171. Heritage, Memory, & Speculative Narratives
発表担当
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To critically examine the role of AI in historical representation and resist anti-LGBTQIA+ biases and erasures, we leverage un/making and propose a tactic we name material reconfigurations. We share an autoethnographic account of un/making and materially reconfiguring AI-generated images of queer histories: the lead author's memories of queer places and events. Through hand annotating, scratching, burying, submerging, and walking with physical images, they un/make and reconfigure, highlighting embodied aspects of archival records unparsable by generative AI. We propose that un/making and materially reconfiguring synthetic archival images can resist generative AI's increasingly hegemonic role in misrepresenting historical data and erasing queer identities. We contribute reflections on un/making and material reconfigurations as tangible tactics for queering AI, attuning to queer temporalities to unsettle AI-generated histories, using embodied, autoethnographic practices as critical strategies, and working through tensions of use and refusal in Queer AI research.
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Pre-visit information can enrich museum experiences, yet creates a dilemma: text-only descriptions can overwhelm without visual anchors, while viewing artworks in advance can spoil surprise. To address this tension, we introduce TwistLens, a docent-informed, AI-supported image transformation system that generates twisted previews--transformed images that convey interpretive cues while concealing original visuals. TwistLens extracts key cues from docent text using a structured taxonomy, then applies two strategies: EchoLens, which preserves intended description while altering representation, and DecoyLens, which distorts described information while maintaining representational coherence. A co-design study identified strategy preferences by information type, informing category-specific refinements. A controlled evaluation further showed that TwistLens preserves anticipation, triggers curiosity, and supports active learning without visual spoil. These findings demonstrate how semantically-aware image transformation can balance knowledge delivery and anticipation in museum contexts.
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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 exemplar 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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AI-generated future selves have been explored for self-reflection and counseling, yet most remain confined to chat, voice, or diary formats. We introduce RoF (Reminiscences of Futures), a system that enables users to encounter and converse with embodied future selves. It synthesizes voice and appearance from personal data and generates three projections of the user ten years ahead, situated within a tunnel-like installation that evokes a time-slip experience. We report the design process, stepwise judgements, and a formative study with six parent–child pairs that informed design strategies. We also present a week-long user study with fifteen participants. Participants perceived RoF as an emotionally rich experience, reporting shifts in their attitudes toward the future and deeper reflection on their present identity and life. We discuss the implications of interacting with future selves for fostering reflection and preparing positively for the future, as well as ethical issues, such as self-replication using AI.
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This paper presents LLooM, a probe designed to capture situated, temporal, and contradictory experiences with language technologies such as voice assistants, chatbots, and LLMs. The design of LLooM draws on work in probes, feminist HCI, and storytelling to invite participants to write stories about their encounters with language technologies on fabric strips and weave them into looms. Through a public, researcher-facilitated, and collective participant deployment with 56 participants, LLooM enabled participants to share diverse perspectives on language technologies. This methodological approach makes two contributions to probe design in HCI: enabling participants to reshape the methodological assumptions underlying research and allowing participants' visible contributions to become provocations that support collaborative meaning-making across diverse experiences.
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The Kaiping Diaolou and Villages, a UNESCO World Heritage Site, exemplify hybrid Chinese and Western architecture shaped by migration culture. However, architectural heritage engagement often faces authenticity debates, resource constraints, and limited participatory approaches. This research explores current challenges of leveraging Artificial Intelligence (AI) for architectural heritage, and how AI-assisted interactive systems can foster cultural heritage understanding and preservation awareness. We conducted a formative study (N=14) to uncover empirical insights from heritage stakeholders that inform design. These insights informed the design of Gen-Diaolou, an integrated AI-assisted interactive system that supports heritage understanding and preservation. A pilot study (N=18) and a museum field study (N=26) provided converging evidence suggesting that Gen-Diaolou may support visitors’ diachronic understanding and preservation awareness, and together informed design implications for future human--AI collaborative systems for digital cultural heritage engagement. More broadly, this work bridges the research gap between passive heritage systems and unconstrained creative tools in the HCI domain.
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While generative AI tools are increasingly adopted for creative and analytical tasks, their role in interpretive practices,where meaning is subjective, plural, and non-causal, remains poorly understood. This paper examines AI-assisted tarot reading, a divinatory practice in which users pose a query, draw cards through a randomized process, and ask AI systems to interpret the resulting symbols. Drawing on interviews with tarot practitioners and Hartmut Rosa's Theory of Resonance, we investigate how users seek, negotiate, and evaluate resonant interpretations in a context where no causal relationship exists between the query and the data being interpreted. We identify distinct ways practitioners incorporate AI into their interpretive workflows, including using AI to navigate uncertainty and self-doubt, explore alternative perspectives, and streamline or extend existing divinatory practices. Based on these findings, we offer design recommendations for AI systems that support interpretive meaning-making without collapsing ambiguity or foreclosing user agency.