arXiv subjects

Shuxu Huffman

Publications and source records attributed to Shuxu Huffman.

3 recordsLinked to original sources

arXiv2026

Bigger than the EAR BOX: A Theory-Grounded Review of XR Accessibility Research for Deaf and Hard of Hearing Communities

Extended Reality (XR) technologies have received growing attention in accessibility research involving Deaf and Hard of Hearing (DHH) communities. Yet less attention has been given to the assumptions shaping this work. We present a theory-grounded review of XR research involving DHH users. Drawing on Disability Studies, Deaf Studies, and DeafSpace, we develop a theoretical framework with four analytical dimensions: orientation toward access, distribution of responsibility, conceptualization of DHH communities, and spatial and perceptual assumptions. We apply this framework to 53 XR studies involving DHH users, identified through a search and screening of ACM publications from 2015 to 2025. Our analysis shows that XR accessibility is frequently framed as supporting communication within hearing-default environments, while overlooking the diversity of DHH communities. We identify directions for redistributing accessibility labor and reconfiguring space, and offer our framework as a tool for future XR research involving DHH communities.

cs.HC↗

arXiv2024

"We do use it, but not how hearing people think": How the Deaf and Hard of Hearing Community Uses Large Language Model Tools

Generative AI tools, particularly those utilizing large language models (LLMs), are increasingly used in everyday contexts. While these tools enhance productivity and accessibility, little is known about how Deaf and Hard of Hearing (DHH) individuals engage with them or the challenges they face when using them. This paper presents a mixed-method study exploring how the DHH community uses Text AI tools like ChatGPT to reduce communication barriers and enhance information access. We surveyed 80 DHH participants and conducted interviews with 11 participants. Our findings reveal important benefits, such as eased communication and bridging Deaf and hearing cultures, alongside challenges like lack of American Sign Language (ASL) support and Deaf cultural understanding. We highlight unique usage patterns, propose inclusive design recommendations, and outline future research directions to improve Text AI accessibility for the DHH community.

cs.HC↗

arXiv2024

"Real Learner Data Matters" Exploring the Design of LLM-Powered Question Generation for Deaf and Hard of Hearing Learners

Deaf and Hard of Hearing (DHH) learners face unique challenges in learning environments, often due to a lack of tailored educational materials that address their specific needs. This study explores the potential of Large Language Models (LLMs) to generate personalized quiz questions to enhance DHH students' video-based learning experiences. We developed a prototype leveraging LLMs to generate questions with emphasis on two unique strategies: Visual Questions, which identify video segments where visual information might be misrepresented, and Emotion Questions, which highlight moments where previous DHH learners experienced learning difficulty manifested in emotional responses. Through user studies with DHH undergraduates, we evaluated the effectiveness of these LLM-generated questions in supporting the learning experience. Our findings indicate that while LLMs offer significant potential for personalized learning, challenges remain in the interaction accessibility for the diverse DHH community. The study highlights the importance of considering language diversity and culture in LLM-based educational technology design.

cs.HC↗