arXiv subjects

Janet Yi-Ching Huang

Publications and source records attributed to Janet Yi-Ching Huang.

9 recordsLinked to original sources

arXiv2026

Workmanship of Learning: Embedding Craftsmanship Values in AI-Integrated Educational Tools

Generative AI's emphasis on automation and efficiency challenges design education, where learning is grounded in exploration, reflection, and responsibility. This work introduces AI Craftsmanship, a value-oriented framework drawing on craftsmanship traditions that emphasize risk, rhythm, and care as central to learning through making. Through a Research through Design (RtD) approach, we designed an AI-integrated creative coding tool embedding these values into interactions and interface rather than outcomes. The tool supports designers learning generative pattern-making with p5.js by constraining AI, encouraging iterative experimentation, and foregrounding reflection. We studied the tool with five design practitioners through one-hour sessions and semi-structured interviews. Findings show craft values manifest unevenly: risk and rhythm shape early sense-making, while care emerges through reflective practices. Emergent values -- such as aesthetic judgment and confidence -- also motivated learning. AI Craftsmanship mediates values, tools, and materials, offering a value-driven perspective on designing AI systems for reflective, responsible, craft-informed learning in design education.

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arXiv2026

Generative Horcrux: Designing AI Carriers for Afterlife Selves

As generative AI technologies rapidly advance, AI agents are gaining the ability not only to collect data and perform tasks but also to respond to environments and evolve over time. This shift opens new possibilities for reimagining digital legacy - raising critical questions about how we remember, commemorate, and interact with the traces of the deceased. The forms of these AI agents are particularly important, as they act as vessels for digital legacies - much like urns for ashes. We will ask: What kinds of devices or representations would we want to store our digital selves or legacies in? How do we envision future generations interacting with these forms? The question is not only about the function of these agents or the object's role as a storage vessel but also the meaning it carries, the memories it preserves, and its connection to the extended notion of our "Generative Horcrux." This three-hour in-person workshop invites design practitioners and researchers from diverse backgrounds to explore the emerging landscape of generative AI agent-based digital legacy. This workshop uses fiction and hands on prototyping to explore how AI agents might reconfigure memory, identity, and posthumous presence in future sociotechnical worlds. We anticipate that this session will foster interdisciplinary dialogue and contribute conceptually and methodologically to HCI, design research, and AI ethics.

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arXiv2026

Memory Printer: Exploring Everyday Reminiscing by Combining Slow Design with Generative AI-based Image Creation

Generative Artificial Intelligence (GAI) offers new opportunities for reconstructing these unrecorded memory scenes, yet existing web-based tools undermine users' sense of agency through disengaging and unpredictable interactions. In this work, we advance three design arguments about how slow, tangible interaction can reshape human-AI relationships by making temporality, embodied agency, and generative processes experientially legible. We instantiate these arguments by presenting Memory Printer, a tangible design that combines silk-screen printing metaphors with text-to-image generation. The design features layered reconstruction that decomposes image generation into incremental steps, a physical wooden scraper enabling embodied control over image revelation, and built-in printing that produces tangible photos. We examine these arguments through a comparative study with 24 participants, exploring how participants engage with, interpret, and respond to this interaction stance. The study surfaces both opportunities -- such as vivid memory evocation, heightened sense of control, and creative exploration -- and critical tensions, including risks of false memory formation, algorithmic bias, and data privacy. Together, these findings articulate important boundaries for deploying generative AI in emotionally sensitive contexts.

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arXiv2025

Sensemaking Through Making: Developing Clinical Domain Knowledge by Crafting Synthetic Datasets and Prototyping System Architectures

Designers have ample opportunities to impact the healthcare domain. However, hospitals are often closed ecosystems that pose challenges in engaging clinical stakeholders, developing domain knowledge, and accessing relevant systems and data. In this paper, we introduce a making-oriented approach to help designers understand the intricacies of their target healthcare context. Using Remote Patient Monitoring (RPM) as a case study, we explore how manually crafting synthetic datasets based on real-world observations enables designers to learn about complex data-driven healthcare systems. Our process involves observing and modeling the real-world RPM context, crafting synthetic datasets, and iteratively prototyping a simplified RPM system that balances contextual richness and intentional abstraction. Through this iterative process of sensemaking through making, designers can still develop context familiarity when direct access to the actual healthcare system is limited. Our approach emphasizes the value of hands-on interaction with data structures to support designers in understanding opaque healthcare systems.

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arXiv2025

Generative AI as a Playful yet Offensive Tourist: Exploring Tensions Between Playful Features and Citizen Concerns in Designing Urban Play

Play is pivotal in fostering the emotional, social, and cultural dimensions of urban spaces. While generative AI (GAI) potentially supports playful urban interaction, a balanced and critical approach to the design opportunities and challenges is needed. This work develops iWonder, an image-to-image GAI tool engaging fourteen designers in urban explorations to identify GAI's playful features and create design ideas. Fourteen citizens then evaluated these ideas, providing expectations and critical concerns from a bottom-up perspective. Our findings reveal the dynamic interplay between users, GAI, and urban contexts, highlighting GAI's potential to facilitate playful urban experiences through generative agency, meaningful unpredictability, social performativity, and the associated offensive qualities. We propose design considerations to address citizen concerns and the `tourist metaphor' to deepen our understanding of GAI's impact, offering insights to enhance cities' socio-cultural fabric. Overall, this research contributes to the effort to harness GAI's capabilities for urban enrichment.

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arXiv2024

Assembling the Puzzle: Exploring Collaboration and Data Sensemaking in Nursing Practices for Remote Patient Monitoring

Remote patient monitoring (RPM) involves the remote collection and transmission of patient health data, serving as a notable application of data-driven healthcare. This technology facilitates clinical monitoring and decision-making, offering benefits like reduced healthcare costs and improved patient outcomes. However, RPM also introduces challenges common to data-driven healthcare, such as additional data work that can disrupt clinician's workflow. This study explores the daily practices, collaboration mechanisms, and sensemaking processes of nurses in RPM through field observations and interviews with six stakeholders. Preliminary results indicate that RPM's scale-up pushes clinicians toward asynchronous collaboration. Data sensemaking is crucial for this type of collaboration, but existing technologies often create friction rather than support. This work provides empirical insights into clinical workflow in nursing practice, especially RPM. We suggest recognizing data sensemaking as a distinct nursing practice within data work and recommend further investigation into its role in the workflow of nurses in RPM.

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arXiv2024

Re.Dis.Cover Place with Generative AI: Exploring the Experience and Design of City Wandering with Image-to-Image AI

The HCI field has demonstrated a growing interest in leveraging emerging technologies to enrich urban experiences. However, insufficient studies investigate the experience and design space of AI image technology (AIGT) applications for playful urban interaction, despite its widespread adoption. To explore this gap, we conducted an exploratory study involving four participants who wandered and photographed within Eindhoven Centre and interacted with an image-to-image AI. Preliminary findings present their observations, the effect of their familiarity with places, and how AIGT becomes an explorer's tool or co-speculator. We then highlight AIGT's capability of supporting playfulness, reimaginations, and rediscoveries of places through defamiliarizing and familiarizing cityscapes. Additionally, we propose the metaphor AIGT as a 'tourist' to discuss its opportunities for engaging explorations and risks of stereotyping places. Collectively, our research provides initial empirical insights and design considerations, inspiring future HCI endeavors for creating urban play with generative AI.

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arXiv2024

AI Cat Narrator: Designing an AI Tool for Exploring the Shared World and Social Connection with a Cat

As technology continues to advance, the interaction between humans and cats is becoming more diverse. Our research introduces a new tool called the AI Cat Narrator, which offers a unique perspective on the shared lives of humans and cats. We combined the method of ethnography with fictional storytelling, using a defamiliarization strategy to merge real-world data seen through the eyes of cats with excerpts from cat literature. This combination serves as the foundation for a database to instruct the AI Cat Narrator in crafting alternative narrative. Our findings indicate that using defamiliarized data for training purposes significantly contributes to the development of characters that are both more empathetic and individualized. The contributions of our study are twofold: 1) proposing an innovative approach to prompting a reevaluation of living alongside cats; 2) establishing a collaborative, exploratory tool developed by humans, cats, and AI together.

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arXiv2024

Unlocking Memories with AI: Exploring the Role of AI-Generated Cues in Personal Reminiscing

While technology-mediated reminiscing has been studied for decades, generating relevant cues to trigger personal reminiscing remains challenging. The potential of AI in generating relevant content across various domains has been recently recognized, yet its use in facilitating reminiscing is still less explored. This work aims to explore the use of AI in supporting the recall of personal memories associated with significant objects at home. We designed Treasurefinder, a device powered by a large language model (LLM) that generates open-ended questions based on stories stored in NFC-tagged physical objects or cards. We conducted an exploratory study with 12 participants, grouped in pairs, to observe reminiscing behaviors when using Treasurefinder. The results showed the AI-generated questions 1) supported individuals to recall the past, 2) provided new insights about the other person, and 3) encouraged reflection. Notably, the device facilitated active memory retrieval related to cherished objects that are often overlooked.

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