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LLM-Driven Optimization of HTML Structure to Support Screen Reader Navigation
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Online interactions and e-commerce are commonplace among BLV users. Despite the implementation of web accessibility standards, many e-commerce platforms continue to present challenges to screen reader users, particularly in areas like webpage navigation and information retrieval. We investigate the difficulties encountered by screen reader users during online shopping experiences. We conducted a formative study with BLV users and designed a web browser plugin that uses GenAI to restructure webpage content in real time. Our approach improved the header hierarchy and provided correct labeling for essential information. We evaluated the effectiveness of this solution using an automated accessibility tool and through user interviews. Our results show that the revised webpages generated by our system offer significant improvements over the original webpages regarding screen reader navigation experience. Based on our findings, we discuss its potential usage as both a user and developer tool that can significantly enhance screen reader accessibility of webpages.
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Task Mode: Dynamic Filtering for Task-Specific Web Navigation using LLMs
An LLM-powered browser extension that filters webpages to task-relevant content reduced screen reader users' task completion time by about half in a 12-participant study.
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