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.

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.

Rhythm and articulation are essential for expressive guitar performance. Existing tools provide basic beat cues, whereas beginners often struggle to align with these cues when playing complex techniques, such as strumming and muting. Informed by a formative study with five instructors and grounded in embodied learning theories, we present FretFlow, a haptic vest-based tool that simulates common instructional practices to guide learners through physical interactions like tapping. The key to FretFlow is its design space that maps rhythmic and articulation patterns in various playing techniques to distinct haptic patterns, enabling authoring of haptic scores. FretFlow further dynamically adapts haptic intensity based on learners’ real-time performance accuracy, accompanied by multimodal guidance across haptic, visual, and audio channels. We iteratively refined haptic designs across two rounds with 46 participants, followed by a two-week user study with 20 beginners. Results show that FretFlow improves learners’ rhythmic accuracy and expressive performance.

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.

Large language model (LLM) systems have been shown to stimulate creative thinking among creators, yet empirical research on whether users can seek inspiration in their everyday lives through these technologies is lacking. This paper explores which attributes of LLMs influence inspiration-seeking processes. Focusing on use cases of travel, cooking, and self-care, we interviewed 20 participants as they explored scenarios of these use cases using LLMs. Thematic analysis revealed that the vast data of LLMs inspires users with unexpected ideas, many of which were highly personalized, and inspired participants towards being motivated to act. Participants were also sensitive to the deficiencies of LLMs, and noted how ethical issues associated with these technologies could negatively impact them applying inspirational ideas into practice. We discuss the behavioral patterns of users actively seeking inspiration via LLMs, and provide design opportunities for LLMs that make the inspiration-seeking process more human-centric.

The development of Autonomous Vehicle (AV) has created a novel job, the safety driver, recruited from experienced drivers to supervise and operate AV in numerous driving missions. Safety drivers usually work with non-perfect AV in high-risk real-world traffic environments for road testing tasks. However, this group of workers is under-explored in the HCI community. To fill this gap, we conducted semi-structured interviews with 26 safety drivers. Our results present how safety drivers cope with defective algorithms and shape and calibrate their perceptions while working with AV. We found that, as front-line workers, safety drivers are forced to take risks accumulated from the AV industry upstream and are also confronting restricted self-development in working for AV development. We contribute the first empirical evidence of the lived experience of safety drivers, the first passengers in the development of AV, and also the grassroots workers for AV, which can shed light on future human-AI interaction research.