Playing cards have long been a central feature in board games, despite their physical limitations, such as fragility, and repetitive content. Yet, while HCI research increasingly explores augmented board games, interactive playing cards remain underexplored. In this work, we investigate the use of e-paper displays as interactive playing cards. Based on a formative study with eight game designers, we designed and implemented InkStack---a prototype consisting of e-paper cards, a dedicated programmer, and a web application for card customisation. We then evaluated InkStack in a within-subject study with $n = 20$ participants, comparing its use across four board game mechanics against traditional paper cards and a smartphone. Results show that InkStack is preferred for more complex mechanics, whereas paper and smartphones are sufficient for simpler tasks. The findings also highlight how customisation and versatility can enhance gameplay and enable novel forms of interaction.
Most generative AI tools prioritize individual productivity and personalization, with limited support for collaboration. Designed for traditional workplaces, these tools do not fit freelancers' short-term teams or lack of shared institutional support, which can worsen their isolation and overlook freelancing platform dynamics. This mismatch means that, instead of empowering freelancers, current generative AI tools could reinforce existing precarity and make freelancer collaboration harder. To investigate how to design generative AI tools to support freelancer collaboration, we conducted co-design sessions with 27 freelancers. A key concern that emerged was the risk of AI systems compromising their creative agency and work identities when collaborating, especially when AI tools could reproduce content without attribution, threatening the authenticity and distinctiveness of their collaborative work. Freelancers proposed "auxiliary AI" systems, human-guided tools that support their creative agencies and identities, allowing for flexible freelancer-led collaborations that promote "productive friction". Drawing on Marcuse's concept of technological rationality, we argue that freelancers are resisting one-dimensional, efficiency-driven AI, and instead envisioning technologies that preserve their collective creative agencies. We conclude with design recommendations for collaborative generative AI tools for freelancers.
With the ongoing shift into the digital, spontaneous social encounters in public spaces are becoming challenging for adolescents. This study explores how robotic street furniture could facilitate meaningful adolescent social interaction. In a focus group and a theater-based co-design workshop, fourteen adolescents envisioned and enacted ten speculative concepts, such as roaming benches that invite serendipitous meetings. An analysis of these concepts identified diverse roles for robots (e.g., icebreaker, scapegoat) and revealed their particular social strengths and weaknesses (e.g., objective yet insistent). These insights were condensed into eight design suggestions, such as designing robots to orchestrate coincidences or framing them as opponents that humans can team up against. We suggest that robots can facilitate adolescents’ social interaction in public spaces, particularly due to certain social strengths inherent in the machinic nature of a robot.
Mixed reality (MR) technologies hold promise for distributed musical collaboration, yet the needs of professional musicians remain underexplored. We conducted a set of co-design workshops with 24 professional musicians, organized into six four-piece bands, to envision future MR systems for networked music performance. Using a three-phase process (hands-on MR experience, individual brainwriting, and collaborative consensus-building), we identified core requirements that challenge current MR paradigms. Professional musicians operate from an "Audio-First'' framework where sonic experience constitutes the primary reality, contradicting visual-first MR approaches. Five unanimous requirements emerged: audio-first workflow, support for non-verbal communication, personalized audio control, spatial layout customization, and plug-and-play simplicity. Other high-consensus themes included real-time voice communication and realistic avatar representation. Taken together, our findings reveal that professional creative collaboration demands fundamentally different design priorities than traditional office-centered collaborative MR applications, requiring systems that support established workflows and nonverbal communication channels while prioritizing workflow preservation over visual immersion.
Responsible AI (RAI) content work, such as annotation, moderation, or red teaming for AI safety, often exposes crowd workers to potentially harmful content. While prior work has underscored the importance of communicating well-being risk to employed content moderators, designing effective disclosure mechanisms for crowd workers while balancing worker protection with the needs of task designers and platforms remains largely unexamined. To address this gap, we conducted individual co-design sessions with 15 task designers, 11 crowdworkers, and 3 platform representatives. We investigated task designer preferences for support in disclosing tasks, worker preferences for receiving risk disclosure warnings, and how platform representatives envision their role in shaping risk disclosure practices. We identify design tensions and map the sociotechnical tradeoffs that shape disclosure practices. We contribute design recommendations and feature concepts for risk disclosure mechanisms in the context of RAI content work.
AI agents, or bots, serve important roles in online communities. However, they are often designed by outsiders or a few tech-savvy members, leading to bots that may not align with the broader community's needs. How might communities collectively shape the behavior of community bots? We present Botender, a system that enables communities to collaboratively design LLM-powered bots without coding. With Botender, community members can directly propose, iterate on, and deploy custom bot behaviors tailored to community needs. Botender facilitates testing and iteration on bot behavior through case-based provocations: interaction scenarios generated to spark user reflection and discussion around desirable bot behavior. A validation study found these provocations more useful than standard test cases for revealing improvement opportunities and surfacing disagreements. During a five-day deployment across six Discord servers, Botender supported communities in tailoring bot behavior to their specific needs, showcasing the usefulness of case-based provocations in facilitating collaborative bot design.
While legacy sketchnotes capture rich design rationales and inspirations, they are rarely reused in contemporary practice. We present Co-Ideation Across Time (CIAT), utilizing Large Language Models (LLMs) to transform decades-old design sketchnotes into interactive "AI-augmented Knowledge Objects". Our system digitizes over 2,000 pages of alumni sketchnotes and connects them with conversational agents trained on corresponding theses and publications, enabling current and future students to engage in multimodal dialogue with past ideas and researchers. An exploratory evaluation with 12 participants showed that interacting with the system stimulated deeper understanding of abstract concepts, idea diversity, and fostered a stronger sense of continuity with the community’s legacy. Our contributions are threefold: (1) a method for integrating design legacies with LLM-driven conversational agents; (2) an empirical study demonstrating how this approach supports learning and intergenerational knowledge sharing; and (3) a conceptual framing of AI-Augmented Knowledge Objects as active participants in design ideation.