user experience

This study addresses the limitations of traditional handcrafted user personas, which are often abstract, costly to produce, and difficult to translate into actionable design features, thereby hindering their practical utility in product design. To overcome these challenges, this work proposes the first interactive system grounded in multimodal large language models (MLLMs) that integrates demographic data with an interactive interface to enable designers to generate fine-grained user personas. The system further automatically derives and restructures these personas into structured design features, achieving an end-to-end transformation from abstract representations to concrete design elements. In an evaluation with twelve professional designers, the approach significantly outperformed a chat-based baseline in terms of persona engagement, perceived transparency, and user satisfaction.

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.

This study investigates whether large language models (LLMs) can bridge the gap between UX experts and non-experts in authoring user scenarios. In a controlled experiment, both groups authored scenarios with LLM assistance; outputs were evaluated via mixed methods—structured scoring and qualitative coding—assessing structural completeness, expressive clarity, and audience orientation. Results demonstrate, for the first time empirically, that LLMs significantly enhance non-experts’ performance: their scenarios achieve structural and clarity levels comparable to experts’, and—remarkably—surpass experts in articulating user perspectives. The findings validate LLMs as effective, democratized tools for requirements analysis and reveal their unique capacity to augment empathic user-centered expression. This work advances accessible UX practice by lowering barriers to rigorous scenario-based design.

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.

This work addresses the challenge that open-source software developers often struggle to empathize with users due to a lack of contextual background, while existing issue-tracking tools prioritize technical details over user perspectives. To bridge this gap, the authors propose PersonaFlow—a lightweight, extensible tool that automatically generates editable user personas from repository artifacts such as issues and pull requests, seamlessly integrating them into the issue-reporting interface. A user study (N=13) demonstrates that PersonaFlow effectively fosters user-centered developer behaviors: most participants revised their understanding of reported issues, and more than half proactively incorporated empathetic language, tailored explanations, or elevated issue priority in their responses. These findings validate the efficacy of simultaneously supporting affective connection and pragmatic decision-making in developer workflows.

This study addresses the challenges in user experience research (UXR)—notably, the lack of methodological rigor and protracted workflows that hinder timely support for product decisions, compounded by the added burdens of prompt engineering, data preprocessing, and result validation when applying generative AI. To overcome these limitations, this work proposes a structured approach that systematically integrates generative AI with a UXR point-of-view (PoV) construction framework. Leveraging Google NotebookLM, the method introduces five context-specific, phased prompts designed to collaboratively distill evidence-driven strategic insights across the four stages of PoV development. Evaluation on eleven UXR reports demonstrates that this approach effectively positions AI as an efficient collaborator, significantly enhancing both the efficiency and feasibility of UXR practices.

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 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.