Personal data and digital technologies increasingly shape children's daily experiences, but younger children are seldom presented with opportunities to reflect in-depth about their perspectives on personal data, beyond simple warnings about not sharing information online. We present Grasping Data, a set of portable activities aimed at helping children explore what counts as personal data, why some data feel more personal than others, and how meaning changes between audiences and purposes. The demo combines three linked tangible activities: a teddy-bear proxy for card sorting-based discussion, a wooden tangible prototype for self-generated activity data, and a set of shape-changing multi-material objects for embodied interpretation of personal data. For the IDC community, the demo contributes a 10-minute encounter highlighting concrete, research-based, low-tech methods to discuss personal data with young children across formal and informal learning settings.

Data physicalization is a promising approach for empowering children to understand and enjoy their own data. However, it relies on embodied metaphors to convey information effectively. This paper explores how to elicit children’s embodied experiences using a set of shape-changing objects that can inform the design of dynamic physicalizations. We propose a set of auxetic metamaterials, which can bend, twist, scale and shear. Following principles of tangible interaction, we conducted a study with 59 children who participated in four movement-based games before being introduced to the collection of shape-changing tangibles. Children expressed metaphors based on these activities related to concepts such as containers, rhythm, resistance, and semantic analogies, which we categorised into embodied schemas. Our findings reveal that characteristics of the shape-changing tangibles aid children in connecting their bodily experiences to dynamic transformations. Translating these insights into idea sketches, we outline how to tailor these affordable shape-changing mechanisms into usable prototypes.

The task of supporting a human operator to understand generated plans, and to explore the plan space, are important problems in automated planning. In this work, we consider the problem of plan explainability and plan space exploration in underwater autonomous vehicle missions. In this context, concepts that are useful for querying the system, such as distance and duration, will not necessarily map directly onto components of the planning model, such as actions. To overcome this difficulty, we focus on an important substructure of these problems: the multi-agent spatial-temporal (MAST) structure. Using this structure, we define a collection of model extensions, which include additional concepts relevant to the MAST structure. We then consider the problem of user-guided plan space exploration, and identify useful query types in this domain, including user queries based on numeric functions. These queries can make use of the extended model, allowing the user to directly reference the new concepts. In an empirical study, we demonstrate the use of the new structure within queries, and compare the new query types in our target domain, and in benchmark domains with the MAST structure. Finally, we report on a qualitative user study, where we investigate the use of these new structural concepts in underwater autonomous vehicle scenarios. Our study indicates that the extended concepts can be used in user queries and agent responses, enabling the user to better communicate their intent in shaping mission objectives, and supporting explanations with more relevant information.

Unlike any previous generation, children's lives are now highly datafied, tracked, and digitally monitored.They are increasingly vulnerable to data collection from seemingly innocuous toys and devices with cameras, sensors, voice recognition, and geolocation.Yet, children typically lack awareness or control over these data exchanges, as consent is usually given by adult caregivers (who may also lack data literacy).Despite regulatory improvements, interdisciplinary approaches remain crucial to empowering children to understand, value, and manage their personal data.How can the IDC community involve children in the design and use of their personal data?This workshop unites experts to explore current practices.1 CCS Concepts• Security and privacy → Social aspects of security and privacy; • Human-centered computing → Interaction design 1 This workshop merged with "Towards a Research Agenda for Including Children and their Care Ecosystems in HCI" to run a joint workshop: Grasping Data Together: Interdisciplinary Agendas for Engaging Children and their Care Ecosystems with their personal data

In autonomous vehicle mission planning, supporting human operators to understand and influence the decision-making process is crucial for building the operator’s trust and establishing effective collaboration. However, it has been observed that human and agent representations will typically not align. As a consequence, concepts that are useful for effective human-agent communication, will not necessarily feature in the agent’s representation. Focusing on specific spatial-temporal concepts, we define automatic model extensions, which can introduce these additional concepts. We report on a qualitative user study, where we investigate the use of these new structural concepts in underwater autonomous vehicle scenarios. Our study indicates that the extended concepts can be used in user queries and agent responses, enabling the user to better communicate their intent in shaping mission objectives, and supporting explanations with more relevant information.

The software submission presents a fully working artificial cognition model, which controls a NAO social robot. The model was specifically designed to control a socio-affective companion robot for use in a medical setting. It was deployed using embedded hardware: a Raspberry Pi 4B and a Jetson Nano Board, and an external RGB-D camera. Based on the ROS operating system, this software package includes components for social signal processing, behaviour selection, affective behaviour rendering, and a web-based user interface. The robot's behaviours are selected by a planning system, which generates the robot's behaviours based on the state of the interaction, the progress of the medical procedure, and the user's affective state. The system has been tested in simulated environments and is currently being used in two clinics to perform a usability test and will subsequently be used to carry out a series of clinical trials