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• Towards Testing the Accessibility of Dynamic Visual Changes in Android Mobile GUI with Multi-Modal LLMs

  Mengxi Zhang, Jianlin Yu, Chen Xu, Jiqun Li, Xinglong Yin, Huaxiao Liu · 2026 · ACM Transactions on Computer-Human Interaction

  This paper addresses a long-standing gap in mobile accessibility testing: dynamic visual changes in Android GUIs that communicate task status or feedback to sighted users but are invisible to blind users of screen readers such as TalkBack. Examples include an input field…

  Android · mobile accessibility · screen readers · TalkBack · automated testing

• Say It My Way: Exploring Control in Conversational Visual Question Answering with Blind Users

  Farnaz Zamiri Zeraati, Yang Cao, Yuehan Qiao, Hal Daumé III, Hernisa Kacorri · 2026 · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI '26)

  This CHI 2026 paper investigates how blind users can exert control over responses generated by conversational visual question answering (VQA) systems built on vision-language models. While prompting and steering techniques are well established in general-purpose generative AI,…

  blind users · generative AI · visual question answering · VQA · personalization

• Interface Support for Evaluating Disability Bias in AI-Generated Images

  Kelly Avery Mack, Lucy Jiang, Lotus Zhang, Leah Findlater · 2026 · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI '26)

  Mack and colleagues investigate whether interface-level interventions can help users of generative text-to-image (T2I) tools recognise and avoid disability stereotypes in AI-generated images. The authors frame the work around a gap in AI safety: while model-side debiasing is an…

  AI bias · generative AI · text-to-image · disability representation · disability stereotypes

• Making Charts Speak: LLM-Based Conversational Chart Question Answering for Blind and Low-Vision Users

  Amit Kumar Das, Mohammad Tarun, Klaus Mueller · 2026 · Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA '26)

  Das, Tarun, and Mueller present GraphWhisper, a conversational system that lets blind and low-vision (BLV) users explore chart images (JPEG, PNG) through natural-language questions, without requiring the chart data to be pre-structured in formats like Vega-Lite. The authors…

  chart accessibility · data visualization · blind and low vision · large language models · conversational interface

• AccessGuru: Leveraging LLMs to Detect and Correct Web Accessibility Violations in HTML Code

  Nadeen Fathallah, Daniel Hernández, Steffen Staab · 2025 · ASSETS 2025: 27th International ACM SIGACCESS Conference on Computers and Accessibility

  This paper introduces AccessGuru, a novel method that combines traditional automated accessibility testing tools with large language models (LLMs) to both detect and correct web accessibility violations in HTML code. The work addresses a persistent gap in accessibility tooling:…

  automated testing · web accessibility · large language models · HTML remediation · prompt engineering

• SoundNarratives: Rich Auditory Scene Descriptions to Support Deaf and Hard of Hearing People

  Liang-Yuan Wu, Dhruv Jain · 2025 · ASSETS 2025: 27th International ACM SIGACCESS Conference on Computers and Accessibility

  This paper introduces SoundNarratives, a real-time system that generates rich, contextual auditory scene descriptions tailored to deaf and hard of hearing (DHH) users. Existing sound recognition technologies typically classify sounds into predefined categories like "door…

  deaf and hard of hearing · sound awareness · generative AI · audio-language models · prompt engineering

• When LLM-Generated Code Perpetuates User Interface Accessibility Barriers, How Can We Break the Cycle?

  Alexandra-Elena Gurita, Radu-Daniel Vatavu · 2025 · Proceedings of the 22nd International Web for All Conference (W4A 2025)

  This paper evaluates the ability of large language models (LLMs) to generate accessible web user interfaces, comparing ChatGPT (GPT-4-turbo) and Claude (3.5 Haiku) across two prompting strategies: accessibility-agnostic prompts ("Design the homepage of a banking app") and…

  large language models · WCAG compliance · automated accessibility · prompt engineering · code generation

• MAIDR Meets AI: Exploring Multimodal LLM-Based Data Visualization Interpretation by and with Blind and Low-Vision Users

  JooYoung Seo, Sanchita S. Kamath, Aziz Zeidieh, Saairam Venkatesh, Sean McCurry · 2024 · ASSETS '24: Proceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility

  This paper investigates how blind and low-vision (BLV) users interact with large language models to interpret data visualizations, building on the authors' previously developed MAIDR (Multimodal Access and Interactive Data Representation) framework. MAIDR already provides…

  blind and low vision · data visualization · large language models · generative AI · sonification

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