Artificial Intelligence as Assistive Technology: A Theoretical Framework of Capability Extension, Dependence and Dignity for Persons with Disabilities
DOI:
https://doi.org/10.68854/fr558873Keywords:
assistive technology, artificial intelligence, disability, capability approach, dependence, autonomy, dignity, human–AI interaction, assistive technology provision, AfricaAbstract
Artificial intelligence has turned ordinary consumer devices into assistive technologies. A phone now describes a street scene to a blind user, a cloud service captions a conversation for a deaf colleague, a decoder guesses the intended movement of a paralysed hand, and a chatbot helps an adult with intellectual disability think through a decision. The research literature evaluates these systems almost entirely on functional performance, and the problem this paper addresses is that functional performance says little about whether a disabled person's life has become larger, more secure or more their own. The purpose of the paper is to develop a framework for evaluating AI-based assistive technology beyond accuracy. It is a theoretical study. Forty peer-reviewed journal articles were retrieved under a 2023–2026 search window, primarily through PubMed and supplemented by web search; all forty retained articles date from 2024 to 2026. Each was verified against publisher records, screened for retractions, and read against the capability approach, the social model of disability and the human–AI interaction literature. The analysis distinguishes three effects any assistive AI produces at once (extending capability, creating dependence and shaping dignity) and shows that they routinely move in opposite directions: the same system that lets a person do more may bind them to a vendor, a data connection or a subscription, and may do so in a register they experience as diminishing. Eight propositions specify the design and provisioning conditions under which capability extension coincides with autonomy and dignity. A further analysis identifies the risks that AI adds to the older problems of assistive technology provision: probabilistic error in safety-critical settings, surveillance, obsolescence and affordability. The framework is situated in African provisioning conditions, where the distance between what the technology could do and what reaches disabled people is widest. Recommendations are directed at ministries, regulators, procurers, universities, designers and organisations of persons with disabilities.
References
Acheampong, E., Kasore, V., Kponyo, B. A., Owusu-Ansah, R. Y., Owusu-Ansah, J. K., Ansah, O., Owusu Ansah, E., Owusu Agyemang, J., Mensah Gyening, R.-M. O., Gyening, M., Ahene, E., & Kponyo, J. J. (2026). Barriers to assistive technology uptake among persons with disabilities in selected urban districts in Ghana. Scientific Reports, 16(1), 1838. https://doi.org/10.1038/s41598-025-31466-4
Alkhateeb, N. (2026). Generative AI as a cognitive assistive technology for adults with dyslexia: The role of anxiety, working memory, and metacognitive regulation. Disability and Rehabilitation: Assistive Technology. Advance online publication. https://doi.org/10.1080/17483107.2026.27152
Alsaleh, A. (2025). The influence of artificial intelligence on individuals with disabilities. Acta Psychologica, 262, 106010. https://doi.org/10.1016/j.actpsy.2025.106010 Álvarez-Aguado, I., Vega, V., Rosselló-Peñaloza, M., Aguado, L. Á., Vilo, K. C., Figueroa, J. F., La Rivera, F. M., González-Carrasco, F., & Parra, F. E. (2026). Conversational AI as personalised cognitive support: Adults with intellectual disability's experiences of everyday decision-making. Journal of Intellectual Disability Research. Advance online publication. https://doi.org/10.1111/jir.70171
Anderson, S., Seghers, F., Saunders, L., Reza, C., Chikhata, F., Bekele, E., Okiring, I., Mugambi, K., Alesinloye, G. I., & Mishra, S. (2026a). Building effective leadership and coordination for assistive technology: Best practices and recommendations from six countries across Sub-Saharan Africa. Disability and Rehabilitation: Assistive Technology. Advance online publication. https://doi.org/10.1080/17483107.2026.27174
Anderson, S., Seghers, F., Saunders, L., Reza, C., Chikhata, F., Bekele, E., & Mishra, S. (2026b). Developing National Priority Assistive Products Lists: Experiences and lessons from six Sub-Saharan African countries. Disability and Rehabilitation: Assistive Technology, 21(2), 668–684. https://doi.org/10.1080/17483107.2025.25612
Arystanbekov, B., Varol, H. A., & Yazici, A. (2026). Edge-based vision-language assistive system for the visually impaired: A quantized VLM approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering. Advance online publication. https://doi.org/10.1109/TNSRE.2026.3696447
Ashouri, D., Weh, L., Borrmann, V., Coenen, C., Kraft, E. M., Loufs, V., Mehnert, W., Muhr, P., Müller, O., Şahinol, M., Schmidt, M., Seyfried, G., Soedibjo, K., Stewart, I. E., Wolbring, G., Youssef, S., & Stieglitz, T. (2026). Neurotechnology through the lens of users and as a novel field for society. Frontiers in Neurology, 17, 1842746. https://doi.org/10.3389/fneur.2026.1842746
Bariffi, F. (2026). Mind, machine, and the law: Reimagining neurotechnology governance through disability rights. Journal of Law and the Biosciences, 13(1), lsag011. https://doi.org/10.1093/jlb/lsag011
Cave, R. (2024). How people living with amyotrophic lateral sclerosis use personalized automatic speech recognition technology to support communication. Journal of Speech, Language, and Hearing Research, 67(11), 4186–4202. https://doi.org/10.1044/2024_JSLHR-24-
Coffey, K. V., Krahn, G. L., Hanley, J. P., & Neely, J. E. (2026). Identifying implicit bias in LLM-based chat AI toward people with intellectual disabilities. Disability and Health Journal, 19(4), 102086. https://doi.org/10.1016/j.dhjo.2026.102086
Curati-Alasonatti, E., Layton, N., Kashyap, A., & Mishra, S. (2026). Lessons learnt from assistive technology ecosystem strengthening programmes in 12 low- and middle-income countries. Disability and Rehabilitation: Assistive Technology, 21(6), 3303–3316. https://doi.org/10.1080/17483107.2026.27121
Dalal, J., Teodoro, D., & Rivas Velarde, M. (2026). The promise and peril of AI for disability health equity: A justice-centered narrative review. PLOS Digital Health, 5(9), e0001663. https://doi.org/10.1371/journal.pdig.0001663
Du, W., Peng, X.-Y., Li, W., Attiq, U.-R., Li, X.-M., Feng, R., Zhong, K., Lu, X.-R., & Chen, H.-L. (2026). Mapping applications of artificial intelligence in social support for persons with disabilities: A systematic scoping review. Rehabilitation Psychology. Advance online publication. https://doi.org/10.1037/rep0000692
Gaiotto, E. M. G., Gabriel, M., Fernandes, M. F. P., de Andrade, O. G., Hedlund Jardim, N., Ramos, V. D., Souza, N. M., & Aguiar, B. G. A. (2026). Access to assistive technology in Africa: Findings from evidence-informed deliberative dialogues on trade and fiscal policies. Frontiers in Rehabilitation Sciences, 7, 1866962. https://doi.org/10.3389/fresc.2026.1866962
Giraldo, M., & Sacchi, F. (2026). From technical performance to assistive validity: A critical review of evaluation approaches for AI-enabled assistive technology. Healthcare, 14(18), 2900. https://doi.org/10.3390/healthcare14182900
Hendy, A. A., Rehim, M., Kaouache, R., & Yaiche, O. (2026). AI and digital assistive technologies and reproductive rights among women with disabilities in Egypt: A qualitative SWOT study. Frontiers in Sociology, 11, 1925235. https://doi.org/10.3389/fsoc.2026.1925235
Joskow, R., Patel, D., Landre, A., Mattick, K., Holloway, C., Danemayer, J., & Austin, V. (2025). Understanding the impact of assistive technology on users' lives in England: A capability approach. Bioengineering, 12(7), 750. https://doi.org/10.3390/bioengineering120707 Kaboré, S. S., Bayani, A., Ewane, L. P. E., Dos Anjos, D. S. O., Nikiéma, J. N., & Mac-Seing, M. (2026). Contribution of artificial intelligence research to the development of inclusive applications for people with disabilities: A scoping review (2015–2025). PLOS Digital Health, 5(10), e0001749. https://doi.org/10.1371/journal.pdig.0001749
Kamenopoulou, L., Prain, M., Bell, D., Maragia, M., Ranzato, E., & Layton, N. (2026). Exploring the assistive technology needs and rights of four Kenyan adults with deafblindness by using the My Assistive Technology Outcomes Framework. Disability and Rehabilitation: Assistive Technology, 21(7), 3639–3657. https://doi.org/10.1080/17483107.2026.26640
Kibret, A. K., Yilma, T. M., Davies, T. C., Alemayehu Genetu, Z., Mekonone, S. T., Alene, B. M., Addis, B. M., Nigussie, K., Ayele, R. G., & Mekonnen Araya, B. (2025). Assistive technology and daily living challenges among students with disabilities at University of Gondar, Ethiopia: A qualitative study. Disability and Rehabilitation: Assistive Technology, 20(1), 201–210. https://doi.org/10.1080/17483107.2024.23622
Kim, J., & Park, E. (2026). When the risk feels personal: Self-relative pessimism in AI-related risk perception among people with disabilities. Disability and Rehabilitation: Assistive Technology. Advance online publication. https://doi.org/10.1080/17483107.2026.27190
Lee, J. Y., Lee, S., Mishra, A., Yan, X., McMahan, B., Gaisford, B., Kobashigawa, C., Qu, M., Xie, C., & Kao, J. C. (2025). Brain–computer interface control with artificial intelligence copilots. Nature Machine Intelligence, 7(9), 1510–1523. https://doi.org/10.1038/s42256-025-01090-y
Mahmoudi, E. (2026). Techno-solutionism in the age of artificial intelligence: A biopsychosocialtech perspective for assistive technology. Disability and Rehabilitation: Assistive Technology, 21(6), 3330–3331. https://doi.org/10.1080/17483107.2026.26488
Marul, E. (2026). Assistive communication technologies for individuals with multiple disabilities: An evidence mapping review informing future AI-enabled AAC. Disability and Rehabilitation: Assistive Technology, 21(7), 3537–3558. https://doi.org/10.1080/17483107.2026.26824
Mekonnen, F. Y., Al Bataineh, M. F., Abu Abdoun, D., Serag, A., Tamiru, K. T., Abula, W., & Darota, S. (2025). Development of a fully autonomous offline assistive system for visually impaired individuals: A privacy-first approach. Sensors, 25(19), 6006. https://doi.org/10.3390/s25196006
Ogondiek, J. W. (2026). Policy-practice gap: A document analysis of assistive technology access for pupils with disabilities in Tanzania. Disability and Rehabilitation: Assistive Technology, 21(7), 3559–3572. https://doi.org/10.1080/17483107.2026.26831
Ortiz-Escobar, L. M., Chavarria, M. A., Hurst-Majno, S., Campo Salazar, O. I., Escobar-Hurtado, C., Stein, M. A., & Rivas Velarde, M. (2026). A social justice approach to assistive technology and well-being of people with visual disabilities in low- and middle-income countries: Qualitative narrative study. JMIR Rehabilitation and Assistive Technologies, 13, e72306. https://doi.org/10.2196/72306
Oxley, T. J., Deo, D. R., Cernera, S., Sawyer, A., Putrino, D., Ramsey, N. F., & Fry, A. (2025). The 'Brussels 4': Essential requirements for implantable brain–computer interface user autonomy. Journal of Neural Engineering, 22(1), 013002. https://doi.org/10.1088/1741-2552/ada0e6
Pakouyowou, M. T. (2026). From aid dependency to market transformation: Harnessing international trade and innovative business models to revitalize the assistive technology ecosystem in Africa. Frontiers in Rehabilitation Sciences, 7, 1905487. https://doi.org/10.3389/fresc.2026.1905487
Ryan, S., & O'Brien, R. (2024). Using a capability approach to explore how people with intellectual disabilities can lead flourishing lives. Journal of Applied Research in Intellectual Disabilities, 37(6), e13299. https://doi.org/10.1111/jar.13299
Singh, A., Bhanushali, M. A., Luo, J., Luo, G., & Pundlik, S. (2026). Assisting the blind to reach daily objects using smart glasses. Displays, 93, 103390. https://doi.org/10.1016/j.displa.2026.103390
Soldatic, K., Balram, R., Lee, M., Santilli, T., & Magee, L. (2026). Relational meanings of AI in disability care: An intersectional, arts-based inquiry. Qualitative Health Research, 36(2-3), 304–316. https://doi.org/10.1177/10497323251401541
Tobin, J., Nelson, P., MacDonald, B., Heywood, R., Cave, R., Seaver, K., Desjardins, A., Jiang, P.-P., & Green, J. R. (2024). Automatic speech recognition of conversational speech in individuals with disordered speech. Journal of Speech, Language, and Hearing Research, 67(11), 4176–4185. https://doi.org/10.1044/2024_JSLHR-24-
Umbelino, L. L., Ferreira, D. M. T. P., & Alonso, C. M. do C. (2026). Ergonomics in assistive technology design: Scoping review. Disability and Rehabilitation: Assistive Technology, 21(7), 3469–3489. https://doi.org/10.1080/17483107.2026.26718
Urqueta Alfaro, A., Roundtree, K. A., Pfaff, M. S., Bos, N., Edell, J. D., & ten-Brink, R. (2026). Automatic speech recognition underperforms with diverse accents as measured by word error rate and a semantic similarity measure. Disability and Rehabilitation: Assistive Technology. Advance online publication. https://doi.org/10.1080/17483107.2026.27165
Wade, W., Hu, S., & Fleming, J. (2026). Market shaping via digital public infrastructure for assistive technology in low- and middle-income countries. Frontiers in Rehabilitation Sciences, 7, 1881812. https://doi.org/10.3389/fresc.2026.1881812
Wilkens, J., Banerji, R., & Mishra, S. (2026). Financing assistive technology: Policy options on the path towards universal health coverage. Frontiers in Public Health, 14, 1901297. https://doi.org/10.3389/fpubh.2026.1901297
Zandam, H., Sulaiman, S. K., Mohammad, A. H., & Sabo, A. (2025). Utilization and impact of assistive device use on mental health among children with disabilities in Nigeria: A cross-sectional study. Disability and Rehabilitation: Assistive Technology, 20(7), 2094–2102. https://doi.org/10.1080/17483107.2025.25240
Zhao, R., Choi, A. S. G., Koenecke, A., & Rameau, A. (2025). Quantification of automatic speech recognition system performance on d/Deaf and hard of hearing speech. The Laryngoscope, 135(1), 191–197. https://doi.org/10.1002/lary.31713
Downloads
Published
Issue
Section
License
Copyright (c) 2026 IIAR Journal of Disability, Inclusion, and Assistive Technologies
This work is licensed under a Creative Commons Attribution 4.0 International License.
