CHI 2025
Emergency Remote Teaching (ERT) during COVID-19 offered a unique chance to study online higher education at scale, beyond traditional lab settings. Through a review of 22 empirical studies, we analyzed how online classrooms addressed different types of interaction. Our findings highlight the need for future research that centers Learner-Content interaction as a way to balance flexibility with structure—especially as ERT may continue to shape education going forward.
CHI 2025
Trust in autonomous vehicles varies widely among individuals, and this study uses machine learning to identify the key factors influencing young adults’ trust. Surveying over 1,400 participants, the analysis reveals that perceptions of AV risks and benefits, usability attitudes, institutional trust, prior experience, and mental models are the strongest predictors of trust—while psychosocial traits and driving styles play a lesser role. These findings underscore the need to account for individual differences when designing trustworthy AV systems.
CHI 2025
Interdisciplinary engagement across disciplines is often hindered by stylistic and conceptual differences. Drawing on Large Language Models (LLMs), this work explores how metaphor-based support can improve accessibility and engagement. A survey of early-career HCI researchers found that metaphors increased interest in STS texts, particularly for those with limited prior exposure. We propose a dialogic model of metaphor exchange to support shared understanding and critical reflection across disciplines.
CHI 2025
Explanation errors from autonomous vehicles undermine user comfort, trust, and satisfaction—particularly in unfamiliar or non-routine driving contexts. Through a driving simulator study, the work shows that even subtle inaccuracies in how AVs communicate can erode user confidence, emphasizing the need for clear, context-aware explanations to foster reliable human-machine interaction.
CHI 2024
Analyzing patient-provider communication through social signals like dominance, interactivity, engagement, and warmth can help improve care by identifying opportunities for better interactions. We introduce a machine-learning pipeline embedded in ConverSense, a web application that visualizes communication patterns across visits. A user study with clinicians and patients highlights its potential to provide actionable, context-specific feedback for enhancing communication quality and patient outcomes.
CHI 2024
Implicit bias among healthcare providers can negatively impact care quality and patient outcomes, necessitating tools to identify and address these biases. Through design sessions with 24 primary care providers, we found they prefer feedback with transparent metrics, trends across visits, and actionable tips presented in a dashboard. These insights can guide the development of interactive systems to support equitable healthcare, especially for marginalized communities.
CHI 2024Through interviews, workshops, and a global survey, this study uncovered gaps between how contouring is taught and what residents need to learn effectively. While faculty often focus on efficiency, residents seek timely, varied, and cognitively rich feedback. Key challenges include limited support for sharing reasoning and balancing clinical with teaching responsibilities. Sociotechnical solutions are proposed to bridge these gaps, such as using senior learners for peer teaching and capturing cognitive insights through in-situ video feedback.
CHI 2023We present a prototype platform for tumor-contouring training that gives radiation-oncology residents overlap scores, visual cues for over- or under-contoured regions, and toxicity forecasts, enriching apprenticeship learning with timely, detailed feedback.
CHI 2023UnMapped merges a live 3-D “window” into the novice’s scene with a static 3-D replica of the expert’s own desk, letting mentors anchor instructions to familiar spatial landmarks. Compared with fully immersive views, this hybrid layout sped up remote guidance, reduced communication overhead, and eased expert workload.
CHI 2022 To understand the perspectives of clinicians on the design of effective educational strategies and for tools to help identify implicit bias, we conducted 21 semi-structured interviews with primary care clinicians about their perspectives and design recommendations for tools to improve patient-centered communication and to help mitigate implicit bias.