AI开发者工具中的视觉可访问性问题表征：一项实证研究
Characterizing Visual Accessibility Issues in AI Developer Tools: An Empirical Study
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中文总结 AI 辅助
本研究通过分析五个AI开发者工具生态系统的问题报告，表征了其中的视觉可访问性障碍类型及差异，揭示了交互设计与生态系统实践对工具可访问性记录的影响。
中文摘要 AI 辅助
AI辅助开发者工具日益通过聊天面板、终端智能体、生成的差异文件（diffs）和流式状态输出介导编程过程，这些交互界面可能会给盲人、低视力及色觉缺陷开发者造成视觉可访问性障碍，但目前人们对这类障碍在公共工具生态系统中的报告情况知之甚少。我们分析了五个AI开发者工具生态系统中的问题及论坛讨论：VS Code中的GitHub Copilot、Cursor、Claude Code、OpenAI Codex和OpenCode。从2652个经关键词检索得到的候选内容中，采用三模型集成方法识别出600份经一致确认的视觉可访问性报告，分层人工合理性检查验证了这一保守选择的合理性。主题建模和定性分析确定了三类反复出现的问题：屏幕阅读器及辅助技术障碍；视觉呈现、对比度和区分度问题；AI特定界面的可读性、缩放和控制限制。这些问题的相对突出程度在不同生态系统中存在差异，反映了编辑器、终端、聊天、差异文件（diffs）和智能体交互界面的差异。探索性元数据分析进一步确定了报告者活动的差异，以及在基于GitHub的生态系统中维护者参与和关闭流程的差异。这些发现表明，AI开发者工具的可访问性记录既受其交互设计的影响，也受其周边生态系统的报告和维护实践的影响。
英文摘要
AI-assisted developer tools increasingly mediate programming through chat panels, terminal agents, generated diffs, and streaming status output. These interaction surfaces may create visual accessibility barriers for blind, low-vision, and color-vision-deficient developers, yet little is known about how such barriers are reported in public tool ecosystems. We analyze issues and forum discussions from five AI developer tool ecosystems: GitHub Copilot in VS Code, Cursor, Claude Code, OpenAI Codex, and OpenCode. From 2,652 keyword-retrieved candidates, a three-model ensemble identified 600 unanimously positive visual accessibility reports. A stratified manual sanity check supported this conservative selection. Topic modeling and qualitative analysis identified three recurring categories: screen-reader and assistive-technology barriers; visual presentation, contrast, and differentiation problems; and readability, scaling, and control limitations in AI-specific interfaces. The relative prominence of these concerns varied across ecosystems and reflected differences in editor, terminal, chat, diff, and agent interaction surfaces. An exploratory metadata analysis further identified differences in reporter activity and, across the GitHub-based ecosystems, maintainer participation and closure processes. These findings show that the accessibility record of AI developer tools is shaped by both their interaction design and the reporting and maintenance practices of their surrounding ecosystems.
