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Professional designers often struggle to apply insights from HCI research in their work. To make academic knowledge more accessible to practitioners, HCI researchers have created translational design tools, such as design cards, that support the translation of research insights into design practice. Prior work explored design cards for behavior change, interaction design, personal health informatics, and the sharing economy. Our work complements prior research by exploring the design and use of translational design cards for social aspects of societal resilience through a two-stage study with 14 student designers and eight professional designers. Our findings provide an empirical understanding of the design cards' generative value for incorporating research insights into the design process. Additionally, we discuss recommendations and highlight opportunities to enhance the design and use of the cards beyond societal resilience.
Autoethnography is a valuable methodological approach bridging the gap between personal experiences and academic inquiry, enabling researchers to gain deep insights into various dimensions of technology use and design. While its adoption in Human-Computer Interaction (HCI) continues to grow, a comprehensive investigation of its function and role within HCI research is still lacking. This paper examines the evolving landscape of autoethnographies within HCI over the past two decades through a systematic literature review. We identify prevalent themes, methodologies, and contributions emerging from autoethnographies by analysing a corpus of 31 HCI publications. Furthermore, we detail data collection techniques and analysis methods and describe reporting standards. Our literature review aims to inform future (HCI) researchers, practitioners, and designers. It encourages them to embrace autoethnography's rich opportunities by providing examples across domains (e.g., embodiment or health and wellbeing) to advance our understanding of the complex relationships between humans and technology.
Policies significantly shape computation's societal impact, a crucial HCI concern. However, challenges persist when HCI professionals attempt to integrate policy into their work or affect policy outcomes. Prior research considered these challenges at the "border" of HCI and policy. This paper asks: What if HCI considers policy integral to its intellectual concerns, placing system-people-policy interaction not at the border but nearer the center of HCI research, practice, and education? What if HCI fosters a mosaic of methods and knowledge contributions that blend system, human, and policy expertise in various ways, just like HCI has done with blending system and human expertise? We present this re-imagined HCI-policy relationship as a provocation and highlight its usefulness: It spotlights previously overlooked system-people-policy interaction work in HCI. It unveils new opportunities for HCI's futuring, empirical, and design projects. It allows HCI to coordinate its diverse policy engagements, enhancing its collective impact on policy outcomes.
Elicitation diary studies, a type of qualitative, longitudinal research method, involve participants to self-report aspects of events of interest at their occurrences as memory cues for providing details and insights during post-study interviews. However, due to time constraints and lack of motivation, participants’ diary entries may be vague or incomplete, impairing their later recall. To address this challenge, we designed an automatic contextual information recording agent, DiaryHelper, based on the theory of episodic memory. DiaryHelper can predict five dimensions of contextual information and confirm with participants. We evaluated the use of DiaryHelper in both the recording period and the elicitation interview through a within-subject study (N=12) over a period of two weeks. Our results demonstrated that DiaryHelper can assist participants in capturing abundant and accurate contextual information without significant burden, leading to a more detailed recall of recorded events and providing greater insights.
While AI-assisted individual qualitative analysis has been substantially studied, AI-assisted collaborative qualitative analysis (CQA) – a process that involves multiple researchers working together to interpret data—remains relatively unexplored. After identifying CQA practices and design opportunities through formative interviews, we designed and implemented CoAIcoder, a tool leveraging AI to enhance human-to-human collaboration within CQA through four distinct collaboration methods. With a between-subject design, we evaluated CoAIcoder with 32 pairs of CQA-trained participants across common CQA phases under each collaboration method. Our findings suggest that while using a shared AI model as a mediator among coders could improve CQA efficiency and foster agreement more quickly in the early coding stage, it might affect the final code diversity. We also emphasize the need to consider the independence level when using AI to assist human-to-human collaboration in various CQA scenarios. Lastly, we suggest design implications for future AI-assisted CQA systems.