论文分析了五种AI编程工具的可访问性问题，发现Copilot、Cursor等对视觉障碍者的支持差异明显，做无障碍开发的人值得看。
一项针对 GitHub Copilot、Cursor、Claude Code、OpenAI Codex 和 OpenCode 五类 AI 开发者工具的研究，从 2652 个候选讨论中识别出 600 个视觉可访问性问题报告。研究通过三模型集成和分层人工检查筛选，归纳出屏幕阅读器障碍、视觉呈现与对比度问题、AI 特定界面可读性等三类主要问题。不同生态系统的编辑器、终端、聊天、diff 和智能体界面中，可访问性问题的分布存在明显差异。基于 GitHub 的生态系统中，维护者参与度和关闭流程也影响问题的记录与修复。
Characterizing Visual Accessibility Issues in AI Developer Tools: An Empirical Study
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.