AI systems are increasingly shaping everyday life through data capture and reasoning processes that often remain difficult for children to grasp. This paper presents ShadowAI, a playful, metaphorical, and creative system designed to support children’s AI literacy in informal learning environments. It comprises the Shadow Capture Box and ShadowStories. Drawing on the metaphor of AI as a “ghost” that hides within the shadow of data, and on the benefits of creative making that makes AI concepts tangible, engaging, and personally meaningful. ShadowAI invites children to create play-doh artefacts whose shadows are captured and interpreted, generating images, narratives, and staged visualisations of AI reasoning. Rather than offering conceptual explanations of AI, the system uses ambiguity, family interaction, and discussion to help children critically engage with how AI observes, interprets, and reasons from partial input, while reflecting on data and agency.

Data has emerged as one of the most powerful resources of the 21st century, yet the processes through which it is collected, transformed, and exploited remain opaque to the general public. While individuals continuously generate data through everyday interactions, they often lack understanding of how data is collected, by whom, and for what purposes. In this poster paper, we present the development process of DataMorph, an artistic provotype inspired by the metaphor of a data factory. It employs data physicalisation to render invisible processes and abstract data flows visible through a physical shape-changing form. Through this design provocation, we aim to foster critical reflection, public dialogue, and engagement with data practices, agency, and power. Lastly, we discuss various contexts in which DataMorph could be deployed.

AI systems increasingly shape everyday life through data capture and reasoning processes that often remain difficult to grasp, existing as ghosts within the shadows of data. This demo paper presents ShadowAI, an experiential AI system comprising the Shadow Capture Box and ShadowStories. Participants craft play-doh artefacts whose shadows are captured as silhouettes, and interpreted by GPT-4o which is embedded within ShadowStories through a structured prompt pipeline. This includes Shadow Parsing, Concept Proposal, Image Prompt, Story Outline, and Final Story, which are displayed as staged reasoning visualisations alongside DALL·E 3 illustrations and GPT-4o-generated narratives. Rather than relying on technical explanation alone, ShadowAI uses shadow play, creative making, and intentional ambiguity to support experiential encounters with how AI interprets. The resulting outputs may be surprising, erroneous, or absurd, prompting curiosity, shared reflection, and critical discussion about AI as an ambiguous and fallible simulation assembled from human-created data.

Somaditation is an embodied meditation experience that combines a wearable glove with a VR world of responsive sound sculptures. Drawing on soma design and metaphor of singing bowl in existing sound meditation practice, the glove maps finger pressure, hand flexion and rotation to visual, auditory and vibrotactile changes in the scene. This interaction turns hand movement into the primary means of providing a somatic interpretive lens on traditional guided meditation, visualising the internal dynamics of the meditative experience through external multisensory feedback. We present the design and implementation of Somaditation, which explores how movement-based interaction could deepen bodily awareness and enhance well-being. It fosters emerging HCI conversations on establishing meditative sensory ritual by demonstrating how multisensory embodiment can extend and enrich meditation practices.