Current research on the impact of Generative Artificial Intelligence (GenAI) on adolescent development yields mixed results, and parents are left to navigate this emerging technology without clear support and knowledge.A missing step toward effective digital parenting is understanding parents' concerns and beliefs.Therefore, this preliminary study explores parental perspectives on the use and impact of GenAI on adolescents aged 13 to 17.We conducted a survey with 𝑁 = 159 parents from 19 countries across Europe, Asia, Africa, and the Americas.Findings suggest that most parents are unaware of how their children use GenAI and feel disconnected from them on this topic.Importantly, while parents recognise the opportunities and risks of GenAI, their views vary based on their own familiarity with the technology, with those who use it regularly being significantly more optimistic about its impact on adolescents' development.These results highlight a gap in digital parenting when it comes to adolescents' use of GenAI, underscoring the need for a systematic approach to parental support.Future work will expand the survey into a larger-scale study and incorporate adolescents' perspectives.

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.

Understanding what constitutes children’s well-being in digital environments is fundamental for designing technology that supports children’s development while minimizing emerging risks. This is even more urgent with the rapid advances in Artificial Intelligence and the radical transformations in children’s everyday activities, play, and learning. With this workshop, we aim to co-create an agenda for future actions by mapping the current state-of-the-art research about children’s well-being, identifying policy initiatives and relevant stakeholders that come into play, and reflecting on existing good practices and proposing new ones for digital technology that promotes children’s well-being with a special focus on children from under-represented groups.

This large-scale study assesses the impact of human oversight on countering discrimination in AI-aided decision-making for sensitive tasks. It follows a mixed method approach, including a quantitative experiment with Human Resources (HR) and banking professionals in Italy and Germany (N=1411), and qualitative analyses through interviews and workshops with participants and fair AI experts. The results show that human overseers were equally likely to follow advice from a fair AI as from a generic, discriminatory AI. Human oversight does not prevent discrimination by the generic AI. Fair AI reduces gender bias but not nationality bias. Participants’ choices are neither more nor less responsive to their preferences when using an AI or when left on their own. Interviews and workshops with participants highlight individual, organizational and societal biases. In case of conflict, participants prioritize their company’s interests over their own view of fairness. Participants also ask for better guidance on when to override AI recommendations. Fair AI experts stress the need for a comprehensive approach when designing oversight systems. Both technological and social aspects should be taken into consideration to ensure fairness.

New emerging technologies powered by Artificial Intelligence (AI) have the potential to disruptively transform our societies for the better. In particular, data-driven learning approaches (i.e., Machine Learning (ML)) have been a true revolution in the advancement of multiple technologies in various application domains. But at the same time there is growing concern about certain intrinsic characteristics of these methodologies that carry potential risks to both safety and fundamental rights. Although there are mechanisms in the adoption process to minimize these risks (e.g., safety regulations), these do not exclude the possibility of harm occurring, and if this happens, victims should be able to seek compensation. Liability regimes will therefore play a key role in ensuring basic protection for victims using or interacting with these systems. However, the same characteristics that make AI systems inherently risky, such as lack of causality, opacity, unpredictability or their self and continuous learning capabilities, may lead to considerable difficulties when it comes to proving causation. This paper presents three case studies, as well as the methodology to reach them, that illustrate these difficulties. Specifically, we address the cases of cleaning robots, delivery drones and robots in education. The outcome of the proposed analysis suggests the need to revise liability regimes to alleviate the burden of proof on victims in cases involving AI technologies. This article appears in the AI & Society track.

During the learning process, a child develops a mental representation of the task he or she is learning. A Machine Learning algorithm develops also a latent representation of the task it learns. We investigate the development of the knowledge construction of an artificial agent through the analysis of its behavior, i.e., its sequences of moves while learning to perform the Tower of Hanoï (TOH) task. The TOH is a well-known task in experimental contexts to study the problem-solving processes and one of the fundamental processes of children's knowledge construction about their world. We position ourselves in the field of explainable reinforcement learning for developmental robotics, at the crossroads of cognitive modeling and explainable AI. Our main contribution proposes a 3-step methodology named Implicit Knowledge Extraction with eXplainable Artificial Intelligence (IKE-XAI) to extract the implicit knowledge, in form of an automaton, encoded by an artificial agent during its learning. We showcase this technique to solve and explain the TOH task when researchers have only access to moves that represent observational behavior as in human-machine interaction. Therefore, to extract the agent acquired knowledge at different stages of its training, our approach combines: first, a Q-learning agent that learns to perform the TOH task; second, a trained recurrent neural network that encodes an implicit representation of the TOH task; and third, an XAI process using a post-hoc implicit rule extraction algorithm to extract finite state automata. We propose using graph representations as visual and explicit explanations of the behavior of the Q-learning agent. Our experiments show that the IKE-XAI approach helps understanding the development of the Q-learning agent behavior by providing a global explanation of its knowledge evolution during learning. IKE-XAI also allows researchers to identify the agent's Aha! moment by determining from what moment the knowledge representation stabilizes and the agent no longer learns.

The design of child-centred, intelligent and collaborative robots is a challenging endeavour, which requires to understand how the implemented robot behaviours and collaboration paradigms affect children's perception about the robot. This paper presents the results of a set of semi-structured interviews of N=81, 5 to 8 years old children who previously interacted in pairs with a robot in the context of a problem-solving task. We manipulated two different factors of the robot behaviour: cognitive reliability in logic game movements (optimal vs sub-optimal) and expressivity in the communication (expressive vs neutral) and we assigned the children in one of the four conditions. At post-intervention interviews, we examined children's perceptions on the robot's attributions, collaboration and social role. Results indicate that a robot's cognitive reliability shapes the helping relationship between the children and the robot, while the robot's expressivity impacts children perception of the robot supportive ability and friendship. Finally, results also indicate that, even if children interact in pairs with the robot, their perceptions about it remain individual, although a good collective task-performance seems to empower children perception of the robot in terms of friendship and reliability.

This paper describes a cross-cultural pilot study on children’s perceptions of fairness in robot-related scenarios with children in Japan (N = 20) and Uganda (N = 24). We used storytelling to facilitate children’s narratives on fairness and to identify areas of alignment and disconnect. Initial results indicate that while both groups referred to similar aspects of fairness, namely psychological, physical and systemic, children in Tokyo focused more on psychological and mental aspects while children in Uganda emphasised on physical and material aspects. Both groups increased their emphasis on mental aspects in robot-related scenarios. All children expressed their interest to further explore fairness and unfairness as experienced by children with different cultural backgrounds and the need for inter-group contact. The results of this study will contribute to the first phase of a study with robots and children in relation to child’s fundamental rights and to the dialogue about the requirements for fairness in robot development by highlighting the importance of considering children’s perspectives especially those of typically under-represented cultural groups.

Human collaboration is more likely to lead to cognitive growth when all group-members are actively involved in the collaborative process. However, there are cases that intragroup relationships need support. In this paper, we present an autonomous robotic system designed to interact with a pair of children in a problem-solving setting, aiming to understand how the robot behaviour impacts the group-members’ social dynamics. We developed an autonomous system with the Haru robot which we evaluated with an experimental study with 5-8yo children (N =84) to test the impact of the robot’s cognitive reliability and social positioning on human-to-human social dynamics, task performance and help-seeking behaviour. All participants took part in a baseline session (without the robot), an intervention (with the robot in a turn-taking setting) and an evaluation session (with a robot in a voluntary interaction setting). Results indicate that children who interacted with the reliable robot had a better task performance but children who interacted with the unreliable robot exhibited more task-related social interactions. Based on the results, we propose an interaction design concept which combines the set of the evaluated robot behaviours for an adaptive targeted support of child-robot teaming.

Ensuring trust in human-robot interaction (HRI) is considered essential for widespread use of robots in society and everyday life. While the majority of studies use game-based and high-risk scenarios with low familiarity to gain a deeper understanding of human trust in robots, scenarios with more subtle trust violations that could happen in everyday life situations are less often considered. In this paper, we present a theory-driven approach to studying the situated trust in HRI by focusing on the experience of vulnerability. Focusing on vulnerability not only challenges previous work on trust in HRI from a theoretical perspective, but is also useful for guiding empirical investigations. As a first proof-of-concept study, we conducted an interactive online survey that demonstrates that it is possible to measure human experience of vulnerability in the ordinary, mundane, and familiar situation of clothes shopping. We conclude that the inclusion of subtle trust violation scenarios occurring in the everyday life situation of clothes shopping enables a better understanding of situated trust in HRI, which is of special importance when considering more near-future applications of robots.

Interactive learning technologies, such as robots, increasingly find their way into schools. However, more research is needed to see how children might work with such systems in the future. This paper presents the unsupervised, four month deployment of a Robot-Extended Computer Assisted Learning (RECAL) system with 61 children working in their own classroom. Using automatically collected quantitative data we discuss how their usage patterns and self-regulated learning process developed throughout the study.

This article presents a study in which we explored the effect of a social robot on the explanatory behavior of children (aged 6--10) while working on an inquiry learning task. In a comparative experiment, we offered children either a baseline Computer Aided Learning (CAL) system or the same CAL system that was supplemented with a social robot to verbally explain their thoughts to. Results indicate that when children made observations in an inquiry learning context, the robot was better able to trigger elaborate explanatory behavior. First, this is shown by a longer duration of explanatory utterances by children who worked with the robot compared to the baseline CAL system. Second, a content analysis of the explanations indicated that children who worked with the robot included more relevant utterances about the task in their explanation. Third, the content analysis shows that children made more logical associations between relevant facets in their explanations when they explained to a robot compared to a baseline CAL system. These results show that social robots that are used as extensions to CAL systems may be beneficial for triggering explanatory behavior in children, which is associated with deeper learning.

Expressivity - the use of multiple, non-verbal, modalities to convey or augment the communication of internal states and intentions - is a core component of human social interactions. Studying expressivity in contexts of artificial agents has led to explicit considerations of how robots can leverage these abilities in sustained social interactions. Research on this covers aspects such as animation, robot design, mechanics, as well as cognitive science and developmental psychology. This workshop provides a forum for scientists from diverse disciplines to come together and advance the state of the art in developing expressive robots. Participants will discuss points of methodological opportunities and limitations, to develop a shared vision for next steps in expressive social robots.

Robotics is a multidisciplinary and highly innovative field. Recently, multiple and often minimally connected sub-communities of child-robot interaction have started to emerge, variously focusing on the design issues, engineering, and applications of robotic platforms and toolkits. Despite increasing public interest in robots, including robots for children, child-robot interaction research remains highly fragmented and lacks regular cross-disciplinary venues for discussion and dissemination. This workshop will bring together researchers with diverse scientific backgrounds. It will serve as a venue in which to reflect on the current circumstances in which child-robot research is conducted, articulate emerging and "near future" challenges, and discuss actions and tools with which to meet those challenges and consolidate the field.

One of the major reasons behind traffic accidents is misinterpretation among road users. Self-driving vehicles are expected to reduce these accidents, given that they are designed with all road users in mind. Recently, research on the design of vehicle-pedestrian communication has emerged, but to our knowledge, there is no research published that investigates the design of interfaces for intent communication towards child pedestrians. This paper reports the initial steps towards the examination of children's views and understandings about the appearance and intention communication of self-driving vehicles. It adopts a design inclusive methodological approach for the development of a prototype for the communication of two basic intentions: "I am going to stop" and "I am going to proceed". The initial results indicate children's need to be aware about the autonomy of the vehicle and the use of their previous experience with traffic signs for the interpretation of communicative signs of the vehicle.

Many researchers have started to explore natural interaction scenarios for children. No matter if these children are normally developing or have special needs, evaluating Child-Robot Interaction (CRI) is a challenge. To find methods that work well and provide reliable data is difficult, for example because commonly used methods such as questionnaires do not work well particularly with younger children. Previous research has shown that children need support in expressing how they feel about technology. Given this, researchers often choose time-consuming behavioral measures from observations to evaluate CRI. However, these are not necessarily comparable between studies and robots.

This review examines recent methodological approaches for the evaluation of child-robot interaction in learning settings. The main aims are to map existing work from a user-centered perspective, to identify possible trends related to evaluation methods for child-robot interaction, and to discuss potential future directions. We present a systematic review of existing studies, which have been thematically organized based on their research objectives. We then examine the evaluation methods that were used in these studies and we propose a conceptual framework based on the one hand on the themes that emerged, namely the social interaction between the child and the robot, the social acceptance, possible emotional interactions, the learning process and the learning outcome, and on the other hand on the corresponding measures. These methods have been considered in relation with the age ranges of the children, because of the relationship of their cognitive level to the choice of a developmentally appropriate evaluation method. We use this framework to highlight current trends and needs for the field and to contextualize the methodological directions for child-robot interaction. Finally, we discuss the challenges and limitations of the current methodological approaches as well as possible future directions for the evaluation methods of child-robot interaction in learning settings.

Since children (5-9 years old) are still developing their emotional and social skills, their social interactional behaviors in small groups might differ from adults' interactional behaviors. In order to develop a robot that is able to support children performing collaborative tasks in small groups, it is necessary to gain a better understanding of how children interact with each other. We were interested in investigating vocal turn-taking patterns as we expect these to reveal relations to collaborative and conflict behaviors, especially with children behaviors as previous literature suggests. To that end, we collected an audiovisual corpus of children performing collaborative tasks together in groups of three. Through automatic turn-taking analyses, our results showed that speaker changes with overlaps are more common than without overlaps and children seemed to show smoother turn-taking patterns, i.e., less frequent and longer lasting speaker changes, during collaborative than conflict behaviors.