CodeA11y: Making AI Coding Assistants Useful for Accessible Web Development
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866929717712519168 |
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| author | Mowar, Peya Peng, Yi-Hao Wu, Jason Steinfeld, Aaron Bigham, Jeffrey P. |
| author_facet | Mowar, Peya Peng, Yi-Hao Wu, Jason Steinfeld, Aaron Bigham, Jeffrey P. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10884 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CodeA11y: Making AI Coding Assistants Useful for Accessible Web Development Mowar, Peya Peng, Yi-Hao Wu, Jason Steinfeld, Aaron Bigham, Jeffrey P. Human-Computer Interaction Software Engineering 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. |
| title | CodeA11y: Making AI Coding Assistants Useful for Accessible Web Development |
| topic | Human-Computer Interaction Software Engineering |
| url | https://arxiv.org/abs/2502.10884 |