Problem
Research questions and friction points this paper is trying to address.
E-textiles
Generative AI
Design bottlenecks
Co-design
Pain points
Mapping E-textiles Design Pain Points and Generative AI Opportunities: Insights from Workshops in Shanghai and Winchester
🤖 AI Summary
This study addresses persistent bottlenecks in electronic textile (e-textile) design, including procedural complexity, interdisciplinary collaboration challenges, data scarcity, and the disconnect between prototyping and manufacturing. Through collaborative design workshops conducted in Shanghai and Winchester, we mapped the design workflow using user journey maps to identify pain points and systematically evaluated the potential of generative AI (GenAI) to empower each stage. We propose a novel pipeline-stage-based GenAI role-mapping methodology, identifying four categories of domain-specific AI tools. Our core finding reveals that the primary barrier to AI adoption is not insufficient model capability but rather the absence of standardized, machine-readable representations. Furthermore, this work delineates critical operational obstacles, notably data scarcity and the inherent trade-offs involved in hardware integration.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
E-textiles
Generative AI
Co-design workshops
Machine-readable representations
Design pipeline
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