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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.02110 |
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| _version_ | 1866913774613561344 |
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| author | Zhong, Mingyuan Chen, Ruolin Chen, Xia Fogarty, James Wobbrock, Jacob O. |
| author_facet | Zhong, Mingyuan Chen, Ruolin Chen, Xia Fogarty, James Wobbrock, Jacob O. |
| contents | Many mobile apps are inaccessible, thereby excluding people from their potential benefits. Existing rule-based accessibility checkers aim to mitigate these failures by identifying errors early during development but are constrained in the types of errors they can detect. We present ScreenAudit, an LLM-powered system designed to traverse mobile app screens, extract metadata and transcripts, and identify screen reader accessibility errors overlooked by existing checkers. We recruited six accessibility experts including one screen reader user to evaluate ScreenAudit's reports across 14 unique app screens. Our findings indicate that ScreenAudit achieves an average coverage of 69.2%, compared to only 31.3% with a widely-used accessibility checker. Expert feedback indicated that ScreenAudit delivered higher-quality feedback and addressed more aspects of screen reader accessibility compared to existing checkers, and that ScreenAudit would benefit app developers in real-world settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02110 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ScreenAudit: Detecting Screen Reader Accessibility Errors in Mobile Apps Using Large Language Models Zhong, Mingyuan Chen, Ruolin Chen, Xia Fogarty, James Wobbrock, Jacob O. Human-Computer Interaction Artificial Intelligence Many mobile apps are inaccessible, thereby excluding people from their potential benefits. Existing rule-based accessibility checkers aim to mitigate these failures by identifying errors early during development but are constrained in the types of errors they can detect. We present ScreenAudit, an LLM-powered system designed to traverse mobile app screens, extract metadata and transcripts, and identify screen reader accessibility errors overlooked by existing checkers. We recruited six accessibility experts including one screen reader user to evaluate ScreenAudit's reports across 14 unique app screens. Our findings indicate that ScreenAudit achieves an average coverage of 69.2%, compared to only 31.3% with a widely-used accessibility checker. Expert feedback indicated that ScreenAudit delivered higher-quality feedback and addressed more aspects of screen reader accessibility compared to existing checkers, and that ScreenAudit would benefit app developers in real-world settings. |
| title | ScreenAudit: Detecting Screen Reader Accessibility Errors in Mobile Apps Using Large Language Models |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2504.02110 |