was based on its learnability estimation. Moreover, we ran a comparison study between curious and non-curious robots, and showed that the robot’s curiosity-based behavior influenced the child’s selections. Based on the artificial curiosity module of the robot, we have formulated an equation that estimates each child’s moment-by-moment curiosity based on their selections. This analysis revealed an overall significant decrease in estimated curiosity during the interaction. However, this drop in estimated curiosity was significantly larger with the non-curious robot, compared to the curious one. These results suggest that the new architecture is a promising new approach to integrate state-of-the-art curiosity-based algorithms to the growing field of social robots.