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• AI4XR: AI in Extended Reality for 3D Scene Editing and Accessibility Design

  Junlong Chen · 2026 · Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA '26) — Doctoral Consortium

  This CHI '26 Doctoral Consortium paper summarises Junlong Chen's PhD research at the University of Cambridge on integrating AI — specifically large language models (LLMs) and vision-language models (VLMs) — into extended reality (XR) workflows. The research covers two…

  extended reality · virtual reality · artificial intelligence · large language models · vision-language models

• Measuring the Semantic Accessibility Gap in LLM-Generated Web UIs

  Tommaso Calo, Alexandra-Elena Gurita, Luigi De Russis · 2026 · Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA '26)

  Calo, Gurita, and De Russis investigate a blind spot of mainstream automated accessibility tools: while scanners like Axe-core reliably catch missing alt attributes or unlabelled form fields (syntactic violations), they cannot tell whether the values present are actually…

  web accessibility · large language models · LLM code generation · semantic accessibility · WCAG

• Auto-Generating Personas from User Reviews in VR App Stores

  Yi Wang, Kexin Cheng, Xiao Liu, Chetan Arora, John Grundy, Thuong Hoang, Henry Been-Lirn Duh · 2026 · Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA ’26)

  This CHI 2026 Extended Abstract reports on an auto-generated persona system developed to help undergraduate students surface accessibility requirements in virtual reality design projects. The authors argue that personas are well-established in user-centered design and…

  virtual reality · VR accessibility · personas · requirements engineering · large language models

• Investigating the Role of Agentic AI in Facilitating Travel Planning for People with Low Vision

  Ranran Ding, Maryam Bandukda · 2026 · Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA ’26)

  This CHI 2026 Extended Abstract examines a stage of accessible travel that most assistive-technology research has overlooked: the pre-trip planning work people with low vision (PLV) do before ever leaving the house. The authors argue that most existing tools — navigation apps,…

  low vision · wayfinding · agentic AI · large language models · conversational agents

• Reassurance Robots: OCD in the Age of Generative AI

  Grace Barkhuff · 2026 · Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA ’26)

  This CHI 2026 Extended Abstracts paper by Grace Barkhuff (Georgia Tech) is an exploratory qualitative study of how generative AI tools, particularly ChatGPT, are reshaping the lived experience of Obsessive-Compulsive Disorder (OCD). OCD is a mental health disorder characterized…

  OCD · Obsessive-Compulsive Disorder · generative AI · ChatGPT · mental health

• 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

• NarrAid: Supporting Storytelling of People with Aphasia via Generative Visual Scene Displays

  Xiangfei Hu, Xiuqi Zheng, Qi Liu, Zejian Li, Ying Zhang, Lu Wang, Xipei Ren · 2026 · Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA ’26)

  NarrAid is a generative AI-driven Visual Scene Display (VSD) system designed to support storytelling, not just basic wants and needs, for people with aphasia (PWA). The authors argue that existing AAC tools, including traditional VSDs, support functional communication well but…

  aphasia · augmentative and alternative communication · visual scene display · storytelling · generative AI

• NeuroBridge: Using Generative AI to Bridge Cross-neurotype Communication Differences through Neurotypical Perspective-taking

  Rukhshan Haroon, Kyle Wigdor, Katie Yang, Nicole Toumanios, Eileen T Crehan, Fahad Dogar · 2025 · ASSETS 2025: 27th International ACM SIGACCESS Conference on Computers and Accessibility

  This paper presents NeuroBridge, an LLM-powered interactive platform designed to help neurotypical individuals better understand autistic communication styles and reflect on their own role in cross-neurotype communication breakdowns. The system is grounded in the double empathy…

  autism · neurodiversity · large language models · cross-neurotype communication · perspective-taking

• Benchmarking PDF Accessibility Evaluation: A Dataset and Framework for Assessing Automated and LLM-Based Approaches for Accessibility Testing

  Anukriti Kumar, Tanushree Padath, Lucy Lu Wang · 2025 · ASSETS 2025: 27th International ACM SIGACCESS Conference on Computers and Accessibility

  This paper addresses a critical gap in PDF accessibility evaluation by introducing the first expert-validated benchmark dataset and standardized evaluation framework for assessing how well different tools and approaches can evaluate PDF accessibility. Despite PDFs being the…

  PDF accessibility · automated testing · large language models · WCAG · PDF/UA

• 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

• Check Now, Can You See It?: Exploring Voice and Video-Capable Language Models for Identifying and Spatially Locating Items of Interest for Blind and Low-Vision Travelers

  Aziz N Zeidieh, JooYoung Seo · 2025 · ASSETS 2025: 27th International ACM SIGACCESS Conference on Computers and Accessibility

  This experience report documents the lived experiences of two blind travelers — Aziz (28, blind in left eye, 20/2200 in right) and JooYoung (35, blind in right eye, limited vision in left) — as they adapted commercially available voice and video-capable language models (VVLMs)…

  artificial intelligence · navigation · blindness and visual impairment · multimodal AI · large language models

• CapTune: Adapting Non-Speech Captions With Anchored Generative Models

  Jeremy Zhengqi Huang, Caluã De Lacerda Pataca, Saelyne Yang Wu, Dhruv Jain · 2025 · ASSETS 2025: 27th International ACM SIGACCESS Conference on Computers and Accessibility

  CapTune is a system that enables customization of non-speech captions—descriptions of environmental sounds, music, and other audio cues—for Deaf and Hard of Hearing (DHH) viewers. Current captioning practices follow a one-size-fits-all model based on standardized guidelines like…

  closed captioning · non-speech information · caption customization · deaf and hard of hearing · generative AI

• Surfacing Variations to Calibrate Perceived Reliability of MLLM-generated Image Descriptions

  Meng Chen, Akhil Iyer, Amy Pavel · 2025 · ASSETS 2025: 27th International ACM SIGACCESS Conference on Computers and Accessibility

  This paper addresses a critical safety problem in AI-powered visual access technology: multimodal large language models (MLLMs) like GPT-4o, Gemini, and Claude produce fluent, confident image descriptions that can contain fabricated content, misinterpretations, and omissions…

  blindness · low vision · image descriptions · multimodal AI · large language models

• DescribePro: Collaborative Audio Description with Human-AI Interaction

  Maryam S Cheema, Sina Elahimanesh, Samuel Martin, Pooyan Fazli, Hasti Seifi · 2025 · ASSETS 2025: 27th International ACM SIGACCESS Conference on Computers and Accessibility

  This paper presents DescribePro, a web-based platform that combines human expertise with AI capabilities to create and refine audio descriptions (AD) for video content. The system addresses the fundamental tension in AD production: human-crafted descriptions are high quality but…

  audio description · video accessibility · human-AI collaboration · authoring tools · blind and low vision

• CARTGPT: Real-Time Correction of CART Captions Using Large Language Models

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

  This paper introduces CARTGPT, a real-time system that enhances Communication Access Realtime Translation (CART) captions by combining human-generated CART transcripts with automatic speech recognition (ASR) output and using GPT-4 to detect and correct transcription errors. CART…

  deaf and hard of hearing · real-time captioning · CART · large language models · automatic speech recognition

• Understanding Human-AI Misalignment in LLM-Based Job-Seeking Support for Neurodivergent Users

  Kaely Hall, Marcus Ma, Xinyue Zhang, Vedant Das Swain, Jennifer G Kim · 2025 · ASSETS 2025: 27th International ACM SIGACCESS Conference on Computers and Accessibility

  This paper examines how misalignments manifest between neurodivergent job-seekers and a GPT-4-powered career support chatbot deployed by Mentra, a neuroinclusive employment platform with over 46,000 neurodivergent users. The researchers analysed 348 real-world chat logs from 271…

  neurodivergence · large language models · employment · AI alignment · autism

• Examining Age-Bias and Stereotypes of Aging in LLMs

  Sherwin Dewan, Ismail Shaikh, Connie Shaw, Abhilash Sahoo, Akshita Jha, Alisha Pradhan · 2025 · ASSETS 2025: 27th International ACM SIGACCESS Conference on Computers and Accessibility

  This paper investigates how large language models encode and reproduce age-related stereotypes about older adults. Using prompts from the Bias Benchmarking Questionnaire (BBQ), a well-established fairness dataset, the researchers administered 1,648 age-bias prompts to ChatGPT…

  ageism · AI bias · large language models · older adults · stereotypes

• Making Lecture Videos Accessible for Students who are Blind or have Low Vision through AI-Assisted Navigation and Visual Question Answering

  Katharina Anderer, Karin Müller, Lukas Strobel, Matthias Wölfel, Jan Niehues, Kathrin Gerling · 2025 · Proceedings of the 27th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2025)

  This paper presents the design and evaluation of LectureAssistant, an AI-powered prototype that makes lecture videos more accessible for students who are blind or have low vision. The research follows a three-part human-centred design process. First, need-finding interviews with…

  blind and low vision · lecture accessibility · higher education · large language models · vision-language models

• 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

• LLMs for Accessibility in Mobile Apps: Detection and Repair

  Wajdi Aljedaani, Ahmed Aljohani, Marcelo M. Eler, Abdulrahman Habib, Hyunsook Do · 2025 · Proceedings of the 22nd International Web for All Conference (W4A 2025)

  This study evaluates the capacity of three large language models—GPT-4o, Gemini 1.0 Pro, and Llama 3—to detect, classify, and remediate accessibility violations in Android mobile applications. While prior LLM accessibility research has focused primarily on web applications, this…

  mobile accessibility · large language models · Android accessibility · automated accessibility testing · accessibility remediation

• QuickQue: Enabling Quick Access to Information in User Reviews for Screen Reader Users

  Mohan Sunkara, Akshay Kolgar Nayak, Sandeep Kalari, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok · 2025 · Proceedings of the 22nd International Web for All Conference (W4A 2025)

  This paper presents QuickCue, a Google Chrome browser extension that helps blind screen reader users efficiently access online customer reviews by using LLM-powered aspect and sentiment classification to organize and summarize review content. The current experience of reading…

  screen readers · blind users · online reviews · large language models · browser extension

• Morae: Proactively Pausing UI Agents for User Choices

  Yi-Hao Peng, Dingzeyu Li, Jeffrey P. Bigham, Amy Pavel · 2025 · Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology (UIST '25)

  This paper introduces Morae, a UI agent that proactively pauses during automated task execution to involve blind and low-vision (BLV) users in critical decisions, rather than completing tasks end-to-end without user input. The work is motivated by a field study with four BLV…

  UI agents · blind and low vision · large language models · human-agent interaction · user agency

• StepWrite: Adaptive Planning for Speech-Driven Text Generation

  Hamza El Alaoui, Atieh Taheri, Yi-Hao Peng, Jeffrey P. Bigham · 2025 · Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology (UIST '25)

  This paper introduces StepWrite, an LLM-powered voice-based writing system that enables structured, hands-free and eyes-free composition of longer-form texts. While speech-to-text tools handle short dictation well, composing structured emails or detailed responses requires…

  voice interface · speech-to-text · hands-free interaction · eyes-free interaction · large language models

• CodeA11y: Making AI Coding Assistants Useful for Accessible Web Development

  Peya Mowar, Yi-Hao Peng, Jason Wu, Aaron Steinfeld, Jeffrey P. Bigham · 2025 · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI '25)

  This paper addresses a persistent problem: despite decades of accessibility standards and tools, ~96% of web pages contain accessibility violations. The authors argue that AI coding assistants like GitHub Copilot represent an untapped opportunity because developers already use…

  web accessibility · AI coding assistants · developer tools · WCAG · automated testing

• Policy Maps: Tools for Guiding the Unbounded Space of LLM Behaviors

  Michelle S. Lam, Fred Hohman, Dominik Moritz, Jeffrey P. Bigham, Kenneth Holstein, Mary Beth Kery · 2025 · Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology (UIST '25)

  This paper introduces "policy maps," an approach to AI policy design for large language models inspired by physical mapmaking. The core insight is that comprehensive policy coverage over an unbounded space of LLM inputs and outputs is impossible — just as no map can capture…

  AI safety · AI policy · large language models · AI ethics · model evaluation