Smart medication systems increasingly automate medication recognition, reminders, and logging. However, automation in home medication routines should be carefully bounded, as users may have different capabilities, privacy expectations, and needs for control over decisions. We present a mixed-methods study of a Smart Medication Support system comparing three automation conditions: confirmation required, automatic logging with undo, and fully automatic support. Across 53 participants and interviews with 11 older adults, we found that higher automation did not necessarily lead to higher trust or acceptance. Participants preferred automation that reduced routine effort while preserving opportunities for correction. Fully automatic support was less interruptive but was rated lower in autonomy, trust, transparency, dignity, and satisfaction. Interviews also showed clear differences among older adults. Their preferences were shaped by privacy concerns, digital confidence, perceived vulnerability, and caregiver involvement. We contribute empirical evidence and design implications for calibrating automation in smart medication systems according to task risk, user control, and ethical acceptability.
Large language model personas are increasingly used to approximate diverse users during early-stage visualization design, but it remains unclear whether persona-conditioned outputs reflect stable personality effects or artifacts of model choice and task framing. We examine this q…
Bowed string instruments demand fine-grained bodily coordination that is typically taught through visual demonstration, creating persistent barriers for musicians with blindness and low-vision (BLV). To understand these challenges and explore new design opportunities, we conducte…
As large language models (LLMs) become integrated into programming education, learner-facing systems increasingly differ in how that assistance is bounded, enacted, and controlled. These governance decisions are often described implicitly, making it difficult to compare systems i…
Large Language Models (LLMs) show promise for medical education, but most existing systems focus on localized interactions such as question answering or single-turn feedback, rather than organizing an entire clinical case into a decision-centered learning trajectory. We introduce…
Navigating the deluge of heterogeneous medical data, from academic literature (PubMed) to clinical guidelines (Web) and private knowledge bases, remains a critical bottleneck for evidence-based medicine. While commercial black-box tools lack transparency, standard open-source RAG…
We investigate how to support authoring narrative visualizations in motion in sports videos, drawing on automated data preparation, systematic analysis, technology probe design, and evaluation, using swimming races as a case study. Sports videos are widely broadcast and shared ac…