Creating Accessibility 2.0 with Artificial Intelligence
Open accessAbstract
Despite the fact that artificial intelligence (AI) has existed for many years, for the first time it managed to attract the attention of the mainstream media. Since the release of the chatbot system "ChatGPT" by OpenAI in November 2022 the hype and interest in AI has increased significantly, not only in research but also among the general public. As a result, the question arises which specific application areas and target groups can benefit from artificial intelligence in the future. This article provides an overview of the use of artificial intelligence in creating accessibility. For this purpose, various aspects are considered on how AI technologies can contribute to removing barriers for people with disabilities and enable them to participate equally in social life. The aim of this paper is to highlight the potentials and challenges of uniting AI and accessibility. It discusses the contribution that artificial intelligence can make to closing the gap between technology and accessibility. Finally, a concept idea for the creation of a new kind of accessibility, the so-called Accessibility 2.0, is proposed.
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.
Integrity signals
Registry
Cites
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.
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?
Existing resources and standards vary in reliability and usually require users to interpret and adapt the information on their own.
“These vary in reliability and usually require users to interpret and adapt the information on their own [26, 27].”
From Proceedings of the 37th Australian Conference on Human-Computer Interaction (2025) · cites this paper as [26]
Section 2, “The Challenges of Implementing Accessibility” (page unavailable), says developers must review complex standards and identify requirements relevant to a specific application. This supports the need to interpret and adapt the information, but the paper does not say the resources or standards vary in reliability. (LLM, conf=0.85)
Experimental — accuracy not yet measuredTypical barriers include fragmented standards and guidelines like WCAG, complex language, and a lack of accessible, pedagogically aligned tools.
“Typical barriers include fragmented standards and guidelines like WCAG, complex language, and a lack of accessible, pedagogically aligned tools [5, 26, 42].”
From Proceedings of the 37th Australian Conference on Human-Computer Interaction (2025) · cites this paper as [26]
Section 2, “The Challenges of Implementing Accessibility” (page not provided), describes numerous standards and guidelines, including WCAG, and says their language is complex and difficult to understand. Sections 3 and 5 discuss accessibility limitations of AI tools and propose a tool to help developers, but they do not establish a lack of pedagogically aligned tools. (LLM, conf=0.87)
Experimental — accuracy not yet measuredMany developers and designers find accessibility requirements difficult to interpret and apply due to their abstract language, dispersed sources, text-heavy nature, and lack of contextual examples.
“Many developers and designers find accessibility requirements difficult to interpret and apply due to their abstract language, dispersed sources, text-heavy nature, and lack of contextual examples [5, 26].”
From Proceedings of the 37th Australian Conference on Human-Computer Interaction (2025) · cites this paper as [26]
The complexity, fragmentation, and abstract language of standards such as WCAG or ISO often hinder practical implementation.
“The complexity, fragmentation, and abstract language of standards such as WCAG or ISO often hinder practical implementation [5, 26, 32].”
From Proceedings of the 37th Australian Conference on Human-Computer Interaction (2025) · cites this paper as [26]
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
Academic integrity and artificial intelligence: is ChatGPT hype, hero or heresy?
Currie, G. M. (2023) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2023.04.008 · Cited by 283 · cited 1× · see citing works
“However, neither self-driving cars nor virtual voice assistants have been able to trigger a lasting hype in society today, as the chatbot ’ChatGPT’ did [8–12, 14, 15, 25].”
Background
Flagged first, then by citations
Research works printed but not linked: 10
- Shadi Abou-Zahra, Judy Brewer, and Michael Cooper. 2018. Artificial Intelligence (AI) for Web Accessibility: Is Conformance Evaluation a Way Forward?. In Pro- ceedings of the 15th International Web for All Conference (Lyon, France) (W4A '18). Association for Computing Machinery, New York, NY, USA, Article 20, 4 pages. https://doi.org/10.1145/3192714.3192834 · DOI
- Francesco Amore, Valentina Silvestri, Matteo Guidobaldi, et al. 2023. Efficacy and Patients' Satisfaction with the ORCAM MyEye Device Among Visually Impaired People: A Multicenter Study. Journal of Medical Systems 47, 11 (2023), 1-8. https://doi.org/10.1007/s10916-023-01908-5 · DOI
- Saminda Sundeepa Balasuriya, Laurianne Sitbon, Andrew A. Bayor, Maria Hoogstrate, and Margot Brereton. 2018. Use of Voice Activated Interfaces by People with Intellectual Disability. In Proceedings of the 30th Australian Confer- ence on Computer-Human Interaction (Melbourne, Australia) (OzCHI '18). As- sociation for Computing Machinery, New York, NY, USA, 102-112. https: //doi.org/10.1145/3292147.3292161 · DOI · Paper page
- Saminda Sundeepa Balasuriya, Laurianne Sitbon, Jinglan Zhang, and Khairi Anuar. 2021. Summary and Prejudice: Online Reading Preferences of Users with Intellectual Disability. In Proceedings of the 2021 Conference on Human Information Interaction and Retrieval (Canberra ACT, Australia) (CHIIR '21). Association for Computing Machinery, New York, NY, USA, 285-289. https://doi.org/10.1145/ 3406522.3446039 · DOI
- Andrew A. Bayor, Margot Brereton, Laurianne Sitbon, Bernd Ploderer, Filip Bircanin, Benoit Favre, and Stewart Koplick. 2021. Toward a Competency-Based Approach to Co-Designing Technologies with People with Intellectual Disability. ACM Trans. Access. Comput. 14, 2, Article 6 (jul 2021), 33 pages. https://doi.org/ 10.1145/3450355 · DOI
- Maarten Buyl, Christina Cociancig, Cristina Frattone, and Nele Roekens. 2022. Tackling Algorithmic Disability Discrimination in the Hiring Process: An Ethical, Legal and Technical Analysis. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (Seoul, Republic of Korea) (FAccT '22). Association for Computing Machinery, New York, NY, USA, 1071-1082. https://doi.org/10.1145/3531146.3533169 · DOI
- Julia Hermann, Carolin Straßmann, Aysegül Dogangün, Simone Roth, and Sab- rina Eimler. 2021. Soziale Roboter in städtischen Einrichtungen des Ruhrgebiets: Eine partizipative Projektkonzeption. In Mensch und Computer 2021 -Workshop- band, Carolin Wienrich, Philipp Wintersberger, and Benjamin Weyers (Eds.). Gesellschaft für Informatik e.V., Bonn, 2. https://doi.org/10.18420/muc2021-mci- ws06-277 · DOI
- Dany Lussier-Desrochers, Claude L. Normand, Alejandro Romero-Torres, Yves Lachapelle, Valérie Godin-Tremblay, Marie-Ève Dupont, Jeannie Roux, Laurence Pépin-Beauchesne, and Pascale Bilodeau. 2017. Bridging the digital divide for people with intellectual disability. Cyberpsychology: Journal of Psychosocial Research on Cyberspace 11, 1 (May 2017), Article 1. https://doi.org/10.5817/ CP2017-1-1 · DOI
- Aqueasha Martin-Hammond, Ulka Patil, and Barsa Tandukar. 2021. A Case for Making Web Accessibility Guidelines Accessible: Older Adult Content Creators and Web Accessibility Planning. In Proceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility (Virtual Event, USA) (AS- SETS '21). Association for Computing Machinery, New York, NY, USA, Article 34, 6 pages. https://doi.org/10.1145/3441852.3476472 · DOI · Paper page
- Tânia Rocha, Maximino Bessa, Luís Magalhães, and Luciana Cabral. 2015. Per- forming Universal Tasks on the Web: Interaction with Digital Content by People with Intellectual Disabilities. In Proceedings of the XVI International Conference on Human Computer Interaction (Vilanova i la Geltru, Spain) (Interacción '15). Association for Computing Machinery, New York, NY, USA, Article 30, 7 pages. https://doi.org/10.1145/2829875.2829897 · DOI
Cited by
Sorted by citations
Kubullek, A.-K., Zimmer, L., Ly, T. T. M., & Dogangün, A. (2025) · Proceedings of the 37th Australian Conference on Human-Computer Interaction · doi:10.1145/3764687.3764711 · Cited by 1 · see citing works
Self-citation
- Authors
- Kubullek, A.-K., & Dogangün, A.
- Affiliations
- Ruhr West University of Applied Sciences (DE)
- Hochschule Ruhr West, Deutschland
- Venue
- Mensch und Computer 2023
- Publisher
- ACM
- Published
- 2023-09-03
- Pages
- 437-441
- OpenAlex
- W4386041174
- Cited by
- 8
- References
- 1
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 2
- FWCI
- 3.35
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Mensch und Computer (MuC) (publishedVersion)
- Topics
- Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
- Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- artificial intelligence, accessibility, people with disabilities, ChatGPT, assistive technology, social participation (health and rehabilitation), barriers to inclusion, technology gap
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
