The impact of AI-enhanced assistive technologies on the learning experiences of children with disabilities in Zimbabwe
DOI:
https://doi.org/10.64754/thedyke.v20i1.497Abstract
Educational inclusion for children with disabilities remains constrained by structural inequalities, limited specialist resources and persistent disparities in access to quality learning opportunities. Although specialised learning environments have traditionally relied on dedicated infrastructure and human support, such arrangements frequently reinforce educational segregation and restrict equitable participation in mainstream schooling, particularly in low-resource contexts. Artificial intelligence-enhanced assistive technologies (AI-AT) present a transformative opportunity to reconfigure these constraints by personalising learning, improving accessibility and extending instructional support beyond conventional pedagogical boundaries. Yet, evidence on their effectiveness within resource-constrained education systems remains limited. This study examines the influence of AI-AT on the learning experiences of children with disabilities in Zimbabwean primary schools, where infrastructural, financial and institutional limitations continue to shape inclusive education outcomes. Rather than assuming technological determinism, the study interrogates how contextual conditions mediate the educational value of AI-AT and the extent to which these technologies advance or reproduce existing inequalities. Guided by the Technology Acceptance Model and sociocultural theory, the study adopts a mixed-methods research design involving ten purposively selected primary schools in Zimbabwe. The findings demonstrate that AI-AT substantially enhances learner engagement, instructional accessibility, communication, personalised learning and participation, thereby expanding opportunities for meaningful educational inclusion. However, these gains are contingent upon institutional readiness, teacher digital competence, affordability and supportive policy environments. The analysis further reveals that algorithmic bias, data governance, cultural relevance and digital inequities represent systemic rather than merely technical challenges, with significant implications for equitable implementation. The study argues that the educational potential of AI-AT in developing countries depends less on technological sophistication than on its contextual adaptation within local pedagogical, socio-economic and cultural realities. It therefore contributes empirical evidence to debates on inclusive artificial intelligence by demonstrating that sustainable educational transformation requires the co-evolution of technology, institutional capacity and inclusive policy frameworks.
References
Anastasiou, D., Wiley, A. L., & Kauffman, J. M. (2025). A critical analysis of theoretical underpinnings of universal design for learning. Exceptionality, 33(3), 145–162. https://doi.org/10.1080/09362835.2024.2376523.
Baker, R. S., Corbett, A. T., Koedinger, K. R., & Roll, I. (2020). Enhancing learning through adaptive technologies. Journal of Educational Technology Systems, 49(3), 145–162.
Bartz, J. (2020). Challenges of implementing assistive technologies in Zimbabwe. Journal of Special Education Technology, 35(2), 123–135. https://doi. org/10.1177/0162643419881610.
Braun, V., & Clarke, V. (2024). Thematic analysis. In A. C. Michalos (Ed.), Encyclopedia of quality of life and well-being research (2nd ed., pp. 7187–7193). Springer. https://doi.org/10.1007/978-3-031-17299-1.
Chikozho, A. C., & Muziringa, M. (2021). The role of assistive technology in enhancing the educational experience of learners with special needs. University of Nebraska–Lincoln.
Cook, A. M., Polgar, J. M., & Encarnação, P. (2020). Assistive technologies. In Assistive technologies: Principles and practice (5th ed., pp. 321–355). Elsevier.
Creswell, J. W., & Plano Clark, V. L. (2023). Revisiting mixed methods research designs twenty years later. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods research designs (2nd ed., pp. 21–36). Sage.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008.
Djeki, E., Dégila, J., Bondiombouy, C., & Alhassan, M. H. (2024). Data protection in digital learning space: An overview. AIP Conference Proceedings, 3109(1), Article 030007. https://doi.org/10.1063/5.0203077.
Duncan, K., Smith, P., & Roberts, J. (2021). User-centred design in assistive technology development. Assistive Technology, 33(2), 75–84.
Farahani, M., & Ghasemi, G. (2024). Artificial intelligence and inequality: Challenges and opportunities. International Journal of Innovative Education, 9, 78–99.
Government of Zimbabwe. (2018). National disability policy. Government Printers.
Greene-Harper, R. (2023). The pros and cons of using AI in learning: Is ChatGPT helping or hindering learning outcomes? eLearning Industry.
Harris, L., & White, R. (2022). Policy frameworks for assistive technology implementation. Educational Policy Review, 34(1), 55–72.
Kulkarni, A. D. (2021). Artificial intelligence in assistive technology for people with disabilities. Nature, 591(7850), 353–360. https://doi.org/10.1038/d41586-021-00513-7.
Lewis, C., & Moje, E. B. (2003). Sociocultural perspectives meet critical theories. In N. Hall, J. Larson, & J. Marsh (Eds.), Handbook of early childhood literacy (pp. 197–212). Sage.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
Miller, S., Brown, T., & Johnson, L. (2022). Professional development for educators on assistive technologies. Teaching Exceptional Children, 54(3), 145–157. https://doi.org/10.1177/00400599211044782.
Mugumbate, J., & Cherenje, M. (2018). Barriers to the implementation of inclusive education in Zimbabwe. International Journal of Inclusive Education, 22(8), 821–835. https://doi.org/10.1080/13603116.2017.1412508.
Mupinga, D. M., & Mavhunga, B. (2022). The role of assistive technologies in enhancing education for students with disabilities in Zimbabwe. Journal of Educational Technology Development and Exchange, 15(1), 1–18.
Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice (4th ed.). Sage.
Quizlet. (2023, August 16). Quizlet's State of AI in Education survey reveals teachers are surprise AI champions [Press release]. PR Newswire.
Ripat, J., & Woodgate, R. (2011). The intersection of culture, disability and assistive technology. Disability and Rehabilitation: Assistive Technology, 6(2), 87–96. https://doi.org/10.3109/17483107.2010.507859.
Sen, A. (1999). Development as freedom. Alfred A. Knopf.
Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18, Article 54. https://doi.org/10.1186/s41239-021-00292-9.
Smith, J., & Jones, K. (2022). Longitudinal studies on AI impact in special education. Journal of Special Education Research, 15(2), 112–130.
United Nations. (1989). Convention on the Rights of the Child. Office of the United Nations High Commissioner for Human Rights. https://www.ohchr.org/en/instruments-mechanisms/instruments/convention-rights-child.
United Nations. (2006). Convention on the Rights of Persons with Disabilities. https://www.un.org/disabilities/documents/convention/convoptprot-e.pdf
United Nations. (2006). Convention on the Rights of Persons with Disabilities:
Article 24—Education. https://www.un.org/development/desa/disabilities/convention-on-the-rights-of-persons-with-disabilities/article-24-education.html
United Nations Department of Economic and Social Affairs. (n.d.). Factsheet on persons with disabilities. https://www.un.org/development/desa/disabilities/resources/factsheet-on-persons-with-disabilities.html.
UNICEF Zimbabwe. (2023). Innovative approaches to education for children with disabilities. UNICEF Zimbabwe.
U.S. Department of Education, Office of Educational Technology. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. U.S. Department of Education.
Vashishth, T. K., Sharma, V., Sharma, M. K., Sharma, K. K., & Sharma, R. (2025). The role of teachers in an AI-enhanced educational landscape. In AI adoption and diffusion in education (pp. 231–264). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-2157-3.ch010.
World Bank. (2024). Disability inclusion overview. https://www.worldbank.org/en/topic/disability
World Health Organization. (2021). Disability and health. https://www.who.int/news-room/fact-sheets/detail/disability-and-health.
World Health Organization. (2022). Global report on assistive technology. World Health Organization. https://www.who.int/publications/i/item/9789240049451
World Health Organization. (2024). Assistive technology. https://www.who.int/news-room/fact-sheets/detail/assistive-technology
Yeratziotis, A., Achilleos, A., Koumou, S., Zampas, G., Thibodeau, R. A., Geratziotis, G., … Kronis, C. (2023). Making social media applications inclusive for deaf end-users with access to sign language. Multimedia Tools and Applications, 82(29), 46185–46215. https://doi.org/10.1007/s11042-023-15469-4.
Yin, W. (2024). Will our educational system keep pace with AI? A student's perspective on AI and learning. EDUCAUSE Review. https://er.educause.edu/articles/2024.
ZainEldin, H., Gamel, S. A., Talaat, F. M., Aljohani, M., Baghdadi, N. A., Malki, A., … Elhosseini, M. A. (2024). Silent no more: A comprehensive review of artificial intelligence, deep learning, and machine learning in facilitating deaf and mute communication. Artificial Intelligence Review, 57(7), Article 188. https://doi.org/10.1007/s10462-024-10884-9.
Zdravkova, K., Krasniqi, V., Dalipi, F., & Ferati, M. (2022). Cutting-edge communication and learning assistive technologies for disabled children: An artificial intelligence perspective. Frontiers in Artificial Intelligence, 5, Article 970430. https://doi.org/10.3389/frai.2022.970430
Zhang, H., & Holden, S. T. (2025). Disability types and children's schooling in Africa. International Journal of Disability, Development and Education, 72(4), 763–783. https://doi.org/10.1080/1034912X.2024.2380196.
Zheng, Y., Li, X., & Leung, H. (2020). Artificial intelligence and education: A review of the literature. Computers & Education, 157, Article 103975. https://doi.org/10.1016/j.compedu.2020.103975.
ZimEduTech. (2023). Emerging technologies in Zimbabwean education: Challenges and opportunities.
https://education-profiles.org/sub-saharan-africa/zimbabwe/~technology
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Upon acceptance and publication, authors grant The Dyke a non-exclusive licence to publish and distribute their article in all formats and media. This means: Authors may freely share, deposit, and republish their work (e.g., in institutional repositories, websites, or future publications), provided proper citation and acknowledgment of The Dyke as the original publisher.
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles in The Dyke are published under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Under this licence:
- Others may copy, redistribute, remix, transform, and build upon the work for any purpose, even commercially.
- Appropriate credit must be given to the original author(s) and source (The Dyke), along with a link to the license.
- Any changes made must be indicated.
Full licence details: https://creativecommons.org/licenses/by/4.0/
Archiving and Preservation
The Dyke supports long-term preservation of scholarly work through partnerships with digital repositories and indexing services, including Sabinet African Journals and DOAJ. Authors are also encouraged to deposit a copy of their published article in institutional or subject-specific repositories.
