human-computer interaction

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 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.

Empirical usability research on mobile applications remains scarce, hindering systematic understanding of critical usability challenges. Method: We conducted semi-structured interviews with 12 industry experts, followed by thematic coding and consensus analysis to identify core usability issues and emerging research directions. Contribution/Results: We systematically delineate five fundamental usability dimensions—information architecture, interface design, performance, interaction patterns, and aesthetics—and, for the first time, articulate five frontier research avenues: AI-driven applications, AR/VR integration, multimodal interaction, personalized mobile ecosystems, and accessibility. This work bridges industrial insights with academic agendas, yielding a comprehensive usability problem landscape and an actionable research roadmap. It advances human–computer interaction (HCI) theory while providing bidirectional support for industrial design practice.

Current HCI design lags behind advances in mobile computing, cloud platforms, and IoT; 92% of existing guidelines lack support for location-based services and cross-device continuous interaction, while the desktop-centric paradigm fails to meet context-awareness and inclusivity requirements for older adults, children, and users with disabilities. Method: This study systematically reviews 50 state-of-the-art publications and pioneers an integrated approach combining agile development with human-centered design to construct a dynamic, adaptive HCI framework for multi-device environments and diverse user populations. It incorporates human factors engineering, context-aware modeling, and mixed-methods research, empirically validating the framework within cloud-native information systems. Contribution/Results: We propose an extensible set of design principles, achieving a 37% improvement in task completion rate and user satisfaction across three representative usage scenarios.

Existing visualization research predominantly focuses on *how to use* interactive features, neglecting the critical question of *how to construct* them. Method: We propose the first three-layer decoupled interaction authoring task model—intent–technique–component—derived from empirical coding and abstraction of 592 interaction units across 47 real-world applications. Contribution/Results: This model provides descriptive, evaluative, and generative capabilities, enabling the first unified formalization of interaction authoring intent, technical implementation, and component instantiation. It yields a reusable, theory-grounded classification framework that supports critical evaluation of existing visualization tools and informs the design and validation of next-generation low-code interaction authoring systems.

This work addresses the lack of explicit modeling of abstraction mechanisms in existing interactive system design, which hinders actionable design guidance. Through a systematic review of 457 publications, the study proposes the first abstraction-centered design space for interactive systems, structured around six core dimensions. Leveraging this framework, it reconceptualizes the Gulf of Execution and Evaluation model to reveal the cognitive and design mechanisms by which users and systems bridge the abstraction gap. By explicitly integrating abstraction into the theoretical foundations of human–computer interaction, this research synthesizes prior work, establishes a coherent theoretical basis, and offers systematic practical guidance for designing and evaluating abstraction mechanisms in interactive systems, thereby charting new directions for future inquiry.

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 challenge of transforming unstructured feedback from simulated user agents in usability testing into actionable user experience insights. To this end, it introduces UXCascade, an interactive analysis tool that pioneers a multi-level analytical framework integrating user personas, task objectives, and usability issues to link agent reasoning traces with specific interface problems. The system supports exploratory analysis—from macro-level patterns to micro-level refinements—through structured overviews, reasoning trace visualization, annotation views, and interactive editing capabilities. A user study demonstrates that UXCascade effectively integrates into existing UX workflows, facilitating rapid iteration during early design stages and yielding high-value, actionable feedback.

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.

This study addresses the current lack of a systematic understanding of user interaction mechanisms with large language model–driven computer-use agents and the key design factors influencing their user experience (UX). Through a two-stage approach, the authors construct and empirically validate a UX design space for such agents. First, they synthesize findings from a literature review and expert interviews to develop a taxonomy encompassing dimensions such as user prompting, explainability, and user control. Second, they conduct a Wizard-of-Oz experiment across normal, error, and high-risk scenarios to observe user behaviors, revealing interdependencies among design dimensions and the diversity of user needs. This work presents the first systematically formulated and empirically validated UX design framework for LLM-driven agents, offering developers a structured and actionable foundation for design decisions.