Women with endometriosis and Polycystic Ovary Syndrome (PCOS) require individualized, long-term, holistic care, yet systemic healthcare barriers leave many managing independently. Increasingly, women are turning to Large Language Models (LLMs), such as ChatGPT, for support in self-management. However, little is understood about how people use and experience LLMs in this context. We employed a feminist participatory approach to collective knowledge production and situated expertise to explore participants’ motivations and expectations in using LLMs for self-management. We engaged in co-annotation of personal ChatGPT conversations and collage-making sessions with 8 women living with endometriosis and/or PCOS, centering their lived experiences. We surfaced the layered experiences of self-managing endometriosis and/or PCOS with LLMs and how these compare and contrast with their ideal visions of care. We contribute empirical insights and design considerations for LLM systems providing support in this context.

Chronic illnesses (CI) are increasing worldwide, positioning virtual assistants (VAs) as valuable tools for supporting patients in self-management. As effective self-management relies on holistic, patient-centered practices, AI is increasingly integrated into VAs to provide more personalized support. Yet, it is essential that VA design processes remain grounded in participatory approaches prioritizing patients’ values, needs, and lived experiences. To assess the current state of VA design processes, we conducted a scoping review of 55 papers examining how care is framed and patients are involved. Our findings reveal AI-driven VAs prioritize reductionist approaches over holistic care with minimal patient involvement. This highlights a gap between the potential of patient-centered care technology and current implementation practices. Our contributions include (1) a mapping of care dimensions currently implemented in VAs, (2) a categorization of patient roles in the design process, and (3) design implications to expand care dimensions and patient involvement in AI-driven VAs.

Anticipating the ethical and societal risks of emerging technologies has become an urgent challenge as their rapid integration into everyday life can produce far-reaching social consequences. In response, Design Futures practices are gaining traction within HCI and design as approaches to critically examine and anticipate the implications of technology. Yet, systematic knowledge on how these practices are structured to foster ethical reflection remains limited. To address this gap, we conducted a scoping review of 32 case studies employing Design Futures to engage with ethical concerns. Drawing from this review, we present a Taxonomy of Design Futures Processes for Ethical Reflection, which illustrates how different activities, actors’ involvement, and types of futures generated shape the scope of ethical discussion. This taxonomy provides researchers and practitioners with practical guidance for creating Design Futures activities that foster ethical reflection on technology.

Gender microaggressions are subtle yet persistent forms of discrimination in workplace interactions. While LLMs can detect them in written texts, it remains poorly understood how their interpretations align or diverge from human perspectives and experiences. We present a mixed-method study comparing how LLMs and humans differing in gender identity and lived experience, interpret gender microaggressions in the workplace. Using short dialogues adapted from real-world accounts, we asked 141 participants to rate the likelihood that a scenario contains a microaggression and provide a rationale for their answers. The same tasks were completed by 7 different LLM models. Our analysis reveals significant differences in how humans and LLMs interpret microaggressions, captured in both ratings and rationales, and more interestingly, the effect of gender and lived experience on human interpretations. These findings highlight the need for systems detecting microaggressions to embrace interpretive plurality, and support reflection and awareness while accounting for ambiguity.

Nearly 20 years ago, Gaver et al. introduced ambiguity as a design resource, proposing tactics to reflect everyday uncertainty into interactive systems. This approach is especially relevant for self-tracking wearables, which often obscure the inherent ambiguity of system design and tracked phenomena with seemingly clear, prescriptive data and insights. Although scholars recognize the importance of ambiguity, its practical application in the design process remains underexplored. To address this, we conducted a two-week workshop with 60 designers, examining the application of Gaver et al.'s tactics into 11 design concepts, and performed interviews with 16 participants. Our findings reveal eight relevant ambiguity tactics for self-tracking and offer insights into participants' experiences with designing using ambiguity. We discuss prescription and overlooked ambiguity as levers for the operationalization of ambiguity, the potential benefits and downsides of ambiguity tactics for users, future directions for HCI research and practice, and the study limitations.

Informal caregivers' engagement with patient data is becoming increasingly central to CSCW and HCI research on health management. Cardiac telerehabilitation (CTR) technologies generate lifestyle and well-being data that support patients and their families in recovery management, yet informal caregivers' roles in CTR remain underexplored. Recreational athletes in rehabilitation are an especially under-researched group, despite their and their support system's unique needs. Focusing on caregivers of recreational athletes, we conducted interviews with ten participants and used six visual scenarios of a dyadic CTR system to explore their perspectives on data and information co-participation. Caregivers reported that co-participation could strengthen dyadic coping and management but emphasized the need to balance important trade-offs. We provide design recommendations for dyadic CTR systems that balance care needs and preferences, promoting caregiver involvement in a supportive, non-supervisory role. We contribute to CSCW research by proposing a conceptual shift in technology-mediated rehabilitation care: positioning caregiver-inclusive CTR systems as negotiation tools that support boundary work and balance competing care values.

Machine learning (ML) provides designers with a wide range of opportunities to innovate products and services. However, the design discipline struggles to integrate ML knowledge in education and prepare designers to ideate with ML. We propose the Mix & Match Machine Learning toolkit, which provides relevant ML knowledge in the form of tangible tokens and a web interface to support designers’ ideation processes. The tokens represent data types and ML capabilities. By using the toolkit, designers can explore, understand, combine, and operationalize the capabilities of ML and understand its limitations, without depending on programming or computer science knowledge. We evaluated the toolkit in two workshops with design students, and we found that it supports both learning and ideation goals. We discuss the design implications and potential impact of a hybrid toolkit for ML on design education and practice.

Social media platforms newsfeeds are generated by AI algorithms, which select and order posts based on user data. However, users are often unaware of what data is collected and employed for this aim, neither can they control it. To open up discussions on what data users are willing to feed the newsfeed algorithm with, we created the Facebook Data Shield, a human-size interactive installation where users can see and control what type of data is collected. By pressing buttons, data categories and/or data variables can be (de)activated. An outer rim with lights gives feedback to users about the level of personalization of the resulting newsfeed. We performed a preliminary study to get insights into what data users are willing to share, their preferred level of control, and the effect of such an installation on users’ awareness. Based on our findings, we discuss implications for design and future work.

Abstract In the COVID-19 pandemic, digital technologies (DT) supported the design and implementation of solutions addressing new needs and living conditions. We describe Design for Emergency, a digital open design platform developed to ideate solutions for people's fast-changing needs in the pandemic, to analyze how DT can affect human-centered design processes during emergencies. We illustrate how DT: i) helped quickly collect and analyse people's needs in different countries, visualize such data, and identify design directions and problem spaces; ii) facilitated the creation of a virtual network of stakeholders and an open-innovation digital platform; iii) inspired the ideation of solutions responding to people's changing needs and affected their implementation. We discuss the implications of adopting DT in designing for and during emergencies, as well as their current and future potential to promptly respond to emergency situations through a human-centered approach.

The research aims at proposing and validating a framework for supporting the design for user experience in interactive spaces (Ambient UX). It suggests that dynamic changes in interactive spaces should be designed focusing on their effects on three levels of the user experience: physical wellbeing, meanings, and social relations. Validation occurred through a field study performed in a work environment, where a dynamic lighting system was designed and installed. Preliminary results validate the relevance of the three levels, thus laying the ground for further research and discussion.