user interfaces

HCI research suffers from numerous context-dependent, non-replicable empirical findings. To address this, we propose *Interaction Cycle Diffraction*—the first method to formalize and compare user interaction behavior across experimental conditions using *interactional properties* (e.g., feedback latency, action reversibility, or mode-switching cost) as fundamental analytical units, rather than interface morphology. This framework systematically enables identification, extraction, and validation of reproducible interactional properties across diverse prototypes, technologies, tasks, and user populations. Through iterative user studies and prototype refinement, we demonstrate its utility in continuously optimizing design workflows and accumulating reusable empirical knowledge. Our work establishes the first reproducibility framework for interactional properties in ubiquitous UIs, offering a novel paradigm for building a theoretical taxonomy and empirical foundation for an interaction science. (138 words)

Current VR user interfaces lack standardized, actionable design guidelines, hindering immersion, usability, comfort, and accessibility. Method: We conducted a systematic literature review synthesizing interdisciplinary empirical evidence to develop the first comprehensive best-practice framework for VR UI design, spanning interaction, visual, spatial, and human factors dimensions. We further implemented an open-source demonstrator application—FlUId—that illustrates design trade-offs through comparative interactive scenarios. Contribution/Results: Validated via multi-round user studies (N=48), the framework significantly improved task completion rate (+32%), subjective comfort (p<0.01), and perceived interface consistency. This work bridges a critical gap between theoretical research and practical implementation in VR UI design, supporting prototype development in education and rehabilitation domains, and providing an evidence-based foundation for future industry standards.

This study addresses the underexplored role of UI/UX designers in organizational privacy practices, a dimension often overlooked in favor of developer-centric approaches. Through semi-structured interviews with twelve privacy-advocating UI/UX designers and subsequent thematic analysis, the research systematically investigates their privacy-related perceptions, influencing factors, cross-functional collaboration challenges, and coping strategies. It reveals, for the first time, how designers navigate tensions among business objectives, technical constraints, and team dynamics through value-driven and adaptive approaches. The study identifies how individual characteristics and organizational contexts shape privacy advocacy, elucidates mechanisms of friction in interdisciplinary collaboration, and proposes designer-centered pathways for organizational change alongside actionable tooling recommendations. These findings offer both theoretical insights and practical foundations for fostering privacy-supportive design ecosystems.

This study addresses the lack of systematic and actionable guidance for privacy protection in current UI/UX design practices, which has led to fragmented implementation. Through a comprehensive literature review and semi-structured interviews with 15 domain experts, complemented by thematic analysis and multiple rounds of practitioner validation—including workshops and online surveys—the research identifies 14 core privacy design principles and four key contextual dimensions. Building on these findings, the work presents the first structured pattern catalog for UI/UX privacy design, offering a theoretically grounded yet practically applicable resource. This catalog equips designers with empirically validated tools to effectively support privacy-by-design approaches in interface development.

Current HCI research overemphasizes interface appearance while neglecting the observability and reproducibility of actual interaction processes. To address this, we propose an interaction-behavior-centered research paradigm, establishing a standardized observational framework spanning technologies, design approaches, and user tasks. Our method employs experiment-driven application prototyping and empirical observation to systematically extract, validate, and accumulate reproducible interaction characteristics. The contributions are threefold: (1) the first ontology-oriented observational framework for human–computer interaction, enabling multi-condition reproducibility and integrative analysis; (2) tight coupling of user task requirements with technological evolution to support dynamic extraction and validation of interaction attributes; and (3) a scalable methodology and empirical foundation for uncovering fundamental principles governing interactive behavior. This paradigm shifts focus from static interface artifacts to observable, measurable, and repeatable interaction phenomena—thereby advancing HCI as an evidence-based science.

Traditional GUI usability evaluation relies heavily on expert reviews and user testing, which are costly and inefficient, while existing computational agents struggle to accurately assess usability. This work proposes uxCUA—a machine learning–based computational user agent that, for the first time, integrates computable usability metrics with large-scale, labeled UI interaction data to enable end-to-end prediction of usability scores. By prioritizing interaction flows and simulating human-like operations, uxCUA generates fine-grained and credible usability critiques. Notably, it achieves higher evaluation accuracy than larger-scale models and demonstrates effectiveness on both synthetic and real-world GUI interfaces.

This work addresses the limitations of traditional static accessibility standards in handling dynamic challenges posed by user-generated content—such as blurry images, missing descriptions, and disorganized layouts—which often hinder accessibility for people with visual impairments, low vision, or age-related needs. To bridge this gap, the authors propose a “generative user interface” approach that dynamically restructures interfaces at runtime to accommodate diverse user requirements. The method employs three key interventions: real-time HTML regeneration, conversational guidance, and audio-assisted photography. By shifting the designer’s role from layout implementation to strategy formulation, this approach effectively mitigates coverage gaps in existing accessibility standards. Evaluated on a consumer-to-consumer (C2C) e-commerce platform, the framework significantly enhances accessibility for heterogeneous user groups and expands the application frontier of generative UI within human-computer interaction.

This study addresses the challenge that users often lack effective support in recognizing and evaluating personalization opportunities within self-directed interface customization, leading to underutilization of available features. To bridge this gap, the paper proposes a “reflexive personalization” approach that guides users to reflect on their own interaction data, thereby enhancing their awareness of personalization value, facilitating trade-off assessments between benefits and effort, and improving the transparency of system-generated suggestions. Through an exploratory design probe employing experimental scenario scripts, semi-structured interviews, and qualitative analysis with twelve participants, the research demonstrates that while users can independently identify personalization opportunities, they strongly prefer system-provided visual recommendations. Interaction data significantly strengthens users’ willingness to change, heightens their perception of data value, and refines their personalization decision-making process.