rapid prototyping

In early conceptual design, motion behavior validation is inefficient, hand-drawn sketches inadequately capture dynamic characteristics, and conversion to CAD/CAE models is time-consuming. To address these challenges, this paper proposes a rapid modeling and real-time simulation method for mechanism motion based on interactive sketching. The method integrates lightweight sketch recognition, automatic topological parsing, and 2D kinematic solving, enabling natural digitalization of mechanism structures and millisecond-level motion feedback within a 2D sketching environment. Its key innovation lies in embedding motion constraints directly into the sketching process, supporting immediate dynamic validation and iterative refinement of design intent. User studies demonstrate a 77% reduction in cognitive load compared to conventional workflows, along with significant improvements in design communication efficiency and user satisfaction—thereby validating the method’s effectiveness and usability in early-stage conceptual design.

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.

Designers often struggle to effectively apply insights from human-computer interaction (HCI) research due to challenges in retrieval, disciplinary terminology barriers, and a lack of contextualized, actionable guidance. To address this gap, this work proposes ReFinE—the first Figma plugin that automatically synthesizes HCI research findings and integrates them directly into the UI design workflow. Leveraging natural language processing and context-aware recommendation techniques, ReFinE delivers real-time, visual, and actionable design suggestions tailored to the designer’s current task. Findings from a user study demonstrate that ReFinE significantly reduces cognitive load, enhances designers’ ability to incorporate empirical research evidence into their practice, improves design quality, and accelerates iterative prototyping cycles.

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 challenge in iterative interaction design where the benefits of prototype refinement are often difficult to balance against diverse and asymmetric fabrication costs, thereby limiting effective exploration of the design space. We propose the first approach that explicitly incorporates designers’ estimated prototype costs into a Bayesian optimization framework by lightly modifying the acquisition function to enable cost-aware sampling. Our method achieves comparable utility at approximately 70% of the cost or, under strict budget constraints, delivers up to three times the performance of baseline methods. A user study with twelve participants further confirms its significant advantages in real-world design tasks, enhancing both the practicality and efficiency of Bayesian optimization for human-computer interaction prototyping.

Existing generative UI (GenUI) tools rely on unstructured prompts, depth-first exploration strategies, and high-fidelity outputs, which hinder effective support for early-stage UI design exploration. This work proposes a contrastive GenUI approach that employs structured inputs, breadth-first exploration, and low-fidelity generation. Through controlled experiments—the first to systematically evaluate the impact of input structure, exploration strategy, and output fidelity on early design—we assess this paradigm using large language model–generated prototypes and user studies. Involving 24 UX designers and product managers, our findings indicate that structured inputs enhance visibility across design dimensions yet raise usability barriers; breadth-first exploration expands the creative solution space but introduces challenges in multi-screen preview management; and expert users still favor high-fidelity outputs. The study elucidates key trade-offs and contextual applicability among different GenUI paradigms.

This study addresses the fragmented workflow experienced by UI/UX designers due to the lack of seamless integration between paper-based and digital prototyping. Through semi-structured interviews with 19 professional designers and nine mixed reality (MR) concept probe experiments, the research systematically investigates how MR can bridge these two media to create a continuous, collaborative, and spatially anchored hybrid design environment. The work proposes four key dimensions for future MR design systems, revealing for the first time MR’s potential to reconcile the divide between analog sketching and digital prototyping. It further envisions innovative capabilities such as AI-augmented assistance, dynamic content synchronization, and unified artifact management. Findings indicate that MR significantly reduces manual reconstruction efforts, enables real-time cross-medium collaboration, and elicits strong designer enthusiasm for integrated MR authoring tools.

研究通过开发虚拟环境GraphXplore解决设计师在多模型间导航和信息检索时遇到的认知负担问题，与传统屏幕设置相比，GraphXplore在可用性和认知负荷上表现出优势。

This study addresses the challenge faced by indie game startups in acquiring user data for continuous experimentation during early development due to resource constraints. Employing an exploratory qualitative approach, we conducted in-depth interviews and case analyses with ten independent game developers. Based on these findings, we propose a pre-release continuous experimentation framework encompassing key components, along with tailored experimental strategies spanning from ideation to content production. This research not only provides systematic implementation guidelines for indie game teams operating without robust data support, but also offers practically valuable insights into user experience validation applicable to general software startups.

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。