CodeA11y: Making AI Coding Assistants Useful for Accessible Web Development

February 15, 2025 Β· Declared Dead Β· πŸ› International Conference on Human Factors in Computing Systems

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Authors Peya Mowar, Yi-Hao Peng, Jason Wu, Aaron Steinfeld, Jeffrey P. Bigham arXiv ID 2502.10884 Category cs.HC: Human-Computer Interaction Cross-listed cs.SE Citations 19 Venue International Conference on Human Factors in Computing Systems Last Checked 4 months ago
Abstract
A persistent challenge in accessible computing is ensuring developers produce web UI code that supports assistive technologies. Despite numerous specialized accessibility tools, novice developers often remain unaware of them, leading to ~96% of web pages that contain accessibility violations. AI coding assistants, such as GitHub Copilot, could offer potential by generating accessibility-compliant code, but their impact remains uncertain. Our formative study with 16 developers without accessibility training revealed three key issues in AI-assisted coding: failure to prompt AI for accessibility, omitting crucial manual steps like replacing placeholder attributes, and the inability to verify compliance. To address these issues, we developed CodeA11y, a GitHub Copilot Extension, that suggests accessibility-compliant code and displays manual validation reminders. We evaluated it through a controlled study with another 20 novice developers. Our findings demonstrate its effectiveness in guiding novice developers by reinforcing accessibility practices throughout interactions, representing a significant step towards integrating accessibility into AI coding assistants.
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