The workshop aims to bring together new interdisciplinary approaches to socio-emotional learning, exploring how interactive artifacts offer the possibility of translating emotions into tangible and physical experiences. By engaging participants in project-based prototyping activities, the workshop will investigate how tangible artifacts can foster interdisciplinary discussion, as well as define concrete principles to transform personal emotions into accessible and shared experiences.

Rett Syndrome is a rare neurodevelopmental disorder associated with severe intellectual and motor impairments. Among its core symptoms, children with Rett progressively lose purposeful hand use, limiting their ability to reach for, touch, and manipulate objects in the surrounding environment. Early intervention is therefore important to help maintain opportunities for functional hand use through repeated and motivating activities. To support this goal, we developed Pufff, a touch-sensitive interactive toy that provides visual feedback during touch-based interaction. Using signals acquired from a six-patch capacitive sensing array and a lightweight machine learning model, the system distinguishes three types of touch and triggers different responses accordingly. This interaction paradigm is intended to support future therapist-led activities aimed at encouraging purposeful hand use through play, while also enabling basic monitoring of touch interaction patterns. This paper presents an early implementation of the toy and demonstrates the technical feasibility of the approach.

In this paper, we provide an in-depth description of Echo, an interactive, smart toy, designed to encourage the symbolic play in autistic children. Symbolic play is a key competence in the cognitive development, and it is observed for instance when a child uses an object to represent other objects. The toy, characterised by an abstract shape, can autonomously recognise how the user handles it during play (e.g. mimicking the movements of a car, an aeroplane, a frog in this study) and produce, as rewarding feedback, distinctive sensory outputs such as colours and sounds, according to the detected imaginary toy. The categorisation, achieved with an average accuracy of 90.1%, is carried out in real-time by a Machine Learning (ML) model, which runs in the device's embedded electronics; the ML model was trained using the data by an onboard IMU sensor, which provides information about the toy movements (i.e. tilt, angular velocity, acceleration). The paper provides an overview of the toy design and functionalities, and presents the results of a feasibility test with neurotypical adult participants. Finally, it proposes Echo as a potential tool to encourage, through the AI-mediated sensory feedback, this pivotal competence; symbolic play is in fact often impaired or atypical in autistic children, and new technologies – such as Echo – can provide interesting novel opportunities for neurodevelopmental therapists and researchers.