"We do use it, but not how hearing people think": How the Deaf and Hard of Hearing Community Uses Large Language Model Tools
On the map: LLM reasoning → Reasoning benchmarks and evaluation
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 3 of its 66 references that are in the map. First posted on arXiv 28 Oct 2024.
Builds on
3 of 66 references.
References · 3
- 2018MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Languagecited by 118
- 2024Systemic Biases in Sign Language AI Research: A Deaf-Led Call to Reevaluate Research Agendascited by 19
- 2024Generative AI in Higher Education: A Global Perspective of Institutional Adoption Policies and Guidelinescited by 13
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- 2024Audio Description Generation in the Era of LLMs and VLMs: A Review of Transferable Generative AI Technologies
- 2025Beyond Visual Perception: Insights from Smartphone Interaction of Visually Impaired Users with Large Multimodal Models
- 2026The interpreter as developer: An autoethnographic design study of building a computer-assisted interpreting tool through human-LLM co-creation
- 2024Emerging Practices for Large Multimodal Model (LMM) Assistance for People with Visual Impairments: Implications for Design
- 2026BackTranslation2.0 -- A Linguistically Motivated Metric to Assess Sign Language Production
- 2026How Multimodal Large Language Models Support Access to Visual Information: A Diary Study With Blind and Low Vision People
- 2025Exploring User Acceptance and Concerns toward LLM-powered Conversational Agents in Immersive Extended Reality
- 2026Accessible, but Not Adopted: Increasing LLM Adoption among First-generation, Low-income (FGLI) College Students beyond Expanding Access