CodeA11y: Making AI Coding Assistants Useful for Accessible Web Development
February 15, 2025 Β· Declared Dead Β· π 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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