Mountain areas are especially vulnerable to climate change. In recent years, intermittent droughts have forced many Alpine huts to close early or rely on cable cars or helicopters to import water. To raise awareness of water scarcity among hut visitors, we developed Framing Water, a reflective game based on the data physicalization of each visitor's water consumption during an overnight stay. The game requires players to select their most essential water-using activities without exceeding a fixed limit. Yet, it is designed not to provide a univocal answer about the right choices to make but to spark reflection and dialogue around trade-offs in daily practices. We evaluated it with 56 participants and found that water-use decisions are influenced by individual needs, values, and social norms. This work contributes to Sustainable HCI by showing how playful, data-driven artifacts can foster reflection and negotiation of resource use in response to climate challenges.
Unhoused individuals in U.S. urban shelters increasingly leverage internet technologies for income-earning and financial management. We investigate app-mediated work used by these communities including online gig, shift, or microtask labor that reduce barriers to earning, but may be unreliable or present other risks. Participants use a patchwork of online platforms that together can help meet income needs, but often treat users unfairly or fail to deliver on expected earnings. Along with a diverse array of gig economy apps, potentially exploitative low-pay apps offer lower-end markets for labor including ``microtasks''---surveys, data-entry, app testing, or gaming---and gambling. These often exhibit dark patterns taking advantage of individuals' urgent need for cash, consuming excessive time or presenting scams, unpredictable costs, malware risks, and other harms; we draw analogies to ``Poverty Industries'' such as payday loans or pawn shops. Nonetheless, these apps’ popularity suggests that monetary incentives could be used to drive uptake of positive interventions among vulnerable groups, with appropriate precautions against malicious actors.
This paper presents speculative performance as an approach for engaging participants in imagining, enacting, and reflecting on technological futures through a combination of speculative design, performance, and intergenerational engagement. While speculative and performance-based methods are well established in HCI, there has been limited exploration of how these practices might explore critical technological literacies across generations. To investigate this, we ran two five-day workshops with approximately 30 older adults and young people, who collaboratively created speculative artefacts and dramatic scenes of technological futures, which culminated in a final public performance. We demonstrate how speculative performance can foster critical literacies of digital technologies and data by enabling participants to embody technological issues, move from technological malfunction to social and relational implications, and imagine alternative futures. We reflect on the opportunities and challenges of speculative performance and argue that this methodological approach expands how HCI imagines, critiques and performs technological futures with intergenerational communities.
The digital transformation of the energy sector introduces HCI challenges that exceed traditional end-user design. While systemic perspectives are increasingly called for, empirical work on the tensions between provider constraints and domestic practices remains limited. We introduce the Socio-Technical Energy Triad, situating digital energy services at the intersection of providers, households, and the emerging energy system. Drawing on 21 provider interviews, a technology-probe deployment with 10 users, and a survey with 98 respondents, we identify a structural disconnect across the Triad. Providers struggle with fragmented data infrastructures and limited user trust, hindering data-driven optimization. For households, this system logic remains largely invisible: they focus on efficiency and resist additional cognitive labor in managing domestic energy. We argue that bridging this "Invisibility Gap" requires not just traditional visualization but the design of legible automation and the translation of system-level needs into clear, user-centered signals.
Nature has long been valued for its restorative impact on emotion and well-being, motivating many HCI systems to incorporate nature as a calming design material. However, cultural traditions such as Taoism frame nature not as passive, but as an active, symbolic force for emotional transformation. We present Zenergy, a mobile application that uses a large language model to generate personalized guided meditations grounded in Taoist nature imagery. Based on users' emotional and contextual input, Zenergy leads them through a symbolic journey using natural metaphors such as wind to release burdens, rivers to restore flow, and sunlight to renew strength. A mixed-method field study (N = 27) showed that Zenergy enhanced users' self-efficacy, emotional clarity, and spiritual connection. We introduce transformative nature imagery as a design lens for everyday empowerment, and offer strategies for embedding culturally grounded symbolism into interactive well-being technologies.
Content moderators review disturbing content to protect social media users, often at significant cost to their mental health. Recent reports document the mental health conditions of African moderators as notably problematic. Beyond the content itself, what factors contribute to the deteriorating mental health of these workers? We surveyed 134 moderators across Africa to understand their mental health and interviewed 15 moderators to contextualize their experiences. We found that African moderators suffer from high psychological distress and lower well-being compared to moderators in other areas. Former moderators showed significantly higher distress levels, demonstrating long-term impact that extends beyond their moderation work. Our interviews showed that systemic and structural labor conditions contribute to moderators’ severe psychological distress and diminished mental well-being. Corporate wellness programs promoted by platforms were found ineffective and inadequate. We discuss how this requires holistic attention and structural solutions by all involved parties to improve moderators’ mental health.
At the heart of conservation are the field staff who study and monitor ecosystems in challenging environments. Recent advances in AI models raise the question of whether LLM assistants could improve the experience of data collection for these staff. However, on-device AI deployment for conservation field work poses significant challenges, and is understudied. To address this gap, we conducted semi-structured interviews, surveys, and participant observation with partner conservancies in the Pacific Northwest and Namibia to better understand the field work context. We employ speculative methods through the lens of technology acceptance theory to critically analyze how on-device AI would affect field work, by developing an on-device transcription-language model pipeline, which we built atop of EarthRanger, a widely-used, open-source conservation platform. Our findings suggest that although on-device LLMs hold some promise for field work, the infrastructure required by current on-device models clashes with the reality of resource-limited conservation settings.