CoChat
Sign Up
Cited by 14Hearing Impairment and CommunicationAssistive Technology in Communication and MobilityHearing Loss and Rehabilitation

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

Open access
Huffman, Shuxu, Chen, Si, Mack, Kelly Avery, Su, Haotian, Wang, Qi, Kushalnagar, Raja·2025·Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems
DOI 10.1145/3706599.3719785
Open this paper in CoChat — free accountOpen PDF

Abstract

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1145/3706599.3719785

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • In research involving people with disabilities, participants mostly collaborate on the design or evaluation of access-supporting generative AI systems.

    “Similarly, in research involving PwD, participants mostly collaborate on the design or evaluation of accesssupporting generative AI systems [2, 3, 22, 60, 61, 73, 98, 109].”

    From Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation (2026) · cites this paper as [60]

    The Abstract describes a study in which Deaf and Hard of Hearing participants reported how they use Text AI, and §3.2 says interviewees tried ChatGPT tasks and assessed its responses. The paper supports participant input on the accessibility of an existing generative AI tool, but it does not describe participants collaborating to design an access-supporting system or establish that this is what participants mostly do across disability research. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 51

  1. Mariana Arroyo Chavez, Bernard Thompson, Molly Feanny, Kafayat Alabi, Minchan Kim, Lu Ming, Abraham Glasser, Raja Kushalnagar, and Christian Vogler. 2024. Customization of closed captions via large language models. In International Conference on Computers Helping People with Special Needs. Springer, 50-58.
  2. Yael Bat-Chava. 2000. Diversity of deaf identities. American annals of the deaf (2000), 420-428.
  3. H-Dirksen L Bauman and Joseph J Murray. 2014. Deaf gain: Raising the stakes for human diversity. U of Minnesota Press.
  4. Larwan Berke, Sushant Kafle, and Matt Huenerfauth. 2018. Methods for evaluation of imperfect captioning tools by deaf or hard-of-hearing users at different reading literacy levels. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1-12.
  5. Virginia Braun and Victoria Clarke. 2019. Reflecting on reflexive thematic analysis. Qualitative research in sport, exercise and health 11, 4 (2019), 589-597.
  6. Timothy J Byatt, Jill Duncan, Kerry Dally, and Louise de Beuzeville. 2021. Evolving identities of adolescents who are deaf or hard of hearing. American Annals of the Deaf 166, 1 (2021), 5-30.
  7. Necati Cihan Camgoz, Oscar Koller, Simon Hadfield, and Richard Bowden. 2020. Sign language transformers: Joint end-to-end sign language recognition and translation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 10023-10033.
  8. Joanna E Cannon, Susan R Easterbrooks, Phill Gagné, and Jennifer Beal-Alvarez. 2011. Improving DHH students' grammar through an individualized software program. Journal of Deaf Studies and Deaf Education 16, 4 (2011), 437-457.
  9. Joanna E Cannon and Susannah Kirby. 2013. Grammar structures and deaf and hard of hearing students: A review of past performance and a report of new findings. American annals of the deaf 158, 3 (2013), 292-310.
  10. Joanna E Cannon and John L Luckner. 2016. Increasing cultural and linguistic diversity in deaf education teacher preparation programs. American Annals of the Deaf 161, 1 (2016), 89-103.
  11. Adrienne Colella and Dianna L Stone. 2013. Workplace discrimination toward persons with disabilities: A call for some new research directions. In Discrimination at work. Psychology Press, 227-253.
  12. Kata Csizér and Edit H Kontra. 2020. Foreign language learning characteristics of deaf and severely hard-of-hearing students. The Modern Language Journal 104, 1 (2020), 233-249.
  13. Aashaka Desai, Lauren Berger, Fyodor Minakov, Nessa Milano, Chinmay Singh, Kriston Pumphrey, Richard Ladner, Hal Daumé III, Alex X Lu, Naomi Caselli, et al. 2024. ASL citizen: a community-sourced dataset for advancing isolated sign language recognition. Advances in Neural Information Processing Systems 36 (2024).
  14. Aashaka Desai, Maartje De Meulder, Julie A Hochgesang, Annemarie Kocab, and Alex X Lu. 2024. Systemic Biases in Sign Language AI Research: A Deaf-Led Call to Reevaluate Research Agendas. arXiv preprint arXiv:2403.02563 (2024). · arXiv
  15. Ewa Domagała-Zyśk and Anna Podlewska. 2019. Strategies of oral communication of deaf and hard-of-hearing (D/HH) non-native English users. European Journal of Special Needs Education 34, 2 (2019), 156-171.
  16. Claire Edwards and Gill Harold. 2014. DeafSpace and the principles of universal design. Disability and Rehabilitation 36, 16 (2014), 1350-1359.
  17. Sally T Freeman. 1989. Cultural and linguistic bias in mental health evaluations of deaf people. Rehabilitation Psychology 34, 1 (1989), 51.
  18. Neil S Glickman and Wyatte C Hall. 2018. Language deprivation and deaf mental health. Routledge.
  19. Blaine Goss. 2003. Hearing from the deaf culture. Intercultural communication studies 12, 2 (2003), 1-17.
  20. Jan Gugenheimer, Katrin Plaumann, Florian Schaub, Patrizia Di Campli San Vito, Saskia Duck, Melanie Rabus, and Enrico Rukzio. 2017. The impact of assistive technology on communication quality between deaf and hearing individuals. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. 669-682.
  21. Matthew L Hall, Wyatte C Hall, and Naomi K Caselli. 2019. Deaf children need language, not (just) speech. First Language 39, 4 (2019), 367-395.
  22. Wyatte C Hall. 2017. What you don't know can hurt you: The risk of language deprivation by impairing sign language development in deaf children. Maternal and child health journal 21, 5 (2017), 961-965.
  23. Iain Hutchison. 2007. Oralism: A sign of the times? The contest for deaf communication in education provision in late nineteenth-century Scotland. European Review of History-Revue européenne d'Histoire 14, 4 (2007), 481-501.
  24. Laura Inzerillo and Cettina Santagati. 2016. Crowdsourcing cultural heritage: from 3D modeling to the engagement of young generations. In Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection: 6th International Conference, EuroMed 2016, Nicosia, Cyprus, October 31-November 5, 2016, Proceedings, Part I 6. Springer, 869-879.
  25. Yueqiao Jin, Lixiang Yan, Vanessa Echeverria, Dragan Gašević, and Roberto Martinez-Maldonado. 2024. Generative AI in Higher Education: A Global Perspective of Institutional Adoption Policies and Guidelines. arXiv preprint arXiv:2405.11800 (2024). · arXiv
  26. Hamid Reza Vaezi Joze and Oscar Koller. 2018. Ms-asl: A large-scale data set and benchmark for understanding american sign language. arXiv preprint arXiv:1812.01053 (2018). · arXiv
  27. Lee Kezar, Jesse Thomason, Naomi Caselli, Zed Sehyr, and Elana Pontecorvo. 2023. The Sem-Lex Benchmark: Modeling ASL Signs and Their Phonemes. In Proceedings of the 25th International ACM SIGACCESS Conference on Computers and Accessibility. 1-10.
  28. Edit H Kontra. 2017. The foreign-language learning situation of Deaf adults: An overview. Journal of Adult Learning, Knowledge and Innovation 1, 1 (2017), 35-42.
  29. Paddy Ladd. 2005. Deafhood: A concept stressing possibilities, not deficits. Scandinavian Journal of Public Health 33, 66_suppl (2005), 12-17.
  30. Yidan Ma, Weifeng Xue, Qin Liu, and Yin Xu. 2022. Discrimination and deaf adolescents' subjective well-being: The role of deaf identity. The Journal of Deaf Studies and Deaf Education 27, 4 (2022), 399-407.
  31. Kelly Mack, Danielle Bragg, Meredith Ringel Morris, Maarten W Bos, Isabelle Albi, and Andrés Monroy-Hernández. 2020. Social app accessibility for deaf signers. Proceedings of the ACM on Human-Computer Interaction 4, CSCW2 (2020), 1-31.
  32. Emma J McDonnell, Soo Hyun Moon, Lucy Jiang, Steven M Goodman, Raja Kushalnagar, Jon E Froehlich, and Leah Findlater. 2023. "Easier or Harder, Depending on Who the Hearing Person Is": Codesigning Videoconferencing Tools for Small Groups with Mixed Hearing Status. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1-15.
  33. Brian T McMahon. 2012. An overview of workplace discrimination and disability. Journal of Vocational Rehabilitation 36, 3 (2012), 135-139.
  34. Cecily Morrison, Martin Grayson, Rita Faia Marques, Daniela Massiceti, Camilla Longden, Linda Wen, and Edward Cutrell. 2023. Understanding Personalized Accessibility through Teachable AI: Designing and Evaluating Find My Things for People who are Blind or Low Vision. In Proceedings of the 25th International ACM SIGACCESS Conference on Computers and Accessibility. 1-12.
  35. Lucie Naert, Caroline Larboulette, and Sylvie Gibet. 2020. A survey on the animation of signing avatars: From sign representation to utterance synthesis. Computers & Graphics 92 (2020), 76-98.
  36. Noel O'Connell. 2022. "Opportunity blocked": Deaf people, employment and the sociology of audism. Humanity & society 46, 2 (2022), 336-358.
  37. Austin Pack and Jeffrey Maloney. 2023. Potential affordances of generative ai in language education: Demonstrations and an evaluative framework. Teaching English with Technology 23, 2 (2023), 4-24.
  38. Ilias Papastratis, Christos Chatzikonstantinou, Dimitrios Konstantinidis, Kosmas Dimitropoulos, and Petros Daras. 2021. Artificial intelligence technologies for sign language. Sensors 21, 17 (2021), 5843.
  39. Ila Parasnis. 1998. Cultural and language diversity and the deaf experience. Cambridge University Press.
  40. Sen Qi and Ross E Mitchell. 2012. Large-scale academic achievement testing of deaf and hard-of-hearing students: Past, present, and future. Journal of deaf studies and deaf education 17, 1 (2012), 1-18.
  41. Georgia Robins Sadler, Hau-Chen Lee, Rod Seung-Hwan Lim, and Judith Fullerton. 2010. Recruitment of hard-to-reach population subgroups via adaptations of the snowball sampling strategy. Nursing & health sciences 12, 3 (2010), 369-374.
  42. Sven Schild and Constance J Dalenberg. 2012. Trauma exposure and traumatic symptoms in deaf adults. Psychological trauma: Theory, research, practice, and policy 4, 1 (2012), 117.
  43. Sven Schild and Constance J Dalenberg. 2016. Information deprivation trauma: Definition, assessment, and interventions. Journal of aggression, maltreatment & trauma 25, 8 (2016), 873-889.
  44. Jessica A Scott, Hannah M Dostal, and Susan Lane-Outlaw. 2021. A Call for a Diversity of Perspectives in Deaf Education Research. American Annals of the Deaf 166, 1 (2021), 49-61.
  45. Per Koren Solvang and Hilde Haualand. 2014. Accessibility and diversity: Deaf space in action. Scandinavian Journal of Disability Research 16, 1 (2014), 1-13.
  46. Thad Starner, Sean Forbes, Matthew So, David Martin, Rohit Sridhar, Gururaj Deshpande, Sam Sepah, Sahir Shahryar, Khushi Bhardwaj, Tyler Kwok, et al. 2024. PopSign ASL v1. 0: an isolated american sign language dataset collected via smartphones. Advances in Neural Information Processing Systems 36 (2024).
  47. William C Stokoe Jr. 2005. Sign language structure: An outline of the visual communication systems of the American deaf. Journal of deaf studies and deaf education 10, 1 (2005), 3-37.
  48. Samuel J Supalla, Jody H Cripps, and Andrew PJ Byrne. 2017. Why American sign language gloss must matter. American annals of the deaf 161, 5 (2017), 540-551.
  49. M Virginia Swisher. 1989. The language-learning situation of deaf students. Tesol Quarterly 23, 2 (1989), 239-257.
  50. Clayton Valli. 2000. Linguistics of American Sign Language: An Introduction. Gallaudet University Press.
  51. Kimberly A Wolbers, Shannon C Graham, Hannah M Dostal, and Lisa M Bowers. 2014. A description of ASL features in writing. Ampersand 1 (2014), 19-27. (3) Rarely (4) Never

Cited by

Sorted by citations

Authors
Huffman, S., Chen, S., Mack, K. A., Su, H., Wang, Q., & Kushalnagar, R.
Affiliations
  • Gallaudet University (US)
  • University of Notre Dame (US)
  • University of Washington (US)
  • Clemson University (US)
ORCID
Venue
Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2025-04-25
Pages
1-9
DOI
10.1145/3706599.3719785
OpenAlex
W4409720490
Cited by
14
References
9
Citations by year
2026: 10 · 2025: 4
FWCI
55.72
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Social Sciences › Psychology › Developmental and Educational Psychology › Hearing Impairment and Communication
  • Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Hearing Loss and Rehabilitation
Keywords
large language models, assistive technology, deafness, technology use, deaf culture

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review