SoftBoard：用于创建和评估低保真原型的多智能体工具
SoftBoard: A Multi-Agent Tool for the Creation and Evaluation of Low-Fidelity Prototypes
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中文总结 AI 辅助
针对软件初创公司环境下数字产品用户体验问题，介绍SoftBoard工具，它集成原型编辑器、团队项目组织和基于大语言模型的多智能体系统，可支持需求获取、自动生成原型及评估界面质量，助力构建符合用户需求的MVP。
中文摘要 AI 辅助
用户体验（UX）是数字产品成功的关键因素，尤其是在软件初创公司，这类环境存在时间限制、资源有限和设计实践成熟度低的问题。通过低保真原型构建最小可行产品（MVP）是一种成熟的低成本快速验证策略。然而，现有工具生态系统存在差距，如通用原型解决方案占主导、缺乏综合方法指导以及设计过程中人工智能应用初期。本文介绍了SoftBoard，这是一个基于网络的工具，用于在MVP开发中创建和评估低保真原型。该工具集成了原型编辑器、团队项目组织和基于大语言模型的多智能体系统，支持需求获取与细化、自动生成原型并根据可用性启发式评估界面质量。这种集成旨在减少对先前UX专业知识的依赖，规范原型制作过程，并支持团队构建符合用户需求的MVP。目前正在与软件专业人员进行可行性研究。
英文摘要
User Experience (UX) is recognized as a critical factor for the success of digital products, particularly in software startups, environments marked by time constraints, limited resources, and low maturity in design practices. Building Minimum Viable Products (MVPs) through low-fidelity prototyping represents a well-established strategy for rapid validation cycles at reduced cost. A systematic literature mapping, however, revealed gaps in the ecosystem of available tools: a predominance of general-purpose solutions adapted for prototyping, the absence of integrated methodological guidance, and the incipient use of Artificial Intelligence in the design process. This paper presents SoftBoard, a web-based tool for the creation and evaluation of low-fidelity prototypes in the context of MVP development. The tool integrates a prototype editor, team-based project organization, and a multi-agent system based on large language models that supports requirements elicitation and refinement, automates prototype generation, and evaluates interface quality based on usability heuristics. This integration aims to reduce reliance on prior UX expertise, standardize the prototyping process, and support teams in building MVPs aligned with user needs. As future work, a feasibility study with software professionals is currently underway.
