user experience design

This study addresses the challenge faced by resource-constrained software startups lacking user experience (UX) expertise in efficiently developing user-centered minimum viable product (MVP) prototypes. To bridge this gap, the authors propose StartFlow, a lightweight method that uniquely integrates wireframes and user flows into a unified “wireflow” representation. StartFlow guides non-UX teams through a structured three-step process—feature organization, prototype construction, and closed-loop validation based on usability heuristics—to iteratively refine MVPs. Empirical results demonstrate that teams employing StartFlow produce prototypes that are clearer, better aligned with user stories and business rules, and exhibit significantly fewer usability flaws. Expert evaluations further confirm the method’s high usability and strong potential for broad adoption in early-stage software development contexts.

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 UI auto-design systems lack iterative refinement capabilities, primarily due to inaccurate design intent interpretation and opaque generation processes. To address this, we propose PrototypeAgent, a multi-agent framework enabling designers to drive prototype generation via natural language instructions and layout preferences. Its novel “intent clarification–dynamic alignment” mechanism enables real-time inference of implicit requirements and interactive correction of intermediate artifacts during generation, facilitating human-AI collaborative iteration. Technically, the framework integrates LLM prompt enhancement, a topic-guided agent coordination architecture, editable intermediate-result interfaces, and a multi-stage intent alignment evaluation module. Extensive experiments and user studies demonstrate that PrototypeAgent significantly outperforms state-of-the-art baselines in prototype fidelity, design accuracy, and output diversity.

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.

Existing UI prototyping tools provide weak support for integrating design artifacts such as screenshots and sketches, hindering component reuse, semantic integration, and cross-role collaboration. This paper proposes a novel UI prototyping paradigm grounded in Conceptual Blending Theory, the first to concretize cognitive-science-based blending mechanisms into an interactive tool. It enables semantic-level element mixing across heterogeneous design examples through example-driven component extraction and semantic alignment, lightweight vision–semantics mapping, and real-time blended preview—facilitating staged intent articulation by developers. An empirical study with 14 frontend developers demonstrates that the approach significantly reduces prototype initiation time (average improvement of 42%), stimulates highly unexpected creative combinations (68% novel composition rate), and enhances design–development collaboration efficiency.

This work addresses the challenges faced by resource-constrained software startup teams with limited user experience (UX) expertise in efficiently creating and evaluating low-fidelity prototypes. To this end, we propose SoftBoard, a web-based multi-agent system that integrates large language model–driven intelligent agents into the prototyping workflow for the first time, enabling an end-to-end pipeline from requirement elicitation to automated generation of low-fidelity prototypes. The system incorporates an embedded evaluation mechanism based on usability heuristic rules and unifies prototype editing, team collaboration, and AI-assisted functionalities within a single platform, substantially reducing reliance on specialized UX knowledge. Preliminary feasibility studies demonstrate that SoftBoard effectively standardizes and streamlines the minimum viable product (MVP) development process.

为了解决生成式用户界面难以直接控制设计意图的问题，GUIDE系统通过设计师的持续互动和修改来优化界面生成，并采用自适应一致性评分模型确保符合设计要求。

This study presents the first systematic empirical investigation into the dual performance of user interfaces prototyped by generative artificial intelligence (GenAI), specifically examining the trade-off between utility and creativity. Through a blind experiment involving 92 participants, the research employed the UEQ-S questionnaire to evaluate GenAI-generated prototypes against human-designed counterparts across dimensions of pragmatic quality (e.g., usability, efficiency) and hedonic quality (e.g., originality, novelty). Findings indicate that AI-generated prototypes received positive assessments on pragmatic attributes but scored neutrally or negatively on hedonic aspects. These results highlight current limitations of GenAI in balancing functional effectiveness with creative expression in interface design, offering empirical insights to inform future human-AI collaborative approaches in user experience development.

This study addresses the evaluation bottleneck caused by the high cost and limited scalability of real-user studies by proposing a role-based universal user simulation framework. By integrating role datasets with application interfaces to capture interaction trajectories, this method establishes a pluggable and parallelizable automated evaluation workflow. Empirical validation across diverse scenarios, including questionnaires, chatbots, and web applications, demonstrates that the framework supports plug-and-play testing and effectively overcomes traditional scalability limitations. Consequently, it generates reproducible, high-quality user feedback, offering an efficient and scalable paradigm for evaluating interactive applications.

本文提出Elicitive用户界面，通过生成引出技术来解决用户难以明确表达需求的问题，以更好地适应用户的偏好。