CoChat
Sign Up
Cited by 159Assistive Technology in Communication and MobilityEthics and Social Impacts of AIDisability Rights and Representation

A Conceptual Model for Inclusive Technology: Advancing Disability Inclusion through Artificial Intelligence

Open access
Almufareh, Maram Fahaad, Kausar, Sumaira, Humayun, Mamoona, Tehsin, Samabia·2024·Journal of Disability Research
DOI 10.57197/jdr-2023-0060
Open this paper in CoChat — free accountOpen PDF

Abstract

Artificial intelligence (AI) has ushered in transformative changes, championing inclusion and accessibility for individuals with disabilities. This article delves into the remarkable AI-driven solutions that have revolutionized their lives across various domains. From assistive technologies such as voice recognition and AI-powered smart glasses catering to diverse needs, to healthcare benefiting from early disease detection algorithms and wearable devices that monitor vital signs and alert caregivers in emergencies, AI has steered in significant enhancements. Moreover, AI-driven prosthetics and exoskeletons have substantially improved mobility for those with limb impairments. The realm of education has not been left untouched, with AI tools creating inclusive learning environments that adapt to individual learning styles, paving the way for academic success among students with disabilities. However, the boundless potential of AI also presents ethical concerns and challenges. Issues like safeguarding data privacy, mitigating algorithmic bias, and bridging the digital divide must be thoughtfully addressed to fully harness AI’s potential in empowering individuals with disabilities. To complement these achievements, a robust conceptual model for AI disability inclusion serves as the theoretical framework, guiding the development of tailored AI solutions. By striking a harmonious balance between innovation and ethics, AI has the power to significantly enhance the overall quality of life for individuals with disabilities across a spectrum of vital areas.

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 · 10.57197/jdr-2023-0060

Cites

1 of 1 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?

  • Unauthorised use of unsanctioned generative AI tools risks producing unequal exposure to risk among an already marginalised group (students with disabilities).

    “unauthorised use of unsanctioned tools risks producing unequal exposure to risk among an already marginalised group (Almufareh et al., 2024).”

    From The Use of Generative AI by Students with Disabilities in UK Higher Education: A Mixed-Methods Survey of 125 Students (2025) · cites this paper as Almufareh et al., 2024

    The 'Addressing algorithmic bias' section says AI bias could lead to unequal access for people with disabilities, and 'Preserving privacy and data security' identifies risks involving their sensitive data. Neither section links unequal risk exposure among students with disabilities to the unauthorised use of unsanctioned generative AI tools. (LLM, conf=0.82)

    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: 41

  1. Abhishek S., Sathish H., Kumar A. and Anjali T. (2022). Aiding the visually impaired using artificial intelligence and speech recognition technol- ogy. In: 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA), 21 Sep 2022; pp. 1356-1362, IEEE.
  2. Afonso-Jaco A. and Katz B.F. (2022). Spatial knowledge via auditory infor- mation for blind individuals: spatial cognition studies and the use of audio-VR. Sensors, 22(13), 4794.
  3. Almufareh M.F., Kausar S., Humayun M. and Tehsin S. (2023). Leveraging motor imagery rehabilitation for individuals with disabilities: a com- prehensive review. Healthcare, 11(19), 2653.
  4. Arumugam D., Govindaraju K. and Tamilarasan A.K. (2022). AIIoT-based smart framework for screening specific learning disabilities. In: Machine Learning for Critical Internet of Medical Things: Applica- tions and Use Cases; pp. 103-124, Springer International Publishing, Cham.
  5. Bharath M.R. (2022). Controlling mouse and virtual keyboard using eye-tracking by computer vision. J. Algebr. Stat., 13(3), 3354-3368.
  6. Boulanger J. (2022). Ways of Knowing, Ways of Being: Exploring a Good Life through Participatory Audio/Visual Methods with People Labelled with an Intellectual Disability, Doctoral dissertation, Uni- versité d'Ottawa/University of Ottawa, Canada.
  7. Bricout J., Baker P.M., Moon N.W. and Sharma B. (2021). Exploring the smart future of participation: community, inclusivity, and people with disabilities. Int. J. E-Plan. Res., 10(2), 94-108.
  8. Collins A., Rentschler R., Williams K. and Azmat F. (2022). Exploring bar- riers to social inclusion for disabled people: perspectives from the performing arts. J. Manag. Organ., 28(2), 308-328.
  9. Debnath S., Roy P., Namasudra S. and Crespo R.G. (2022). Audio-visual automatic speech recognition towards education for disabilities. J. Autism Dev. Disord., 53, 3581-3594. Journal of Disability Research 2024
  10. Dixon K., Braye S. and Gibbons T. (2022). Still outsiders: the inclusion of disabled children and young people in physical education in England. Disabil. Soc., 37(10), 1549-1567.
  11. Edenberg E. and Wood A. (2023). Disambiguating algorithmic bias: from neutrality to justice. In: Proceedings of the 2023 AAAI/ACM Confer- ence on AI, Ethics, and Society, 8 Aug 2023; pp. 691-704.
  12. Fosch-Villaronga E. and Poulsen A. (2022). Diversity and inclusion in arti- ficial intelligence. In: Law and Artificial Intelligence: Regulating AI and Applying AI in Legal Practice; pp.109-134, Springer Nature.
  13. Furini M., Gaggi O., Mirri S., Montangero M., Pelle E., Poggi F., et al. (2022). Digital twins and artificial intelligence: as pillars of personal- ized learning models. Commun. ACM, 65(4), 98-104.
  14. Givens A.R. and Morris M.R. (2020). Centering disability perspectives in algorithmic fairness, accountability, & transparency. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transpar- ency; pp. 684-684.
  15. Ingavélez-Guerra P., Robles-Bykbaev V.E., Perez-Muñoz A., Hilera- González J., Otón-Tortosa S. (2022). Automatic adaptation of open educational resources: an approach from a multilevel methodology based on students' preferences, educational special needs, artificial intelligence and accessibility metadata. IEEE Access, 10, 9703-9716.
  16. Irugalbandara I.B., Naseem A.S., Perera M.S. and Logeeshan V. (2022) HomeIO: Offline Smart Home Automation System with Automatic Speech Recognition and Household Power Usage Tracking. In: 2022 IEEE World AI IoT Congress (AIIoT), 6 Jun 2022; pp. 571-577, IEEE.
  17. Kem D. (2022). Personalised and adaptive learning: emerging learning plat- forms in the era of digital and smart learning. Int. J. Soc. Sci. Human Res., 5(2), 385-391.
  18. Khoury M.J., Bowen S., Dotson W.D., Drzymalla E., Green R.F., Goldstein R., et al. (2022). Health equity in the implementation of genomics and precision medicine: a public health imperative. Genet. Med., 24(8), 1630-1639.
  19. Mao C. and Chang D. (2023). Review of cross-device interaction for facil- itating digital transformation in smart home context: a user-centric perspective. Adv. Eng. Inform., 57, 102087.
  20. Mohammad A., Alkhawaldeh E. and Khasawneh M.A. (2023). Harnessing the power of artificial intelligence for personalized assistive technology in learning disabilities. J. Southwest Jiaotong Univ., 58(4), 794-805.
  21. Morris M.R. (2020). AI and accessibility. Commun. ACM, 63(6), 35-37.
  22. Müftüoğlu Z., Kızrak M.A. and Yıldırım T. (2022). Privacy-preserving mechanisms with explainability in assistive AI technologies. In: Advances in Assistive Technologies: Selected Papers in Honour of Professor Nikolaos G. Bourbakis (George A. T., Maria V., Anna E., Lakhmi C. J., eds.), Vol. 3; pp. 287-309, Springer Nature.
  23. Nayak S. and Das R.K. (2020). Application of artificial intelligence (AI) in prosthetic and orthotic rehabilitation. In: Service Robotics (Sezer V., Öncü S. and Boyraz Baykas P., eds.) pp. 1-23, IntechOpen.
  24. Nazeer H. and Naseer N. (2022). Brain-controlled lower-limb exoskeleton to assist elderly and disabled. In: 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT), 17 May 2022, Vol. 1; pp. 827-830, IEEE.
  25. Patthanajitsilp P. and Chongstitvatana P. (2022). Obstacles detection for electric wheelchair with computer vision. In: 2022 14th International Conference on Knowledge and Smart Technology (KST), 26 Jan 2022; pp. 97-101, IEEE.
  26. Pydala B., Kumar T.P. and Baseer K.K. (2023). Smart_Eye: a navigation and obstacle detection for visually impaired people through smart app. J. Appl. Eng. Technol. Sci., 4(2), 992-1011.
  27. Roemmich K., Schaub F. and Andalibi N. (2023). Emotion AI at work: implications for workplace surveillance, emotional labor, and emo- tional privacy. In: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 19 Apr 2023; pp. 1-20.
  28. Sahal R., Alsamhi S.H. and Brown K.N. (2022). Personal digital twin: a close look into the present and a step towards the future of personal- ised healthcare industry. Sensors, 22(15), 5918.
  29. Satyavathi D.M., Rutwik A., Kumar N.A., Lokesh M., Kumar N.P. and Karthik P. (2023). AI & IoT enabled smart exoskeleton for rehablita- tion of a finger for paralysed people. J. Data Acquis. Process., 38(2), 4033.
  30. Shahabi S., Pardhan S., Shabaninejad H., Teymourlouy A.A., Tabrizi R. and Lankarani K.B. (2022). Toward good governance for the prosthetics and orthotics sector in Iran: evidence from a qualitative study. Pros- thet. Orthot. Int., 46(4), e398-406.
  31. Sharma T., Stangl A., Zhang L., Tseng Y.Y., Xu I., Findlater L., et al. (2023). Disability-first design and creation of a dataset showing private visual information collected with people who are blind. In: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 19 Apr 2023; pp. 1-15.
  32. Siregar N.C., Gumilar A., Warsito W., Amarullah A., and Rosli R. (2023). Enhancing STEM Learning for All: A Paper Concept of Accessible Resources. Ibn Khaldun Int. J. Appl. Sci. and Sustain., 1(1), 58-68.
  33. Slee R. and Tait G. (2022). Ethics and Inclusive Education: Disability, Schooling and Justice, Springer Nature.
  34. Taeihagh A. (2021). Governance of artificial intelligence. Policy Soc., 40(2), 137-157.
  35. Tilmes N. (2022). Disability, fairness, and algorithmic bias in AI recruit- ment. Ethics Inf. Technol., 24(2), 21.
  36. Valipoor M.M. and de Antonio A. (2023). Recent trends in computer vision- driven scene understanding for VI/blind users: a systematic mapping. Univers. Access Inf. Soc., 22(3), 983-1005.
  37. Walle H., De Runz C., Serres B. and Venturini G. (2022). A survey on recent advances in AI and vision-based methods for helping and guiding vis- ually impaired people. Appli. Sci., 12(5), 2308.
  38. Wieseler C. (ed.) (2022). Disability bioethics, Ashley X, and disability jus- tice for people with cognitive impairments. In: The Disability Bioethics Reader (Reynolds J.M. and Wieseler C., eds.), Routledge, New York.
  39. Wong P.K. (2022). Searching for a dynamic equilibrium in decision making: the voices of adults with mild intellectual disability and their signifi- cant others in Hong Kong. Disabil. Rehabil., 44(18), 5141-5151.
  40. Yang Q., Jin W., Zhang Q., Wei Y., Guo Z., Li X., et al. (2023). Mixed-mo- dality speech recognition and interaction using a wearable artificial throat. Nat. Mach. Intell., 5(2), 169-180.
  41. Zdravkova K. (2022). The potential of artificial intelligence for assistive technology in education. In: Handbook on Intelligent Techniques in the Educational Process: Vol 1 Recent Advances and Case Studies; pp. 61-85, Springer International Publishing, Cham.

Cited by

Sorted by citations

Authors
Almufareh, M. F., Kausar, S., Humayun, M., & Tehsin, S.
Affiliations
  • Jouf University (SA)
  • Bahria University (PK)
Corresponding author
Humayun, Mamoona
ORCID
Venue
Journal of Disability Research
Publisher
King Salman Center for Disability Research
Published
2024-01-04
Volume
3
Issue
1
ISSN
  • 1658-9912 (ISSN-L)
  • 2676-2633
DOI
10.57197/jdr-2023-0060
OpenAlex
W4390584313
Cited by
159
References
1
Citations by year
2026: 50 · 2025: 93 · 2024: 16
FWCI
135.25
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Disability Research (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
King Salman Center for Disability Research
Topics
  • Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Safety Research › Disability Rights and Representation
Keywords
artificial intelligence, disability inclusion, assistive technology, accessibility, voice recognition, smart glasses, wearable devices, exoskeleton, mobility (physical function), inclusive education, algorithmic bias, data privacy, digital divide, conceptual model, AI ethics

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