发表机构
Aalto University; Tongji University; University of Southampton（阿尔托大学; 同济大学; 南安普顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
通过在上海和温彻斯特举办工作坊，本研究映射了电子纺织品设计的痛点，发现主要障碍是缺乏标准化机器可读表示，并提出了四类领域定制的GenAI工具以支持设计过程。
AI 中文摘要
电子纺织品设计涉及材料、传感器和执行器结构、制造、服装集成以及数据处理等复杂决策。该过程通常需要迭代式原型制作和测试，耗时且劳动密集，而很少有从业者具备跨所有相关领域的跨学科专业知识。为识别当前瓶颈并探索生成式人工智能（GenAI）如何支持设计过程，我们分别在上海和温彻斯特举办了两次半天的协同设计工作坊，参与者来自材料科学、电子学、服装设计、人机交互和制造领域。上海工作坊有二十名从业者参与；其中十人具有电子纺织品相关经验，构成此处分析的贡献样本。另有十名从业者参加了温彻斯特工作坊。参与者绘制了自己的设计流程，标注了瓶颈，并提出了GenAI可以提供支持的环节。我们没有呈现一个排序的机会列表，而是报告了一个过程图，将每个提议的GenAI角色索引到从业者所定位的流程阶段，并附上他们认为其有用性所依赖的条件。在两个地点，从业者一致识别出特定领域的操作障碍，包括数据稀缺、原型制作与制造之间的脱节，以及材料-硬件集成中的权衡。他们还强调，GenAI驱动的电子纺织品设计的主要障碍不是通用模型能力，而是缺乏标准化、机器可读的电子纺织品设计表示。基于这些发现，我们识别出四类领域定制的AI工具，可支持未来的电子纺织品设计过程。
英文摘要
E-textile design involves complex decisions across materials, sensor and actuator structures, fabrication, garment integration, and data processing. It typically requires iterative prototyping and testing, which are time- and labour-intensive, while few practitioners possess cross-disciplinary expertise across all relevant domains. To identify current bottlenecks and explore how Generative AI (GenAI) might support the design process, we conducted two half-day co-design workshops, one in Shanghai and one in Winchester, with practitioners from materials science, electronics, garment design, human-computer interaction, and manufacturing. Twenty practitioners participated in the Shanghai workshop; ten of them had prior experience in e-textiles and form the contributing sample analysed here. A further ten practitioners participated in the Winchester workshop. Participants mapped their own design pipelines, annotated bottlenecks, and proposed where GenAI could provide support. Rather than presenting a ranked list of opportunities, we report a process map that indexes each proposed GenAI role to the pipeline stage at which practitioners located it, together with the conditions on which they stated its usefulness would depend. Across both sites, practitioners consistently identified domain-specific operational barriers, including data scarcity, the disconnect between prototyping and manufacturing, and trade-offs in material-hardware integration. They also emphasized that the primary barrier to GenAI-driven e-textile design is not general model capability, but the lack of standardized, machine-readable representations of e-textile designs. Based on these findings, we identify four classes of domain-tailored AI tools that could support future e-textile design processes.
Comments10 pages, 2 figures, 2 tables
