Social robots show promise as learning partners for children, yet most child–robot interaction (CRI) systems for education continue to rely on screens as the primary interface, even when an embodied robot is present. This dependency constrains physical movement, limits social engagement, and conflicts with children’s developmental needs for a tangible, embodied experience. We present TEECRI, a Tangible and Embodied Educational Child–Robot Interaction framework, that provides design principles and illustrative scenarios for screen-free CRI, in which interaction is distributed across a social robot and everyday physical objects augmented with embedded sensors such as RFID tags, accelerometers, touch, and pressure sensors. These sensor-enhanced objects communicate their state to the robot in real time, enabling the robot to maintain continuous spatial awareness of objects and children without any visual display. We present the preliminary system design and components, outline three design principles for fully embodied CRI, and illustrate this approach through five design scenarios.

To support engaging sustainability education, this paper introduces the Multi-Realm Robot (MR2) framework, a narrative-driven learning experience where a single robot character, Blossom, spans a 2D game, an augmented reality (AR) environment, and the physical world. The system integrates game-based learning, AR guidance, and a Wizard-of-Oz-operated social robot within a unified narrative. To our knowledge, this is the first work portraying a robot as a continuous character across multiple modalities, fostering children’s sustainable practices. We describe the narrative, system design, and playtest results, showing the framework’s promising impact.

Challenges such as hallucinations, biased outputs, and deepfakes underscore the need for AI literacy that helps users question, verify, and make sense of AI outputs. Furthermore, AI literacy in early childhood education (ages 3-8) remains an underdeveloped research area, compared to the rapidly expanding body of work for adults and older students. Yet significant challenges remain, including limited AI knowledge among caregivers and educators, a lack of validated age-appropriate curricula, and ongoing concerns about overuse, privacy abuse, security risks, anthropomorphism, and misunderstandings of AI capabilities. This workshop brings together researchers, educators, and designers to envision what community-centered AI literacy might look like.

Most mental health conditions emerge during adolescence, making university years pivotal for intervention. Nearly 30% of students worldwide experience mental health difficulties, yet support remains constrained by stigma and limited resources. This work investigates how interactive technologies, integrated with a wearable heart-rate sensor, can enhance psychological and emotional well-being through the ancient yogic breathing practice, Nadi Shuddhi. We developed two autonomous, adaptive systems - one combining a social robot with a tablet, and one tablet-only, both delivering real-time guidance based on heart-rate variability and breath rate. A study involving 42 university students across 200 sessions revealed both systems significantly increased parasympathetic activation, mindfulness, and calmness while reducing short-term stress and depression symptoms. Compared to a tablet-only condition, the robot’s physical presence led to a significant decrease in breath rate, improved mood, higher competence, and more positive user perceptions, while usability remained comparable, highlighting its potential for supporting youth mental health through social robots with biofeedback.

Transparent and consistent reporting of user studies is essential for advancing scientific knowledge. In human-robot interaction (HRI), studies are often reported incompletely, even in top-tier venues, limiting proper evaluation, replication, and practical application of findings in practice. This study aimed to generate expert consensus on a reporting checklist for HRI user studies and provide a validated tool to improve transparency, reproducibility, and methodological rigor in the field, leading to easier translation of research into practice. A two-round Delphi study was conducted with 34 HRI experts from academia and industry from over 12 countries. An international panel of nine interdisciplinary experts first synthesized a preliminary list of 116 reporting items from the literature. Experts rated the importance of each item and provided qualitative feed- back. Consensus was defined as 70% agreement, and items were iteratively refined through anonymous online surveys. Overall, consensus was achieved on 106 items, encompassing both essential and context-dependent elements in nine domains. The resulting RUSH checklist (Reporting User Studies in Human-Robot Inter- action) provides the first community-endorsed, consensus-based reporting guideline for HRI user studies.

Social robots have shown considerable potential in supporting children’s learning. However, most child-robot interaction (CRI) research has been conducted in supervised laboratory settings, leaving questions about how children engage with robots autonomously in real-world learning environments. We developed a fully autonomous social robotic system and deployed it in summer camp classrooms without direct adult supervision. Our study spanned two months across seven summer camps (each lasting 3–5 days) and examined children’s interactions with a humanoid robot through game-based learning. A total of 100 children from two age groups (6–8 and 9–12 years) engaged with ten educational games tailored to their developmental stages, guided by a QTrobot. Our findings show that autonomous social robots can be integrated into classroom settings without direct supervision and are well accepted by children across age groups, while revealing age-specific differences in perception, engagement, and social behavior, alongside practical challenges related to time, distraction, and technical robustness.

In response to the growing psychological challenges children face, this paper presents "Whispers of Hope", a narrative-centered, webbased game integrated with Augmented Reality (AR), designed to cultivate hope, empathy, and emotional resilience in children aged 7 to 11. Drawing upon Snyder's Hope Theory, the game integrates interactive storytelling, reflective daily affirmations, and curated uplifting news to encourage goal-directed thinking and emotional growth.Through a combination of narrative immersion, socio-emotional learning, and accessible AR technology, the system supports the development of core psychological traits, including self-esteem, optimism, and perspective-taking, that contribute to a hopeful mindset.Preliminary user testing indicates high levels of engagement and perceived emotional relevance, suggesting its promise as a novel, child-centered tool for fostering hope through digital play.

It is crucial to prioritize diversity, equity, and inclusion (DEI) in the development of AI and robotics. Neglecting these factors not only exacerbates existing discrimination and biases, but also continues perpetuating them over time. Despite global awareness, urgent action is needed within the human-robot interaction (HRI) community. This workshop aims to bridge the gap by providing a platform for sharing experiences and research insights related to identifying, addressing, and integrating DEI principles in HRI. Building upon its last few iterations, this year's workshop will actively involve participants in tackling human biases which can be transferred to the robots, aiming to mitigate inequity, recognize and minimize prejudice, and promote inclusion within the field of HRI.

This paper presents a co-designed robot system developed through an 22-month collaboration with Speech Language Pathologists (SLPs) for the use in real-world therapeutic setting. We created persona profiles of SLPs and children with speech and language challenges to inform the development of five game types for two age groups (0-4 and 5-9 years). The system integrates a robot platform with a web-based application that facilitates real-time interaction during therapy sessions. Each game addresses specific therapeutic needs, using the developed child personas as a reference point. Prototype testing with SLPs through role-playing sessions revealed usability insights that led to system refinements, including enhanced robot dialogue, age-appropriate content adjustments, and additional interactive features. The resulting system demonstrates how human-centered design can create robotic system that addresses the practical challenges faced by SLPs and children in therapeutic settings.

Social robots have great potential in supporting individuals’ physical and mental health/wellbeing. While they have been increasingly evaluated in some domains, such as with children with autism, their evaluation has not been as extensive in other areas. We present a systematic review of domains in which social robots have been evaluated specifically in health/wellbeing contexts. We ask which robots have been evaluated, who the participants were, and how participants interacted with the robots. PRISMA guidelines for systematic reviews were followed. Articles with children as participants, using a purely robotic device, and in languages other than English were excluded. A total of 9,362 peer-reviewed articles (up to February 2021) from ACM DL, IEEE Xplore, Scopus, PubMed, and PsychInfo were identified. After applying the inclusion/exclusion criteria 443 articles were included in the review. The majority of studies were conducted at care centers while studies in hospitals/clinics have seen relatively limited attention. In many cases, the social robots were not programmed for specific health-related tasks, limiting their application. We also discuss robots used in real-world settings and propose a “Personal healthcare journey,” which includes different stages of one’s life which could benefit from a social robot, with the goal of increasing long-term adoption of social robots for supporting health/wellbeing.

Public attitudes toward stuttering are rooted in stereotypes and misconceptions, leading to negative reactions and discrimination against individuals who stutter. Previous research highlights the positive impact of educational interventions on people’s attitudes toward stuttering. The potential of social robots as an educational tool in the context of stuttering awareness remains unexplored. In the present study, we investigate whether a social robot can improve public attitudes when giving an interactive presentation on the topic. We compare its impact with a tablet-only condition. Additionally, we differentiate between two robot conditions—one in which the robot imitates stuttering and another where the robot has fluent speech. In the robot conditions, visuals are shown on a tablet. We used a co-design approach and incorporated the perspectives and experiences of two individuals with lived experiences of stuttering into our study design. A user study with 69 participants reveals significant improvements in attitudes across all three conditions, with no significant difference between conditions. However, participants perceived the robot as significantly “warmer,” more “attractive,” and “novel” when compared to the tablet. These findings provide valuable insights into the potential of social robots as intervention techniques for improving attitudes in the field of stuttering.

This paper presents a preliminary design and development of a novel wristband connected to a mobile application to enhance social interaction among children aged 7-12 years who struggle with expressing themselves in group settings. Targeting pedagogical environments, the wristband can facilitate structured group discussions by allowing teachers to set parameters such as time limits via the app. Each child involved in the group discussion would wear an individual wristband, receiving notifications according to the set parameters. The system not only aids teachers in managing inclusive classroom discussions but also benefits parents by providing valuable insights into their child’s social development. Moreover, it helps children build communication skills and confidence during group interactions. This innovative technology has the potential to positively impact educational settings by fostering inclusive happiness and a supportive environment for children’s social and cognitive growth.

Maldaimonic game experiences occur when people engage in personally fulfilling play through egocentric, destructive, and/or exploitative acts. Initial qualitative work verified this orientation and experiential construct for English-speaking Westerners. In this comparative mixed methods study, we explored whether and how maldaimonic game experiences and orientations play out in Japan, an Eastern gaming capital that may have cultural values incongruous with the Western philosophical basis underlying maldaimonia. We present findings anchored to the initial frameworks on maldaimonia in game experiences that show little divergence between the Japanese and US cohorts. We also extend the qualitative findings with quantitative measures on affect, player experience, and the related constructs of hedonia and eudaimonia. We confirm this novel construct for Japan and set the stage for scale development.

Critical scholarship has elevated the problem of gender bias in data sets used to train virtual assistants (VAs). Most work has focused on explicit biases in language, especially against women, girls, femme-identifying people, and genderqueer folk; implicit associations through word embeddings; and limited models of gender and masculinities, especially toxic masculinities, conflation of sex and gender, and a sex/gender binary framing of the masculine as diametric to the feminine. Yet, we must also interrogate how masculinities are “coded” into language and the assumption of “male” as the linguistic default: implicit masculine biases. To this end, we examined two natural language processing (NLP) data sets. We found that when gendered language was present, so were gender biases and especially masculine biases. Moreover, these biases related in nuanced ways to the NLP context. We offer a new dictionary called AVA that covers ambiguous associations between gendered language and the language of VAs.

Maintaining physical, emotional and psychological health is vital for well-being. Social robots have been increasingly used in healthcare to support physical and mental health. Providing appropriate, adaptive and personalised feedback based on the user’s internal states is crucial for effective and engaging human-robot interaction, especially in one-to-one interaction scenarios. In this research, we developed an adaptive and autonomous system, integrating a social robot, a wearable non-intrusive Polar chest sensor and algorithms to guide people in three application scenarios to promote physical, emotional, and psychological well-being. The social robot senses users’ psychophysiological measures such as heart rate and heart-rate variability via the wearable sensor, monitors their stress responses, provides real-time feedback and guides them to perform activities. We detail the system development and a pilot study with fifteen participants to evaluate the system in the three scenarios. The findings suggest that the autonomous system could effectively guide participants through the activities by regulating their stress responses. Participants’ physiological data also support these results. Moreover, the system was well-accepted by its users.

Students with a learning disability (LD) generally require supplementary one-to-one instruction and support to acquire the foundational academic skills learned at school. Because learning is more difficult for students with LD, students can frequently display off-task behaviours to avoid attempting or completing challenging learning tasks. Re-directing students back to their learning task is a frequent strategy used by educators to support students. However, there have been limited studies investigating the use of assistive technology to support student re-direction, specifically in a "real-world" educational setting. In this in situ study, we investigate the impact of integrating socially assistive robot to provide re-direction strategies to students. A social robot, QT, was employed within the existing learning program during one-to-one remedial instruction sessions. The study comprised two phases, "Instruction as usual" (IAU) and "Robot-mediated instructions" (RMI). Both followed the students' one-to-one instructional program where students get personalised learning support from their instructors, except for the RMI phase which included a social robot as a tool. We investigated the impact of the robot on students' on-task behaviours and progress towards learning goals. The results of our mixed method analysis suggest that the robotic intervention supported students in staying on-task and completing their learning goal.

Playing games is an inherent part of children’s lives as it impacts several aspects of their physical and mental development. Technological advances have been manifesting new and exciting avenues of interaction when children play games, ranging from board and card games, to videogames that are played on mobile devices, virtual and augmented reality (VR/AR) headsets, robotic systems, and social agents. These games encompass a wide range of applications aiming to provide educational benefits, promote development, enhance well-being, or simply enjoy leisure time. Along with the fun and excitement, these advancements also bring unique challenges in the game design process due to the inclusion of complex technology, the maximization of the players’ engagement and expectations and interests of the children. The player-centric approach of co-designing games with the target audience has a unique position as it involves creating the games for and with the children, allowing them to act as an equal stakeholder rather than simple users or informants. This half-day workshop aims to expose the researchers to collaborative techniques used in game design to create interactive and playful activities for children that involve contemporary technologies such as AR/VR and social agents.

Research on child-agent interaction is rapidly expanding. It is, therefore, necessary to converge our collective efforts to broaden our understanding and perspectives of how virtual agents, affect and potentially improve the well-being of children. “Children, Robots and Virtual Agents: Present and Future Challenges” follows our International Conference on Social Robotics (ICSR) 2020 workshop on child-robot interactions. In this full-day workshop, we will focus on the unique technical and empirical challenges of designing and conducting child-agent interactions. In light of the current pandemic situation, we will also address the challenges and adaptations of conducting research under the “new normal” to understand how researchers overcome these challenges and what we can learn and keep in the future. We also aim to join the virtual agents and robotics communities to learn from each other and discuss both areas’ common and specific challenges. Our primary goal is to provide an opportunity for an interdisciplinary debate about the present and future of child-agent interactions. We want to bring together researchers, practitioners and pioneers from relevant disciplines and create collaboration opportunities. As part of the workshop, we will have a collaborative activity where our participants will work together and brainstorm about intelligent agents in different time frames (past, present and future). We will also have a panel of experts discussing the topics of this workshop and answering participants questions.

This paper explores how robotic teammates can enhance and promote cooperation in collaborative settings. It presents a user study in which participants engaged with two fully autonomous robotic partners to play a game together, named “For The Record”, a variation of a public goods game. The game is played for a total of five rounds and in each of them, players face a social dilemma: to cooperate i.e., contributing towards the team's goal while compromising individual benefits, or to defect i.e., favouring individual benefits over the team's goal. Each participant collaborates with two robotic partners that adopt opposite strategies to play the game: one of them is an unconditional cooperator (the pro-social robot), and the other is an unconditional defector (the selfish robot). In a between-subjects design, we manipulated which of the two robots criticizes behaviours, which consists of condemning participants when they opt to defect, and it represents either an alignment or a misalignment of words and deeds by the robot. Two main findings should be highlighted (1) the misalignment of words and deeds may affect the level of discomfort perceived on a robotic partner; (2) the perception a human has of a robotic partner that criticizes him is not damaged as long as the robot displays an alignment of words and deeds.

Social robots are being used to create better educational scenarios, thereby fostering children»s learning. In the work presented here, we describe an autonomous social robot that was designed to enhance children»s handwriting skills. Exploiting the benefits of the learning-by-teaching method, the system provides a scenario in which a child acts as a teacher and corrects the handwriting difficulties of the robotic agent. To explore the children»s perception towards this social robot and the effect on their learning, we have conducted a multi-session study with children that compared two contrasting competencies in the robot: 'learning' vs 'non-learning' and presented as two conditions in the study. The results suggest that the children learned more in the learning condition compared with the non-learning condition and their learning gains seem to be affected by their perception of the robot. The results did not lead to any significant differences in the children»s perception of the robot in the first two weeks of interaction. However, by the end of the 4th week, the results changed. The children in the learning condition gave significantly higher writing ability and overall performance scores to the robot compared with the non-learning condition. In addition, the change in the robot»s learning capabilities did not show to affect their perceived intelligence, likability and friendliness towards it.

In this paper, we propose a taxonomy of handwriting errors exhibited by children as a way to build adequate strategies for integration with a co-writing peer. The exploration includes the collection of letters written by children in an initial study, which were then revised in a second study. The second study also analyses the "peer-learning" (PL) and "peer-tutoring" (PT) learning methods in an educational scenario, where a pair of children perform a collaborative writing activity in the presence of a robot facilitator. The data obtained in the first two studies allowed us to create a "taxonomy of handwriting errors". A set of writing errors were selected and implemented in an educational activity for validation. This activity constituted a third study, wherein we systematically induced the errors into a Nao robot's handwriting using the {PT} method - A teacher-child corrects the handwriting errors of the learner-robot. The preliminary results suggest that the children in general showed awareness to the writing errors and were able to perceive the writing abilities of the robot.

Research in education has long established how children mutually influence and support each other's learning trajectories, eventually leading to the development and widespread use of learning methods based on peer activities. In order to explore children's learning behavior in the presence of a robotic facilitator during a collaborative writing activity, we investigated how they assess their peers in two specific group learning situations: peer-tutoring and peer-learning. Our scenario comprises of a pair of children performing a collaborative activity involving the act of writing a word/letter on a tactile tablet. In the peer-tutoring condition, one child acts as the teacher and the other as the learner, while in the peer-learning condition, both children are learners without the attribution of any specific role. Our experiment includes 40 children in total (between 6 and 8 years old) over the two conditions, each time in the presence of a robot facilitator. Our results suggest that the peer-tutoring situation leads to significantly more corrective feedback being provided, as well as the children more disposed to self-disclosure to the robot.

In order to explore the impact of integrating a robot as a facilitator in a collaborative activity, we examined interpersonal distancing of children both with a human adult and a robot facilitator. Our scenario involves two children performing a collaborative learning activity, which included the writing of a word/letter on a tactile tablet. Based on the learning-by-teaching paradigm, one of the children acted as a teacher when the other acted as a learner. Our study involved 40 children between 6 and 8 years old, in two conditions (robot or human facilitator). The results suggest first that the child acting as a teacher feel more responsible when the facilitator is a robot, compared to a human; they show then that the interaction between a (teacher) child and a robot facilitator can be characterized as being a reciprocity-based interaction, whereas a human presence fosters a compensation-based interaction.