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.

With the growing use of digital platforms for professional chat support, waiting time has become a major source of frustration, especially in digital mental health services for children, where many leave the queue before receiving help. This paper explores how an AI-powered interactive feature can improve children’s queue experience by identifying key design and functional requirements and evaluating its impact on engagement, boredom, and perceived waiting time. Through a Design Thinking process, two iterations were conducted: a low-fidelity prototype co-designed with children and experts, and a high-fidelity prototype featuring an LLM-based storytelling agent. The findings underscore the importance of safety, transparency, emotional expression, and personalization in child-AI interactions, showing that storytelling agents can reduce boredom and enhance engagement while children wait.

This paper explores the co-design of an Interactive Digital Narrative (IDN) aimed at supporting the development of executive function (EF) in young children through culturally grounded interactive storytelling. Adopting a user-centered design approach, the research followed a five-phase iterative process involving nine participants from various disciplines, including social sciences, education, and Artificial Intelligence (AI), as well as parents. Three co-design workshops were conducted: (1) to integrate Swedish culture and values into the IDN, (2) to embed EF-stimulating interactions based on parent-child play, and (3) to conceptualise an AI-enhanced support tool for parent-child storytelling and cognitive development. Thematic analysis was employed to extract design insights from the audio recording of the workshops. These insights informed the development of a high-fidelity prototype featuring culturally resonant story elements that can support the development of EF in young children. Additionally, by positioning parents as companions rather than instructors and by embedding EF-supportive design into a joyful storytelling context, this work provides a framework for designing IDNs that are both pedagogically effective and playful for young children. This paper presents initial results of co-design of this IDN.

Accurate recognition of human emotions is a crucial challenge in affective computing and human-robot interaction (HRI). Emotional states play a vital role in shaping behaviors, decisions, and social interactions. However, emotional expressions can be influenced by contextual factors, leading to misinterpretations if context is not considered. Multimodal fusion, combining modalities like facial expressions, speech, and physiological signals, has shown promise in improving affect recognition. This paper proposes a transformer-based multimodal fusion approach that leverages facial thermal data, facial action units, and textual context information for context-aware emotion recognition. We explore modality-specific encoders to learn tailored representations, which are then fused and processed by a shared transformer encoder to capture temporal dependencies and interactions. The proposed method is evaluated on a dataset collected from participants engaged in a tangible tabletop Pacman game designed to induce various affective states. Our results demonstrate improvements from incorporating contextual information and multimodal fusion, achieving 89% F1 score with our full model compared to 65% for action units alone and 30% for thermal data alone.

Human-robot interactions increasingly require adaptive instruction delivery, yet robots struggle to calibrate instruction detail levels without explicit user input. We present a system that automatically modulates instruction granularity using real-time affect detection through multi-modal fusion of thermal imaging, facial expressions, and contextual information. Our transformer-based architecture integrates these signals to enable decisions about instruction delivery based on detected user states. In a between-subjects study (N=40), participants completed assembly tasks under either manual adjustment or automatic adaptation conditions. Results showed significantly fewer manual adjustments in the adaptive condition (0.7 vs 2.0 per session), with comparable user satisfaction across conditions. This work shows the effectiveness of affect-driven adaptive instruction in human-robot interaction, contributing to more responsive robotic interfaces while providing guidelines for balancing automation with user control.

Storytelling is an effective evidence-based practice as an accepted intervention by therapists for the therapy of children with autism spectrum disorder (ASD). Digital storytelling, particularly using smart tangibles, offers a structured, interactive and engaging environment for children with ASD allowing for repetition, offering feedback with visual supports, and giving the child more authority over the learning experience.

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.

This paper describes the first steps of an ongoing participatory design with teachers from Switzerland to co-create Human-Robot Interaction setups for integrating children with migration history. The herein presented phase had two main goals: (i) initially mapping the current issues and the teachers’ strategy when integrating these children, and (ii) understanding teachers’ perceptions regarding social robots for this goal. Results show that teachers we interviewed are already using technology to communicate with immigrant children, not necessarily for inclusion or promote socialisation with their peers, but simply to understand them. Findings also point to a well-defined application of social robots in inclusion activities, even when never seeing or using them, which contradicts previous results in the literature and which gives potential ways to unfold the next steps of the participatory design.

Neurodivergent children with cognitive and communicative difficulties often experience a lower level of social integration in comparison to neurotypical children. Therefore it is crucial to understand social inclusion challenges and address exclusion. Since previous work shows that gamified robotic activities have a high potential to enable inclusive and collaborative environments we propose using robot-mediated games for enhancing social inclusion. In this work, we present the design of a multiplayer tangible Pacman game with three different inter-player interaction modalities: semi-dependent collaborative, dependent collaborative, and competitive. The initial usability evaluation and the observations of the experiments show the benefits of the game for creating collaborative and cooperative practices for the players and thus also potential for social interaction and social inclusion. Importantly, we observe that inter-player interaction design affects the communication between the players and their physical interaction with the game.

The social aspects of therapy and training are important for patients to avoid social isolation and must be considered when designing a platform, especially for home-based rehabilitation. We proposed an online version of the previously proposed tangible Pacman game for upper limb training with haptic-enabled tangible Cellulo robots. Our main objective is to enhance motivation and engagement through social integration and also to form a gamified multiplayer rehabilitation at a distance. Thus, allowing relatives, children, and friends to connect and play with their loved ones while also helping them with their training from anywhere in the world. As well as connecting therapists to their patients through haptically linking capabilities. This is especially relevant when there are social distancing measures which might isolate the elderly population, a majority of all rehabilitation patients.

Under the umbrella of smart toys, a myriad of interactive systems have addressed a variety of scenarios considering entertainment, education, sustainability, social and environmental learning through play. Tangibles and small toy robots prevail; but interaction technology based on wearables, conversational interfaces or augmented reality may also be part of this landscape to implement smart toys that are both social and/or connected. In the second edition of this workshop, we set a special focus on challenges associated to opening smart toys to different degrees of ”connectedness”, and reflecting on concerns regarding data privacy and ethics when smart toys are reframed to function in a connected way. After the pandemic, social isolation forced us to change the way we interact with each other. We are convinced that smart technologies can open new avenues for promoting social connectedness enabling more interactive playing and learning activities.

Gamified rehabilitation tackles the problem of keeping patients engaged in, and motivated to do physical rehabilitation to improve its efficacy. However, with respect to standard rehabilitation, patients are freer to move about and may compensate their motion difficulties with parasite movements, which would greatly reduce the efficacy of the rehabilitation. To identify and characterize compensatory motions, we collected and analyzed video data of people playing the "tangible Pacman" game (an upper-limb rehabilitation game in which a patient moves a semi-passive robot, the "Pacman", on a map to collect 6 apples, while being chased by one or two autonomous robots, the "ghosts"). Participants include 10 healthy elderly adults and 10 chronic stroke patients, who played multiple runs of the game, with different sized maps and various game configurations. By analyzing the video recordings we successfully identified higher shoulder and torso lateral tilt compensation in stroke patients and developed a proof-of-concept compensatory motion detection system which relies on a wearable Inertial Measurement Unit and ROS to provide in-game, real-time visual feedback on compensation.

Geometry and handwriting rely heavily on the visual representation of basic shapes. It can become challenging for students with visual impairments to perceive these shapes and understand complex spatial constructs. For instance, knowing how to draw is highly dependent on spatial and temporal components, which are often inaccessible to children with visual impairments. Hand-held robots, such as the Cellulo robots, open unique opportunities to teach drawing and writing through haptic feedback. In this paper, we investigate how these tangible robots could support inclusive, collaborative learning activities, particularly for children with visual impairments. We conducted a user study with 20 pupils with and without visual impairments, where they engaged in multiple drawing activities with tangible robots. We contribute novel insights on the design of children-robot interaction, learning shapes and letters, children engagement, and responses in a collaborative scenario that address the challenges of inclusive learning.

A key element for the success of any game is its ability to produce a different experience at each round, thus keeping the player engagement high. This is particularly important for those games that also have a serious objective, such as gamified rehabilitation systems, aiming at encouraging patients in performing home rehabilitation exercises. In all cases, a game element which is typically static is the workspace, i.e. the "floor" upon which the game takes place. This is especially true for robot-assisted rehabilitation games, where the workspace must satisfy the requirements given by the robot's locomotion and localization systems, as well as the patient's exercise motion requirements.In this article we present a simple yet effective solution for designing dynamic and customizable tangible workspaces, which relies on hexagonal tiles and our previously proposed Cellulo localization system. These "hextiles" can be easily tangibly rearranged at each game round to yield a desired workspace shape and configuration, allowing tabletop mobile robots to move continuously within each new workspace. We ground our solution in the context of robot-assisted rehabilitation, where high adaptability is crucial for the efficacy of the solution, and propose a dynamic extension of our "tangible Pacman" rehabilitation game.Experiments show that the proposed solution allows for adaptation in range of motions, exercise types, physical and cognitive difficulty, besides reducing repetitiveness.

Exercising is strongly recommended for prevention and treatment of pathologies with high prevalence such as cardiovascular diseases, cancer, and diabetes. The World Health Organization (WHO) states that insufficient physical activity is one of the leading risk factors for death worldwide. The decrease of physical activity in our society is not just an individual problem but it is influenced by a variety of environmental and social factors. Hence, it is important to target this issue from a multi-perspective and interdisciplinary point of view. This full-day workshop will offer a forum for researchers from a variety of backgrounds to discuss the potentials and limitations of using social robots to promote physical activity.

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.

Rehabilitation aims to ameliorate deficits in motor control via intensive practice with the affected limb. Current strategies, such as one-on-one therapy done in rehabilitation centers, have limitations such as treatment frequency and intensity, cost and requirement of mobility. Thus, a promising strategy is home-based therapy that includes task specific exercises. However, traditional rehabilitation tasks may frustrate the patient due to their repetitive nature and may result in lack of motivation and poor rehabilitation. In this article, we propose the design and verification of an effective upper extremity rehabilitation game with a tangible robotic platform named Cellulo as a novel solution to these issues. We first describe the process of determining the design rationales to tune speed, accuracy and challenge. Then we detail our iterative participatory design process and test sessions conducted with the help of stroke, brachial plexus and cerebral palsy patients (18 in total) and 7 therapists in 4 different therapy centers. We present the initial quantitative results, which support several aspects of our design rationales and conclude with our future study plans.

The main contribution of this study is the design and implementation of an autonomous human robot interaction system to engage children in performing several physical exercise motions by providing real-time feedback and guidance. The system is designed after several preliminary experiments with children and exercise coaches. In order to test the feasibility and the effectiveness of the exercise system across a variety of performance and evaluation measures, an experimental study was conducted with 19 healthy children. The results of the study validate the effectiveness of the system in motivating and helping children to complete physical exercises. The children engaged in physical exercise throughout the interaction sessions and rated the interaction highly in terms of enjoyableness, and rated the robot exercise coach highly in terms of social attraction, social presence, and companionship via a questionnaire answered after each session.

The purpose of this study is fusing play-like child robot interaction with physiotherapy in order to achieve upper arm rehabilitation by motivating the child. The proposed system is not intended to substitute for the physiotherapist, but to assist them in their therapeutic tasks by encouraging the child's participation in the activity. Recognizing the imitation performance of the child and supporting him/her with feedback for drawing the child's attention and motivating the child to imitate the robot is crucial. This study concentrates on automatically evaluating the upper body actions of the child during an imitation based physical therapy. For quantifying the performance of the child, two measures were considered: Range of Motion (RoM) and Dynamic Time Warping (DTW) distance. In our initial experiments, eight healthy children were asked to stand in front of a Kinect sensor and to mimic the actions of the humanoid robot Nao, which consist of shoulder abduction, shoulder vertical flexion&extension and elbow flexion. The proposed evaluation measure is verified as a reliable measurement according to Intraclass Correlation Coefficient (ICC) through comparison with evaluations of five physiotherapists as ground truth. The degree of consistency among our ratings and the physiotherapist ratings is between %76 and %96 for different motions.