Enabling Uniform Computer Interaction Experience for Blind Users through Large Language Models
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Abstract (arXiv)
Blind individuals, who by necessity depend on screen readers to interact with computers, face considerable challenges in navigating the diverse and complex graphical user interfaces of different computer applications. The heterogeneity of various application interfaces often requires blind users to remember different keyboard combinations and navigation methods to use each application effectively. To alleviate this significant interaction burden imposed by heterogeneous application interfaces, we present Savant, a novel assistive technology powered by large language models (LLMs) that allows blind screen reader users to interact uniformly with any application interface through natural language. Novelly, Savant can automate a series of tedious screen reader actions on the control elements of the application when prompted by a natural language command from the user. These commands can be flexible in the sense that the user is not strictly required to specify the exact names of the control elements in the command. A user study evaluation of Savant with 11 blind participants demonstrated significant improvements in interaction efficiency and usability compared to current practices.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jul 2024.
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Citing papers per year: 2025: 4; 2026: 5.
9 papers.
- 2025Morae: Proactively Pausing UI Agents for User Choicescited by 18
- 2025Surfacing Variations to Calibrate Perceived Reliability of MLLM-generated Image Descriptionscited by 6
- 2025AccessGuru: Leveraging LLMs to Detect and Correct Web Accessibility Violations in HTML Codecited by 4
- 2025Permission Manifests for Web Agentscited by 3
- 2026A11y-CUA Dataset: Characterizing the Accessibility Gap in Computer Use Agentscited by 3
- 2026Finding the Signal in the Noise: An Exploratory Study on Assessing the Effectiveness of AI and Accessibility Forums for Blind Users' Support Needscited by 1
- 2026Lost in Instructions: Study of Blind Users' Experiences with DIY Manuals and AI-Rewritten Instructions for Assembly, Operation, and Troubleshooting of Tangible Productscited by 0
- 2026Insight: Enhancing Mobile Accessibility for Blind and Visually Impaired Users with LLMscited by 0
- 2026From Plots to Words: Model-Aware Multimodal Explanations as a Foundation for Accessible, Non-Visual Interactioncited by 0
- 2026From Plots to Words: Model-Aware Multimodal Explanations as a Foundation for Accessible, Non-Visual Interaction22 Jul 2026
- 2026Insight: Enhancing Mobile Accessibility for Blind and Visually Impaired Users with LLMs10 May 2026
- 2026Lost in Instructions: Study of Blind Users' Experiences with DIY Manuals and AI-Rewritten Instructions for Assembly, Operation, and Troubleshooting of Tangible Products20 Feb 2026
- 2026Finding the Signal in the Noise: An Exploratory Study on Assessing the Effectiveness of AI and Accessibility Forums for Blind Users' Support Needs20 Feb 2026
- 2026A11y-CUA Dataset: Characterizing the Accessibility Gap in Computer Use Agents10 Feb 2026
- 2025Permission Manifests for Web Agents7 Dec 2025
- 2025Morae: Proactively Pausing UI Agents for User Choices29 Aug 2025
- 2025AccessGuru: Leveraging LLMs to Detect and Correct Web Accessibility Violations in HTML Code24 Jul 2025
- 2025Surfacing Variations to Calibrate Perceived Reliability of MLLM-generated Image Descriptions21 Jul 2025