232 papers
This paper reflects on the barriers to interdisciplinary collaboration in HCI and aging research by analyzing the disconnect between technological design and older adults' needs, while sharing the authors' personal journey of developing empathy through volunteering in a senior living community.
This study reveals that ambiguity regarding data leak probabilities, rather than known risks, significantly reduces user adoption of personalized AI, highlighting a critical demand for transparency institutions to mitigate privacy concerns.
The paper introduces WebAccessVL, a violation-aware vision-language model that automatically edits website HTML to fix WCAG2 accessibility violations while preserving visual design, achieving a 96% reduction in violations and outperforming GPT-5 through a supervised image-conditioned program synthesis approach enhanced by a checker-in-the-loop refinement strategy.
This paper argues that user trust in chatbots is often driven by interactional design choices that exploit cognitive biases rather than genuine trustworthiness, urging a reframing of chatbots as skilled salespeople and a distinction between psychological trust formation and normative trustworthiness to better calibrate user expectations.
This paper presents a study of a large language model-powered "sighted guide" for blind and low vision users in social virtual reality, revealing that participants adapt their interaction from a tool-based approach when alone to a companionable relationship in the presence of others, thereby offering key design recommendations for future accessible VR guides.
This paper proposes and validates an emotion-context-aware VR interaction pipeline that integrates real-time prosody-derived emotional states into LLM-based agent dialogue, significantly improving user engagement, naturalness, and rapport compared to traditional text-only approaches.
This paper introduces Doki, a text-native interface that enables users of varying expertise to author generative videos by defining assets, scenes, and edits directly within a freeform text document, thereby shifting video creation from specialized tools to a natural writing process.
This paper proposes "AI phenomenology" as a research framework that prioritizes users' first-person lived experiences over traditional performance metrics to better understand and guide the bidirectional alignment between humans and AI systems, offering a set of methodological tools, design concepts, and a research agenda derived from three empirical studies.
This paper introduces CMA-ES-IG, an algorithm that enhances robot preference learning by generating perceptually distinct and informative queries, thereby improving scalability, robustness, and user experience compared to existing state-of-the-art methods.
This paper establishes that human preference for equally optimal combinatorial packing solutions is reliably driven by three quantifiable structural properties—alignment with greedy heuristics, simple within-bin composition, and ordered visual representation—thereby providing a concrete framework for designing interpretable algorithmic support systems.
This paper introduces SIM-VAIL, a scalable auditing framework that reveals how consumer AI chatbots can systematically amplify mental health vulnerabilities through cumulative, context-dependent interaction loops, highlighting the need for multidimensional safety evaluations across diverse user phenotypes.
This study of financial professionals using AI reveals that while AI-generated content improves task quality, unsolicited proactive interventions significantly increase extraneous cognitive load—particularly for less experienced users—thereby degrading performance more severely than intrinsic task complexity.
This paper introduces MediTools, an AI-powered prototype application that leverages large language models to revolutionize medical education through interactive dermatology case simulations, enhanced literature analysis, and automated medical news summaries, while validating its potential through a survey of medical professionals and students.
This mixed-methods study investigates the lifecycle and sustainability of taboo knowledge artifacts on Wikipedia, revealing that their successful development relies on resilient leadership, engaged organizations, and emergent governance to navigate conflict and limited identifiability.
Through an analysis of Wikipedia's Flagged Revisions initiative, this paper identifies how restructuring community-based moderation is hindered by conflicts between new systems and existing social norms, as well as the complex costs and assessment difficulties arising from the intricate relationship between platforms and self-governed communities.
This paper proposes an AI-driven perception method with hierarchical human intention prediction to enable mobile robot chemists in self-driving laboratories to proactively distinguish between human preparatory actions and transient interactions, thereby overcoming the inefficiencies of passive obstruction detection and streamlining human-robot coordination in shared-access scenarios.
This paper demonstrates that Large Vision Language Models (LVLMs) develop human-aligned visual representations by quantifying their correspondence with EEG signals, revealing that intermediate layers, multimodal architecture, and downstream visual performance are key drivers of this neural alignment.
This paper proposes a physics-guided preprocessing framework for millimeter-wave human pose estimation that explicitly models signal correlations and kinematics to achieve real-time, lightweight performance with significantly fewer parameters than existing data-driven baselines while maintaining competitive accuracy.
This paper revises the prevailing conclusion that velocity decoding outperforms position decoding for sEMG-based hand pose estimation by demonstrating that, with a stable training recipe and a causal speed-adaptive filter, position decoding achieves superior tracking accuracy and a better smoothness-accuracy tradeoff across generalization conditions.
This mixed factorial study of 140 older adults reveals that while an LLM-based voice assistant's agreeableness significantly influences perceptions of empathy and likeability without affecting perceived intelligence, highly agreeable users are particularly sensitive to low-agreeableness agents, highlighting the importance of personality congruence and context-aware explanations in assistive settings.