未见全貌：利用AI进行视障人士物理DIY-AT协同制作的挑战与机遇
Not Seeing the Whole Picture: Challenges and Opportunities in Using AI for Co-Making Physical DIY-AT for People with Visual Impairments
AI总结：
本文探讨了利用AI辅助视障人士进行物理DIY-AT协同制作的挑战与机遇，提出基于LLM的解决方案，强调空间与视觉支持及可访问性设计的重要性。
摘要：
现有的辅助技术（AT）往往采用一刀切的方法，忽视了视障人士（PVI）多样化的需求。DIY-AT工具包提供了一种定制化的途径，但大多数仍然有限——要么针对与工程师的协作设计，要么需要编程专业知识。残疾人中的非专业人士，包括PVI，也面临诸如工具不可用、缺乏信心和技术知识不足等障碍。这些差距凸显了需要原型技术来使PVI能够直接制作自己的辅助技术的必要性。基于新兴证据，大型语言模型（LLMs）不仅可以作为视觉辅助工具，还可以作为协作设计伙伴，我们提出了一个探索性研究，探讨基于LLM的AI如何支持PVI在实物DIY-AT协同制作过程中的作用。我们的发现揭示了关键挑战和设计机会：对更大空间和视觉支持的需求、缓解新型AI错误的策略，以及对更可访问的AI辅助原型设计的启示。
英文摘要：
Existing assistive technologies (AT) often adopt a one-size-fits-all approach, overlooking the diverse needs of people with visual impairments (PVI). Do-it-yourself AT (DIY-AT) toolkits offer one path toward customization, but most remain limited--targeting co-design with engineers or requiring programming expertise. Non-professionals with disabilities, including PVI, also face barriers such as inaccessible tools, lack of confidence, and insufficient technical knowledge. These gaps highlight the need for prototyping technologies that enable PVI to directly make their own AT. Building on emerging evidence that large language models (LLMs) can serve not only as visual aids but also as co-design partners, we present an exploratory study of how LLM-based AI can support PVI in the tangible DIY-AT co-making process. Our findings surface key challenges and design opportunities: the need for greater spatial and visual support, strategies for mitigating novel AI errors, and implications for designing more accessible AI-assisted prototypes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
