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• Context-Aware Image Descriptions for Web Accessibility

  Ananya Gubbi Mohanbabu, Amy Pavel · 2024 · Proceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '24)

  This paper addresses a fundamental limitation of current AI-generated image descriptions: they describe images in isolation without considering the surrounding webpage context. When blind and low-vision (BLV) users encounter images on the web, what they need to know about an…

  alt text · image descriptions · blind and low vision · artificial intelligence · large language models

• 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

• TwIPS: A Large Language Model Powered Texting Application to Simplify Conversational Nuances for Autistic Users

  Rukhshan Haroon, Fahad Dogar · 2024 · ASSETS '24: Proceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility

  This paper presents TwIPS, a prototype texting application powered by a large language model that assists autistic users with the pragmatic and tonal aspects of text-based communication. Many autistic individuals experience difficulties interpreting non-literal language…

  autism · communication · large language models · text messaging · tone interpretation

• "I'm treating it kind of like a diary": Characterizing How Users with Disabilities Use AI Chatbots

  Kayla Mullen, Wenhan Xue, Manasa Kudumu · 2024 · Proceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2024)

  This study investigates how people with disabilities actually use LLM-based chatbots like ChatGPT, Gemini, Claude, and Perplexity in their daily lives. While previous research has focused primarily on identifying harms that LLMs impose on the disability community — such as…

  large language models · AI chatbots · disability representation · disability justice · assistive AI

• Audio Description Customization

  Rosiana Natalie, Ruei-Che Chang, Smitha Sheshadri, Anhong Guo, Kotaro Hara · 2024 · Proceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2024)

  This paper investigates how audio descriptions (AD) for video content can be customized to meet the diverse preferences of blind and low-vision (BLV) users. Traditional ADs are fixed narratives created by sighted describers, offering no ability for users to adjust what…

  audio description · blind and low vision · customization · video accessibility · assistive technology

• Does ChatGPT Generate Accessible Code? Investigating Accessibility Challenges in LLM-Generated Source Code

  Wajdi Aljedaani, Abdulrahman Habib, Ahmed Aljohani, Marcelo Eler, Yunhe Feng · 2024 · Proceedings of the 21st International Web for All Conference (W4A)

  This paper presents the first empirical evaluation of the accessibility of web code generated by ChatGPT (GPT-3.5), examining both how accessible the generated code is and how well the model can fix accessibility violations. The study involved 88 web developers who prompted…

  web accessibility · large language models · ChatGPT · automated testing · WCAG

• In-Page Navigation Aids for Screen-Reader Users with Automatic Topicalisation and Labelling

  Jorge Sassaki Resende Silva, Paula Christina Figueira Cardoso, Raphael Winckler De Bettio, Daniela Cardoso Tavares, Carlos Alberto Silva, Willian Massami Watanabe, Andre Pimenta Freire · 2024 · ACM Transactions on Accessible Computing

  This paper addresses a fundamental challenge for screen reader users: navigating lengthy web documents that lack proper heading structure. When web pages do not include semantic headings or internal navigation links, users must read content linearly, which increases cognitive…

  screen readers · navigation · natural language processing · topic segmentation · large language models

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