Neha Kumar

Given the growing global crises caused by the growth economy, there is a pedagogical responsibility to prepare future Human–Computer Interaction (HCI) professionals to embrace uncertainty and question unsustainable ideologies and practices. This workshop creates a space for educators and students to critically reflect on how HCI pedagogy might move beyond “bigger–and-faster” framings and toward practices of sufficiency, repair, and care. Through activities such as co-designing a living syllabus and reimagining evaluation criteria for student work, participants will explore how education can itself function as an infrastructural practice for cultivating post-growth perspectives within HCI. In doing so, this workshop aims to foreground pedagogy as a vital site where post-growth commitments can take root, reorienting the content and practice of HCI toward cultivating socio-ecologically just futures.

Menstrual experiences are shaped by many different stakeholders, entities, and the broader socio-cultural context, or in other words, the ecological systems surrounding a menstruator. We explore what it means to take an ecological approach in studying and designing menstrual technologies. We translated and adapted Ecological Systems Theory (EST) for menstrual wellbeing and packaged the outcome in the form of a socio-ecological Canvas, accompanied by written examples and a set of prompts to facilitate engagement. We invited ten experts to engage with the Canvas in reflective workshops, which informed its further refinement. These sessions highlighted the Canvas’ generative value, fostering critical reflection on how design choices are shaped by and ripple across layers of influence. With this translational research, we invite HCI researchers and practitioners to critically reflect on the ecologies they study and design for, envisioning both aspired versions of existing realities and realities that do not exist yet.

As AI deployment accelerates globally, the urgency for responsible AI(RAI) frameworks has catalysed unprecedented policy initiatives and research investments worldwide. The CHI community, positioned uniquely at the intersection of technology, people, society, and values, has a crucial role in shaping RAI development. This meet-up brings together international HCI researchers working across value-sensitive design, human-centered AI, ethics, explainability, trustworthy AI, sustainability and equitability to foster collaborative dialogue on CHI's perspectives and potential in RAI. Through an interactive town hall format featuring community standups, democratic topic selection, and structured roundtable discussions, we will generate actionable outcomes including network building, knowledge sharing, and concrete collaboration opportunities for the CHI community around RAI. This meetup would serve as a foundational response from the CHI community to establish priorities, forge connections, and catalyse the next generation of RAI research that centers human values, promotes fairness, and advances democratic principles in an era of AI transformation.

The impact of culture on how people express distress in online support communities is increasingly a topic of interest within Computer Supported Cooperative Work (CSCW) and Human-Computer Interaction (HCI). In the United States, distinct cultures have emerged from each of the two dominant political parties, forming a primary lens by which people navigate online and offline worlds. We examine whether partisan culture may play a role in how U.S. Republican and Democrat users of online mental health support communities express distress. We present a large-scale observational study of 2,184,356 posts from 8,916 statistically matched Republican, Democrat, and unaffiliated online support community members. We utilize methods from causal inference to statistically match partisan users along covariates that correspond with demographic attributes and platform use, in order to create comparable cohorts for analysis. We then leverage methods from natural language processing to understand how partisan expressions of distress compare between these sets of closely matched opposing partisans, and between closely matched partisans and typical support community members. Our data spans January 2013 to December 2022, a period of both rising political polarization and mental health concerns. We find that partisan culture does play into expressions of distress, underscoring the importance of considering partisan cultural differences in the design of online support community platforms.

Computer-Supported Cooperative Work (CSCW) research on maternal and child health (MCH) has largely taken mother- or child-centric lenses to technology design for improved outcomes. An emerging body of CSCW research has called for a renewed focus on supporting caregivers through sociotechnical interventions to enable better health outcomes. Our research--with underserved communities in urban India--extends this prior work by centering fathers and their care work during pregnancy and early childhood. We examine how gender roles, religious beliefs, aspirations for social mobility, and financial responsibilities influence fathers' participation in MCH. We use qualitative inquiry in highlighting the constraints on the nature of care work fathers are able to perform given the patriarchal and societal norms they operate within, and how the care work they perform is rendered invisible by diverging from traditional notions of care. In doing so, we expand dominant notions of care work in such ecologies, and discuss implications for the design of technologies for better supporting fathers towards familial wellbeing.

People experiencing severe distress increasingly use Large Language Model (LLM) chatbots as mental health support tools. Discussions on social media have described how engagements were lifesaving for some, but evidence suggests that general-purpose LLM chatbots also have notable risks that could endanger the welfare of users if not designed responsibly. In this study, we investigate the lived experiences of people who have used LLM chatbots for mental health support. We build on interviews with 21 individuals from globally diverse backgrounds to analyze how users create unique support roles for their chatbots, fill in gaps in everyday care, and navigate associated cultural limitations when seeking support from chatbots. We ground our analysis in psychotherapy literature around effective support, and introduce the concept of therapeutic alignment, or aligning AI with therapeutic values for mental health contexts. Our study offers recommendations for how designers can approach the ethical and effective use of LLM chatbots and other AI mental health support tools in mental health care.