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CodeA11y: Making AI Coding Assistants Useful for Accessible Web Development

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arxiv 2502.10884 v1 pith:VYA5TBJ5 submitted 2025-02-15 cs.HC cs.SE

Peya Mowar , Yi-Hao Peng , Jason Wu , Aaron Steinfeld , Jeffrey P. Bigham This is my paper
classification cs.HCcs.SE
keywords accessibilitydeveloperscodingassistantscodenoviceaccessibility-compliantaccessible
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 3 Pith papers

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  1. Exploring the Challenges and Opportunities of AI-assisted Codebase Generation

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  3. A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications

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