The overturning of Roe v. Wade in 2022 by the U.S. Supreme Court in Dobbs v. Jackson exposed and exacerbated existing gaps in reproductive privacy. In the post-Roe era, aggressive surveillance by both government and private entities has made real and heightened concerns about privacy violations for people capable of pregnancy (PCOP). We investigate PCOPs' reproductive privacy concerns and the strategies they use to address these concerns post-Roe. We conducted semi-structured interviews with 18 adult cisgender women and transgender men in the U.S. Our findings show that for PCOPs, privacy risks are both persistent and anomalous, imposing what we conceptualize as reproductive privacy labor , a type of safety and data work that is, to them, both necessary and exhausting. We introduce the conceptual framework Sociotechnical Reproductive Privacy , outlining the relationships between actors, technologies, and identities that are implicated in the many contexts of reproductive privacy vulnerabilities post-Roe. We conclude with considerations for research and design, and explore the utility of approaches like refusal and regulation (e.g., technical, policy) in promoting sociotechnical reproductive privacy. This research underscores the urgent need to address the intersection of reproductive rights, privacy, and technology, offering insights into how affected individuals navigate and manage their reproductive health decisions in an increasingly surveilled sociotechnical landscape.

Older adults (OAs) usually face various challenges when using smartphones due to their limited knowledge and the declines in memory and information processing capabilities. Many studies in HCI and CSCW communities have focused on supporting OAs to independently use smartphones. However, compared to independent exploration, support from younger family members (YFMs) has specific advantages in problem understanding, solution personalization, and security protection. However, OAs and YFMs generally have gaps in time, knowledge, and experience, affecting the efficiency of support and their experience. For this problem, we conduct a formative study to gather insights into OAs and YFMs' perspectives and expectations in the supporting procedure. Then we introduce chatbot to mediate the gaps between OAs and YFMs and build a system named WePilot to assist them to collaboratively solve smartphone usage problems. Evaluations with 12 pairs of participants (OA and corresponding YFM) suggest WePilot's strengths in improving problem solving efficiency and OAs and YFMs' experience. Based on these findings, we propose several insights into the future design of intergenerational technical support systems.

Writing and collaboration are crucial skills in professional and academic settings. However, assessments of collaborative writing often focus only on the final text, overlooking individual contributions and the diverse strategies students employ during the writing process. To support teachers in interpreting and assessing student behaviour, we propose analysing writing data at a granular level, down to individual characters, and using user sessions as observation units to capture coherent interaction patterns. This paper introduces three methods for identifying interaction patterns in collaborative writing. The first method classifies session types by analysing features such as writing, reading, and communication behaviours, session length, and the number of group members collaborating synchronously. The second method identifies frequent sequences of session types by examining their order, enabling process analyses at an abstract yet manageable level compared to using log data. The third method focuses on text-level collaboration by evaluating the frequency of text passage modifications made by the original author or other group members. This approach quantifies individual collaboration and, at the group level, identifies isolated versus closely connected group members, shedding light on the mode of collaboration and degree of group cohesion. We demonstrate these three methods in a case study involving two cohorts, K A = 294 and K B = 242 groups of up to 9 learners (N A = 1, 848, N B = 1, 463). The interaction patterns identified using these methods are intended to help teachers understand collaborative writing processes and identify situations where the participating learners require support.

The integration of AI into decision-making processes offers substantial benefits, particularly in enhancing accuracy and efficiency. However, long-term consequences, such as over-reliance, skill erosion, and loss of human agency, present significant challenges. This study investigates various human-AI collaboration protocols~-~traditional, inhibition, displacement, and replacement~-~across multiple medical settings, including radiological imaging, ECG, and endoscopy. We introduce a novel framework that includes a choice nomogram and qualitative assessment tool, designed to optimize both decision accuracy and socio-technical impacts. Our findings reveal that the displacement protocol consistently outperformed others in several contexts, achieving 87% accuracy in MRI analysis, 89% in x-ray reading and 85% in endoscopy; conversely, the traditional protocol was most effective only in ECG analysis, with 82% accuracy. These results demonstrate that no single protocol is universally optimal, highlighting the need for context-specific selection to ensure effective and sustainable AI-supported decision-making, with a focus on balancing short-term performance with long-term human factors.

High-stakes algorithmic decisions should be contestable, yet there is limited guidance on how to design for contestability, resulting in inadequate contestation processes. Using an algorithmic university admissions decision scenario, we investigate what type of review process people prefer when contesting algorithmic decisions. Through a choice-based conjoint experiment, we explore review process preferences, asking participants to choose their preferred review process from a choice of two that differ across five design attributes: who the reviewer is, how independent the review is, the ability to communicate with the reviewer, the style of the review, and how much the review costs. Expanding on existing studies that focus on the perspective of the decision subject, we introduce eight different scenario perspectives to explore the extent to which particular design attributes matter to different stakeholders. People consistently prefer human reviewers who can make fresh decisions and communicate directly, while also prioritising affordable review processes. These findings highlight the importance of human involvement and cost-effectiveness in review processes. While the impact of perspective was relatively small, our qualitative analysis reveals important underlying tensions between efficiency, fairness, and individual needs. These subtle variations underscore the complexity of designing universally acceptable review processes. We propose design considerations that can help decision-makers to design review processes that are tailored to the specific decision-making context.

This paper provides guidance for building and maintaining infrastructure for participatory AI efforts by sharing reflections on building World Wide Dishes (WWD), a bottom-up, community-led image and text dataset of culinary dishes and associated cultural customs. We present WWD as an example of participatory dataset creation, where community members both guide the design of the research process and contribute to the crowdsourced dataset. This approach incorporates localised expertise and knowledge to address the limitations of web-scraped Internet datasets acknowledged in the Participatory AI discourse. We show that our approach can result in curated, high-quality data that supports decentralised contributions from communities that do not typically contribute to datasets due to a variety of systemic factors. Our project demonstrates the importance of participatory mediators in supporting community engagement by identifying the kinds of labour they performed to make WWD possible. We surface three dimensions of labour performed by participatory mediators that are crucial for participatory dataset construction: building trust with community members, making participation accessible, and contextualising community values to support meaningful data collection. Drawing on our findings, we put forth five lessons for building infrastructure to support future participatory AI efforts.

Disengagement from games can be challenging for players, and various attempts have been made to provide parents with the tools to limit children’s playing time. However, little is known about how children experience disengagement, and how children and parents jointly navigate the process of exiting a play session. To address this gap, we conducted semi-structured interviews with 25 participants, including 13 children (ages 4–9) and 12 parents. Through Thematic Analysis, we show that children – like adults – struggle to exit play if they have not yet reached a point of closure and are unsatisfied with their experience. At the same time, parents experience frustration when establishing limits, and many found it difficult to account for the state of a play session. On this basis, we highlight the relevance of disengagement-friendly game design and mediation strategies, and provide recommendations for children’s disengagement from play sessions while protecting their player experience.

As wildfires increasingly threaten communities worldwide, those at risk can benefit from community-centric approaches to designing interventions. A multi-layered approach can make members of the community powerful co-designers by incorporating their ideas alongside insights from experts and designers to successfully create and deploy an intervention. We took a community-centered Research through Design approach to develop a Serious Game to promote wildfire preparedness in two communities. Participatory Design workshops, interviews, and targeted user studies allowed us to leverage local knowledge of residents and expertise of stakeholders at distinct stages where their insight could be best utilized. Based on this work, we propose a process for designing Serious Games as interventions for complex societal challenges by utilizing community and expert involvement throughout iteration. Our findings suggest that the games we designed following this process lead to concrete learning and opportunities for discussion, and offer flexibility for integration with other community initiatives.

Game modding offers unique and personalized gaming experiences, but the technical complexity of creating mods often limits participation to skilled users. We envision a future where every player can create personalized mods for their games. To explore this space, we designed StarCharM, a GenAI-based non-player character (NPC) creator for Stardew Valley. Our tool enables players to iteratively create new NPC mods, requiring minimal user input while allowing for fine-grained adjustments through user control. We conducted a user study with ten Stardew Valley players who had varied mod usage experiences to understand the impacts of StarCharM and provide insights into how GenAI tools may reshape modding, particularly in NPC creation. Participants expressed excitement in bringing their character ideas to life, although they noted challenges in generating rich content to fulfill complex visions. While they believed GenAI tools like StarCharM can foster a more diverse modding community, some voiced concerns about diminished originality and community engagement that may come with such technology. Our findings provided implications and guidelines for the future of GenAI-powered modding tools and co-creative modding practices.

Persuasive games, designed to change attitudes and inspire behavior change, have attracted significant interest. In particular, persuasive recruitment games are increasingly in demand due to under recruitment in many occupations. However, research on persuasive games often lacks clear design guidance and tends to focus on individual player experience factors, rather than identifying and prioritizing the most influential ones that should shape design decisions. Our study (n = 957) examined how player experience impacts recruitment interventions aimed at encouraging careers in teaching. We compared three approaches; a persuasive game, a “realistic job preview” (RJP) and a control game. Effectiveness was measured by increases in Interest (in teaching), Person-vocation (PV) fit and Self-efficacy. Results showed that the persuasive game was the most effective at increasing Interest, while the RJP had the greatest impact on PV fit and Self-efficacy. Interest was primarily influenced by experiences of meaning, followed by mastery. Conversely, mastery followed by meaning were the strongest influences on PV fit and Self-efficacy. Experiences of immersion or autonomy had no significant effect on persuasion. We discuss how understanding which aspects of game experience have most impact can aid the design of persuasive games for recruitment and other purposes. 