Children are increasingly interacting with AI systems and robots in everyday contexts, yet how they understand and interpret these systems is not thoroughly studied. Existing approaches to explainable AI largely focus on making model behaviour interpretable, often assuming users who can critically evaluate explanations. However, this assumption does not hold for children, who are still developing their understanding of knowledge, agency, and information sources. In this paper, we argue that explainability and transparency in child–AI interaction should be understood as interactional processes that support children’s construction of meaning about AI systems over time. We introduce a conceptual distinction between interactional transparency—making system behaviour and properties visible—and interactional explainability—supporting the interpretation of that behaviour through reasons and justifications. We further conceptualise explainability as a mechanism for aligning children’s mental models with system capabilities. Building on this framework, we outline key research directions, including co-design with children, developmental approaches to explanation, mechanisms for detecting and correcting misunderstandings, and transparency in interpretation design. Together, these contributions shift the focus of explainability from exposing system internals to supporting understanding, trust calibration, and learning in child–AI and child-robot interaction.

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.

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.

As children face global challenges, creating environments that nurture hope and empower them to shape a fair, transparent future is essential.Conversational AI systems (CAIs) offer opportunities for cognitive and emotional growth, with trust built through transparent, responsive interactions.This workshop offers participants a hands-on opportunity to analyze child-CAI interactions, bringing their own use cases alongside pre-recorded examples from five countries provided by organizers.In collaboration with a diverse group of stakeholders, the focus will be on identifying the human factors that influence trust in child-AI interactions, aiming to advance guidelines for building transparent, trustworthy conversational AI systems.

As social robots become increasingly integrated into educational activities, it is essential to understand how parental AI literacy and perceptions of robot transparency influence children's learning experiences.This Work-in-Progress paper investigates two distinct robot personalities-Maximus (transparent, expressive) and Flexion (non-transparent, straightforward) to explore how variations in transparency affect parental perceptions in educational settings.Six parents participated in the study, evaluating the robots' communication styles and completing AI literacy assessments.Findings suggest that transparent robot behaviours, such as clear explanations and motivational feedback, enhance parental perceptions.This study represents a preliminary exploration; while limited by a small sample size, it offers important early insights into how transparency and parental AI literacy influence trust and acceptance.Future work will address these limitations by incorporating child perspectives, expanding participant diversity, and integrating adaptive robot behaviours.

This work presents REFLEX: Robotic Explanations to FaiLures and Human EXpressions, a comprehensive multimodal dataset capturing human reactions to robot failures and subsequent explanations in collaborative settings. It aims to facilitate research into human-robot interaction dynamics, addressing the need to study reactions to both initial failures and explanations, as well as the evolution of these reactions in long-term interactions. By providing rich, annotated data on human responses to different types of failures, explanation levels, and explanation varying strategies, the dataset contributes to the development of more robust, adaptive, and satisfying robotic systems capable of maintaining positive relationships with human collaborators, even during challenges like repeated failures.

When autonomous mobile robots (AMRs) share space with humans, establishing trust becomes essential for safe, seamless, and effective interaction. Clear communication of a robot's intent is key to building trust by reducing uncertainty and enabling intuitive interaction. This study explores how AMRs can effectively communicate their intentions through simple, intuitive modalities like movement and light, making their actions more predictable and fostering trust. We designed distinct movement cues combined with light patterns to communicate two key intents; yielding (backing off) and making way (prompting humans to move), tested across four different scenarios. To evaluate the clarity and effectiveness of these behaviours, we conducted an online video study analysing qualitative feedback from open-ended responses. Additionally, we collected quantitative data assessing participants' perceptions of the safety and trustworthiness of the robot. Our findings demonstrate a strong correlation between these perceptions and the robot's ability to display socially aware behaviours.

Robots powered by AI and machine learning are increasingly capable of collaboration and social interaction with humans, leading to a demand to develop new approaches to ensure their transparency and explainable behaviour. As explainable AI (XAI) seeks to clarify AI decisions, its integration into physical robots often creates an illusion of explainability—raising questions about whether current approaches truly enhance understanding. The 3rd Workshop on Explainability in Human-Robot Collaboration aims to address the real-world concerns associated with developing explainable and transparent robots through a focused, multi-faceted panel discussion and a series of paper presentations. In this workshop, we will focus on refining when and how explanations should be provided, integrating human communication principles to enhance trust and transparency in human-robot collaboration through both technical and user-centred solutions.

This work aims to interpret human behavior to anticipate potential user confusion when a robot provides explanations for failure, allowing the robot to adapt its explanations for more natural and efficient collaboration. Using a dataset [1] that included facial emotion detection, eye gaze estimation, and gestures from 55 participants in a user study [2], we analyzed how human behavior changed in response to different types of failures and varying explanation levels. Our goal is to assess whether human collaborators are ready to accept less detailed explanations without inducing confusion. We formulate a data-driven predictor to predict human confusion during robot failure explanations. We also propose and evaluate a mechanism, based on the predictor, to adapt the explanation level according to observed human behavior. The promising results from this evaluation indicate the potential of this research in adapting a robot’s explanations for failures to enhance the collaborative experience.

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.

In recent years, the awareness of the academy around responsible research has notably increased. For instance, with advances in machine learning and artificial intelligence, recent efforts have been made to promote ethical, fair, and inclusive AI and robotics. To better understand if and to what extent HRI is incentivizing researchers to engage in responsible research, we conducted an exploratory review of the publishing guidelines for the most popular HRI conference venues. We identified 18 conferences which published at least 7 HRI papers in 2022. From these, we discuss four themes relevant to conducting responsible HRI research in line with the Responsible Research and Innovation framework: ethical and human participant considerations, transparency and reproducibility, accessibility and inclusion, and plagiarism and LLM use. We identify several gaps and room for improvement within HRI regarding responsible research. Finally, we establish a call to action to provoke conversations among HRI researchers about the importance of conducting responsible research within emerging fields like HRI.

Movies and news reports represent for many the first source of interaction with social robots. Congruently, as tools for the dissemination of popular representations of robots, movies can have a direct impact on public perception, acceptance, and discourse about this type of technology. In this article, a content analysis of popular movies and franchises involving (fictional) social robots was conducted (k = 34). With this analysis, we sought to understand a) the main tropes used in movies involving robotic characters, b) the type of human-robot relationships depicted in those movies, and c) how the fictional robots compared with real robots in terms of their abilities. The results suggest that robots tend to be typically depicted in a polarized way that either emphasizes their extreme social abilities or their violent and destructive motives, with the former being slightly more prevalent. As a result, the relations between humans and robots tend to be either friendship or antagonism. Fictional robots are often portrayed as having advanced technical abilities that allow them to navigate multiple complex social settings and engage in different occupations typically performed by humans, in contrast with the abilities held by the most popular commercially available robots we have today.Highlights Movies and news reports represent for many the first source of interaction with social robots.Social robots tend to be portrayed in a very polarized way.Recommendations for future research and robot development are discussed.

Multisensory integration influences emotional perception, as the McGurk effect demonstrates for the communication between humans. Human physiology implicitly links the production of visual features with other modes like the audio channel: Face muscles responsible for a smiling face also stretch the vocal cords that result in a characteristic smiling voice. For artificial agents capable of multimodal expression, this linkage is modeled explicitly. In our studies, we observe the influence of visual and audio channels on the perception of the agents' emotional expression. We created videos of virtual characters and social robots either with matching or mismatching emotional expressions in the audio and visual channels. In two online studies, we measured the agents' perceived valence and arousal. Our results consistently lend support to the ‘emotional McGurk effect' hypothesis, according to which face transmits valence information, and voice transmits arousal. When dealing with dynamic virtual characters, visual information is enough to convey both valence and arousal, and thus audio expressivity need not be congruent. When dealing with robots with fixed facial expressions, however, both visual and audio information need to be present to convey the intended expression.

We present here some lessons learned from observations and applications of child-robot interaction research in diverse ecosystems such as schools, therapy centers, and hospitals, where the interaction was facilitated in real-world circumstances rather than lab settings. Specifically, we use observational results from our reflections on multiple child-robot interaction practices in the wild conducted over a 9-year research period. Using these exploratory studies, we outline some general design considerations and adaptation guidelines for improving the design and implementation of robotic systems in healthcare and education that might lead to more practical, feasible, and ethically sustainable results.

To facilitate human-robot interaction and gain human trust, a robot should recognize and adapt to changes in human behavior. This work documents different human behaviors observed while taking objects from an interactive robot in an experimental study, categorized across two dimensions: pull force applied and handedness. We also present the changes observed in human behavior upon repeated interaction with the robot to take various objects.

Spatial reasoning is one of the malleable skills well-suited to be developed using robotics that not only benefits children in their pursuit of STEM-related topics but also fosters their perspective-taking skills on dimensions beyond spatial skills. In a study involving elementary school children aged 7-10 years old, we investigated the impact of playing a game with physical robots in a physical environment versus playing the same game with virtually embodied robots in a virtual environment. The game focused on developing spatial perspective-taking skills, requiring children to make moves based on the robots’ point of view. We examined how the two environments influenced their experience of fun and learning spatial reasoning skills. We conducted a between-subject user study with 59 participants from 3rd and 4th grades, where they either played with the physical or virtual version of the game. Children in both conditions showed significant improvement in their perspective-taking and spatial orientation test scores. Furthermore, they rated the physical game as more fun compared to the virtual version.

Despite the growing literature on human attitudes toward robots, particularly prosocial behavior, little is known about how robots' perspective-taking, the capacity to perceive and understand the world from other viewpoints, could influence such attitudes and perceptions of the robot. To make robots and AI more autonomous and self-aware, more researchers have focused on developing cognitive skills such as perspective-taking and theory of mind in robots and AI. The present study investigated whether a robot's perspective-taking choices could influence the occurrence and extent of exhibiting prosocial behavior toward the robot. We designed an interaction consisting of a perspective-taking task, where we manipulated how the robot instructs the human to find objects by changing its frame of reference and measured the human's exhibition of prosocial behavior toward the robot. In a between-subject study (N=70), we compared the robot's egocentric and addressee-centric instructions against a control condition, where the robot's instructions were object-centric. Participants' prosocial behavior toward the robot was measured using a voluntary data collection session. Our results imply that the occurrence and extent of prosocial behavior toward the robot were significantly influenced by the robot's visuospatial perspective-taking behavior. Furthermore, we observed, through questionnaire responses, that the robot's choice of perspective-taking could potentially influence the humans' perspective choices, were they to reciprocate the instructions to the robot.

DH Despite significant improvements in robot capabilities, they are likely to fail in human-robot collaborative tasks due to high unpredictability in human environments and varying human expectations. In this work, we explore the role of explanation of failures by a robot in a human-robot collaborative task. We present a user study incorporating common failures in collaborative tasks with human assistance to resolve the failure. In the study, a robot and a human work together to fill a shelf with objects. Upon encountering a failure, the robot explains the failure and the resolution to overcome the failure, either through handovers or humans completing the task. The study is conducted using different levels of robotic explanation based on the failure action, failure cause, and action history, and different strategies in providing the explanation over the course of repeated interaction. Our results show that the success in resolving the failures is not only a function of the level of explanation but also the type of failures. Furthermore, while novice users rate the robot higher overall in terms of their satisfaction with the explanation, their satisfaction is not only a function of the robot’s explanation level at a certain round but also the prior information they received from the robot.

Human-robot collaboration (HRC) relies on smooth and safe interactions. In this paper, we focus on the human-to-robot handover scenario, where the robot acts as a taker. We investigate the feasibility of detecting the intention of a human-to-robot handover action through the analysis of electroencephalogram (EEG) signals. Our study confirms that temporal patterns in EEG signals provide information about motor planning and can be leveraged to predict the likelihood of an individual executing a motor task with an average accuracy of 94.7%. We also suggest the effectiveness of the time-frequency features of EEG signals in the final second prior to the movement for distinguishing between handover action and other actions. Furthermore, we classify human intentions for different tasks based on time-frequency representations of pre-movement EEG signals and achieve an average accuracy of 63.5% for contrasting every two tasks against each other. The result encourages the possibility of using EEG signals to detect human handover intention in HRC tasks.

Spatial understanding and communication are essential skills in human interaction. An adequate understanding of others’ spatial perspectives can increase the quality of the interaction, both perceptually and cognitively. In this paper, we take the first step towards understanding children’s perspective-taking abilities and their tendency to adapt their perspective to a counterpart while completing a task with a robot. The elements used for studying children’s behaviours are the frame of reference and perspective marking, which we evaluated through a task where players needed to compose instructions to guide each other to complete the task. We developed the interaction with an NAO robot and analyzed the children’s instructions and their performance throughout the game. Our initial findings demonstrated that children tend to compose their first instruction by following the principle of least collaborative effort. Children significantly changed and adapted their perspective, i.e. frame of reference and perspective marking to the robot, mainly when the robot failed to follow their instructions correctly. Additionally, results show that children tend to create a mental model of their counterparts and the robot changing that frame of reference might affect their performance or the flow of the interaction.

Recent studies with children have pointed out the importance of spatial thinking as an essential factor in determining later success in STEM-related fields. The current study explores the potential of using embodied activities with robots to aid the development of children’s spatial perspective-taking abilities. This research focuses on evaluating children’s spatial perspective-taking abilities and assessing the potential of the designed activity to practice perspective-taking. The activity design is inspired by the dynamic and mental processes involved in remote-controlled cars and racing games, it is developed with a Cozmo robot, and it includes guiding the robot within the maze by considering the robot’s point of view. We evaluated the activity through a user study with 22 elementary school children between the ages of 8 and 9. The findings showed that children’s performance at different angular disparities was aligned with the previous research in developmental psychology. Additionally, most children made fewer mistakes in guiding the robot as they played more. Finally, while we did not observe any performance improvement in the group of children who had access to the robot’s point of view during the game, we learned new insights about how children perceived seeing the maze through the robot’s eyes.

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.

In this paper, we present a robotic approach to improve the teaching of handwriting using the tangible, haptic-enabled and classroom-friendly Cellulo robots. Our efforts presented here are in line with the philosophy of the Cellulo platform: we aim to create a ready-to-use tool (i.e. a set of robot-assisted activities) to be used for teaching handwriting, one that is to coexist harmoniously with traditional tools and will contribute new added values to the learning process, complementing existing teaching practices.

This paper describes research aimed at supporting children's reading practices using a robot designed to interact with children as their reading companion. We use a learning by teaching scenario in which the robot has a similar or lower reading level compared to children, and needs help and extra practice to develop its reading skills. The interaction is structured with robot reading to the child and sometimes making mistakes as the robot is considered to be in the learning phase. Child corrects the robot by giving it instant feedbacks. To understand what kind of behavior can be more constructive to the interaction especially in helping the child, we evaluated the effect of a deictic gesture, namely pointing on the child's ability to find reading mistakes made by the robot. We designed three types of mistakes corresponding to different levels of reading mastery. We tested our system in a within-subject experiment with 16 children. We split children into a high and low reading proficiency even-though they were all beginners. For the high reading proficiency group, we observed that pointing gestures were beneficial for recognizing some types of mistakes that the robot made. For the earlier stage group of readers pointing were helping to find mistakes that were raised upon a mismatch between text and illustrations. However, surprisingly, for this same group of children, the deictic gestures were disturbing in recognizing mismatches between text and meaning.