Younah Kang

This study examines how parents of LGBTQ+ individuals in South Korea navigate the emotional rupture fueled by fear, isolation, and disorientation after learning their children’s queer identity, encounter queer-related (mis)information as a way of coping with this emotional toll, and come to listen to queer realities relationally. Through this process, we highlight how parents reconstruct their identities as supportive parents, which reshapes their informating practices, making them more critical in assessing queer-related (mis)information, developing strategies to protect themselves from harmful narratives, and actively challenging misinformation to support others navigating similar experiences. This work contributes to CSCW by (1) foregrounding parents of LGBTQ+ individuals, an underrepresented yet critical stakeholder group in Queer HCI; (2) demonstrating how identity reconfiguration following a trauma-healing process could transform informating practices; and (3) arguing that addressing misinformation requires attention beyond individual fact-based discerning to account for its relational, cultural, and emotional dimensions. Further, we invite CSCW scholars to reconsider the balance between abstracting and humanizing information, explore future design possibilities for parents of LGBTQ+ children, and reflect on the role of researchers as participants in collective research communities fueled by care.

Qualitative interviewing is a foundational method in Human Computer-Interaction (HCI) research and a common component of methods education. Yet interview instruction is often framed around procedural competencies, developing protocols, minimizing bias, and ensuring ethical compliance, while the relational tensions that shape real interview encounters remain largely implicit. This provocation argues that interviews are not neutral data-gathering practices but socially situated interactions co-constructed through relational dynamics. Drawing on interdisciplinary scholarship from qualitative methodology, feminist epistemology, and ethics-of-care research, we synthesize three relational dimensions of interview practice. These include power and positionality, emotional work, and ethics-in-practice, and demonstrate how interviews inherently involve negotiation, interpretation, and responsibility that exceed procedural models of training. Building on this synthesis, we propose pedagogical approaches for HCI education that make relational work explicit and teachable, including reflexive exercises that surface positionality and interactional dynamics, case-based analyses to cultivate emotional literacy, researcher-centered protocols, and structured rehearsals for ethical decision-making. Our aim is not to replace procedural instruction but to expand it by framing interviewing as both a technical and relational practice, inviting educators to reconsider how methods are taught and supported while bridging the gap between the formal curriculum of training and the hidden relational work that unfolds in practice.

Large language models (LLMs) are increasingly used in biomedical question-answering (QA) systems, yet clinician adoption remains constrained by challenges in trust. Prior work has largely operationalized trust as a static outcome. Such measures capture whether clinicians trusted a system, but obscure how they arrived at that judgment. We reframe clinician trust in LLM-based biomedical QA as an ongoing process of information foraging and sensemaking. In this view, trust calibration emerges through iterative verification shaped by interface-level transparency. Using an exploratory study design that holds AI-generated answers constant while varying only the structure of evidence access—unlike much prior work that manipulates model accuracy or explanation type—we examined how three participants with medical training navigated, verified, and judged AI-generated biomedical answers across three transparency conditions. Pilot observations indicate that transparency features structured verification behavior rather than directly increasing or decreasing trust: participants selectively attended to the specificity of evidence, revisited claims after inspecting supporting or conflicting snippets, and deferred or rejected answers when evidence appeared incomplete. These observations suggest that effective transparency in clinical AI systems should support controlled cognitive engagement—enabling clinicians to perform their own verification—rather than aim to reduce cognitive effort through persuasive justification.

The experience of leaving North Korea and navigating life as an undocumented person in China presents significant challenges for those seeking refuge in South Korea. This paper presents findings from an eight-month longitudinal ethnographic study with five newly arrived North Korean defector women in their first year in South Korea. We closely examine how digital technologies are intertwined with their everyday adaptation: how newcomers engage with digital systems, what frictions emerge, and how past experiences of living invisibly shape these interactions. This study contributes to the HCI community by (1) offering an empirically grounded, underrepresented account of early-stage digital resettlement among North Korean defectors; (2) conceptualizing first-time digital identity as a site of acute vulnerability shaped by past experiences and long histories of marginalization; and (3) proposing temporal, context-sensitive, self-reliance–oriented approaches to digital adaptation that supports vulnerable newcomers’ sociotechnical transition into a new society.