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• Exploring Collaboration to Center the Deaf Community in Sign Language AI

  Rie Kamikubo, Abraham Glasser, Alex X Lu, Hal Daumé III, Hernisa Kacorri, Danielle Bragg · 2025 · ASSETS 2025: 27th International ACM SIGACCESS Conference on Computers and Accessibility

  This paper investigates the critical disconnect between machine learning (ML) practitioners and Deaf American Sign Language (ASL) signers in the development of sign language AI technologies. Despite sign language processing's promise for societal inclusivity, the field…

  Deaf community · sign language · American Sign Language · machine learning · participatory AI

• 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

• Joint Bilingual Navigation of Informed Consent and Intake Forms in American Sign Language and Written English

  Matthew Seita, Michaela Usha, Rachel Skwersky, Poorna Kushalnagar, Raja Kushalnagar · 2025 · Proceedings of the 22nd International Web for All Conference (W4A 2025)

  This paper presents a joint bilingual informed consent and intake form that can be navigated simultaneously in American Sign Language (ASL) and written English, addressing a critical accessibility barrier for the approximately 500,000 Americans who primarily use ASL. Current…

  deaf and hard of hearing · sign language recognition · informed consent · healthcare accessibility · American Sign Language

• Color-Coded Emotional Learning Serious Games for Children with Autism: Foregrounding Facial Expression Recognition & Accessibility

  Tahani Jaser Alahmadi, Haifa Albahli, Modhi Alqahtani, Hind Bin Mehaya, Imtinan Alqahtani, Joory Alotaibi · 2025 · Proceedings of the 22nd International Web for All Conference (W4A)

  This technical note presents Muheet, a mobile application that uses color-emotion associations and AI-based facial expression recognition to help children with autism spectrum disorder (ASD) understand and recognize emotions. ASD affects approximately 1.5-2% of the global…

  autism · emotional learning · serious games · facial expression recognition · machine learning

• AI and Gamification for Employees with ADHD: A Tool for Enhancing Workplace Focus & Productivity

  Tahani Jaser Alahmadi, Ruyouf AlKhuzaim, Meral AlHelwah, Abbrar AlOtaibi, Dima AlTammami, Daad AlSaqer · 2025 · Proceedings of the 22nd International Web for All Conference (W4A)

  This paper presents WADAQ, an AI-driven gamification tool designed to support employees with ADHD in improving focus, task management, and workplace productivity. The system operates in three stages: first, machine learning classification models predict whether a user is likely…

  ADHD · gamification · machine learning · reinforcement learning · workplace accessibility

• Conversational Voice User Interfaces Supporting Individuals with Down Syndrome: A Literature Review

  Franceli L. Cibrian, Concepción Valdez, Lauren Min, Vivian Genaro Motti · 2025 · ACM Transactions on Accessible Computing

  This systematic literature review examines 43 papers published between 1998 and 2023 on the use of Conversational Voice User Interfaces (CVUIs) to support individuals with Down syndrome. Using the PRISMA methodology, the authors searched ACM Digital Library, IEEE Xplore, PubMed,…

  voice user interfaces · Down syndrome · speech recognition · intellectual disability · conversational AI

• FormA11y — Research and Development of a Tool for Remediating PDF Forms for Accessibility

  Sparsh Paliwal, Joshua Hoeflich, J. Bern Jordan, Rajiv Jain, Vlad I. Morariu, Alexa Siu, Jonathan Lazar · 2025 · ACM Transactions on Computer-Human Interaction, Vol. 32, No. 1

  Paliwal and colleagues — a team spanning the University of Maryland's Maryland Initiative for Digital Accessibility (MIDA), Northwestern, and Adobe Research — tackle a problem that has been largely overlooked in PDF accessibility research: the remediation of interactive *PDF…

  PDF accessibility · PDF forms · PDF/UA · document accessibility · remediation tools

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