Reimagining Work-Integrated Learning in Rural School Contexts: Harnessing AI for Equitable Professional Development
DOI:
https://doi.org/10.38140/obp5-2026-09Keywords:
Artificial intelligence, equitable, harnessing, professional development, reimaginingAbstract
Work-Integrated Learning (WIL) is essential for the professional preparation of student teachers; however, persistent inequalities in rural areas hinder access to mentorship and professional development. This qualitative case study, grounded in Critical Pedagogy, examines how the integration of Artificial Intelligence (AI) into work-integrated learning (WIL) can promote equitable professional development within teacher education in South Africa's Eastern Cape Province. A purposive sample of 30 participants, including Heads of Department, teaching practice supervisors, and student teachers, engaged in semi-structured interviews and focus group discussions as data collection methods. The data were analysed thematically. The findings revealed that AI-supported WIL enhances access to mentorship and professional development by expanding networks and facilitating feedback in isolated settings. Additionally, the study highlighted those structural, ethical, and contextual factors, such as infrastructural inequities and data governance, significantly influence the implementation of AI-supported WIL. The study concluded that the use of AI tools necessitates critical, relational, human-centred supervision. Furthermore, it emphasised the importance of involving rural educators in the design of AI-supported systems to ensure relevance and maintain pedagogical integrity. The study recommended that teacher education institutions should critically focus on enhancing digital infrastructure in rural areas to establish robust ethical governance for AI and adopt hybrid mentorship models. These recommendations elucidate how AI can substantially improve equitable, human-centred work-integrated learning (WIL) in rural teacher education.
References
Acharya, D. B., Kuppan, K., & Divya, B. (2025). Agentic AI: Autonomous intelligence for complex goals—A comprehensive survey. IEEE Access, 13, 18912–18936. https://doi.org/10.1109/ACCESS.2025.1234567
Amo Filva, D., Balbín, A. M., Conde, M. Á., Fidalgo Blanco, Á., Fonseca, D., Gamazo, A., García Holgado, A., García Peñalvo, F. J., Hernández García, Á., & Martín Lucas, J. (2023). Trends on communication, educational assessment, educational innovation, identity, smart learning, and doctoral consortium. In Proceedings of the International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM 2023). Springer. https://doi.org/10.1007/978 981 97 1814 6_75
Badshah, A., Ghani, A., Daud, A., Jalal, A., Bilal, M., & Crowcroft, J. (2023). Towards smart education through Internet of Things: A survey. ACM Computing Surveys, 56(2), 1–33. https://doi.org/10.1145/3610401
Bagai, R., & Mane, V. (2024). Designing an AI powered mentorship platform for professional development: Opportunities and challenges. arXiv. https://arxiv.org/abs/2407.20233
Baker, R. S., & Hawn, A. (2022). Algorithmic bias in education. International Journal of Artificial Intelligence in Education, 32(4), 1052–1092. https://doi.org/10.1007/s40593 021 00285 9
Begum, N., & Gul, N. (2025). Bridging the gap: The role of technology in reducing educational inequality in developing countries. ASSAJ, 3(2), 2409–2421. https://doi.org/10.54254/2753 7064/2025.22478
Billett, S. (2025). Learning through work: Practices, purposes and outcomes. Routledge. https://doi.org/10.4324/9781003519416
Bircan, T., & Özbilgin, M. F. (2025). Unmasking inequalities of the code: Disentangling the nexus of AI and inequality. Technological Forecasting and Social Change, 211, Article 123925. https://doi.org/10.1016/j.techfore.2024.123925
Brandão, A., Pedro, L., & Zagalo, N. (2024). Teacher professional development for a future with generative artificial intelligence: An integrative literature review. Digital Education Review, 45, 151–157. https://doi.org/10.1344/der.2024.45.151 157
Braun, V., & Clarke, V. (2021). One size fits all? What counts as quality practice in reflexive thematic analysis? Qualitative Research in Psychology, 18(3), 328–352. https://doi.org/10.1080/14780887.2020.1769238
Bulathwela, S., Pérez Ortiz, M., Holloway, C., Cukurova, M., & Shawe Taylor, J. (2024). Artificial intelligence alone will not democratise education: On educational inequality, techno solutionism, and inclusive tools. Sustainability, 16(2), Article 781. https://doi.org/10.3390/su16020781
Bull, D. A. (2025). Impact of curriculum misalignment and assessment practices on student learning outcomes in higher education: A PRISMA-guided qualitative content synthesis. International Journal of Interdisciplinary Research and Innovations, 13(3), 65–87.
Bulut, O., Beiting Parrish, M., Casabianca, J. M., Slater, S. C., Jiao, H., Song, D., Ormerod, C. M., Fabiyi, D. G., Ivan, R., & Walsh, C. (2024). The rise of artificial intelligence in educational measurement: Opportunities and ethical challenges. arXiv. https://arxiv.org/abs/2406.18900
Cianferoni, F. (2025). AI in higher education: From literature to a course anchored chatbot (Doctoral dissertation). Politecnico di Torino.
Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education (8th ed.). Routledge.
Creswell, J. W., & Poth, C. N. (2016). Qualitative inquiry and research design: Choosing among five approaches. Sage.
Creswell, J. W., & Poth, C. N. (2017). Qualitative inquiry and research design: Choosing among five approaches. SAGE Publications.
Dhiman, M. T., Malik, M. A., Kumar, A., & Kamboj, M. G. (2025). Artificial intelligence synergies: A multidisciplinary outlook. Chyren Publication.
Du Plessis, A. E., & Dreyer, J. (2024). Reflections on initial teacher education with context-conscious pedagogy. Education Sciences, 14(5), Article 448. https://doi.org/10.3390/educsci14050448
Dyantyi, N., & Mkabile-Masebe, B. (2025). Closing the digital divide in higher education. Research in Social Sciences and Technology, 10(1), 412–425. https://doi.org/10.4102/aosis.2025.bk494.04
Essien, I. A., Nwokocha, G. C., Erigha, E. D., Obuse, E., & Olayiwola, A. (2022). The role of 5G in enabling smart cities: Policy, infrastructure, and societal impacts.
Facer, K., & Selwyn, N. (2021). Digital technology and the futures of education: Towards non-stupid optimism. UNESCO. https://doi.org/10.1057/9781137269881.0016
Fidalgo, P., & Thormann, J. (2024). The future of lifelong learning: The role of artificial intelligence and distance education. In Artificial intelligence and distance education. IntechOpen. https://doi.org/10.5772/intechopen.114120
Fobosi, S. C., & Malima, T. (2025). Unveiling inequality: Infrastructure development and social justice in rural Eastern Cape. Frontiers in Sociology, 9, Article 1481133. https://doi.org/10.3389/fsoc.2024.1481133
Freire, P. (1970). The adult literacy process as cultural action for freedom. Harvard Educational Review, 40(2), 205–225. https://doi.org/10.17763/haer.40.2.q7n227021n148p26
Frempong, D., Ifenatuora, G. P., & Ofori, S. D. (2020). AI-powered chatbots for education delivery in remote and underserved regions.
Ghamrawi, N., Shal, T., & Ghamrawi, N. A. (2024). Cultivating teacher leadership through transformative professional development. School Leadership & Management, 44(4), 413–441.
Gulson, K. N., Sellar, S., & Webb, P. T. (2022). Algorithms of education: How datafication and artificial intelligence shape policy. University of Minnesota Press.
Hadzic, S. (2024). South Africa's digital transformation: Understanding the limits of traditional policies and alternative approaches. Computer Law & Security Review, 55, Article 106011. https://doi.org/10.1016/j.clsr.2024.106011
Hillman, V. (2023). Bringing in the technological, ethical, educational, and social-structural for a new education data governance. Learning, Media and Technology, 48(1), 122–137. https://doi.org/10.1080/17439884.2022.2052313
Holmes, W., & Littlejohn, A. (2024). Artificial intelligence for professional learning. In Handbook of artificial intelligence at work (pp. 191–211). Edward Elgar Publishing.
Hu, S. (2024). The effect of artificial intelligence-assisted personalized learning on student learning outcomes: A meta-analysis based on 31 empirical research papers. Science Insights Education Frontiers, 24(1), 3873–3894. https://doi.org/10.15354/sief.24.re395
Hussein, S., Qureshi, S. S., & ul Emman, S. K. (2025). AI and digital inequality in education. Critical Review of Social Sciences Studies, 3(2), 143–157.
Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Artificial intelligence in education: Vision, challenges, and research issues. Computers & Education: Artificial Intelligence, 1, Article 100001. https://doi.org/10.1016/j.caeai.2020.100001
Joyce, K., Smith Doerr, L., Alegria, S., Bell, S., Cruz, T., Hoffman, S. G., Noble, S. U., & Shestakofsky, B. (2021). Toward a sociology of artificial intelligence. Socius, 7. https://doi.org/10.1177/2378023121999581
Karataş, F., & Yüce, E. (2024). Preservice teachers' reflections on the use of artificial intelligence. International Review of Research in Open and Distributed Learning, 25(3), 304–325. https://doi.org/10.19173/irrodl.v25i3.7785
Katende, R. (2025). Rethinking data efficient artificial intelligence for low resource settings. Machine Learning with Applications, —, Article 100796. https://doi.org/10.1016/j.mlwa.2025.100796
King, G., Keohane, R. O., & Verba, S. (2021). Designing social inquiry: Scientific inference in qualitative research. Princeton University Press.
Koukaras, C., Stavrinides, S. G., Hatzikraniotis, E., Mitsiaki, M., Koukaras, P., & Tjortjis, C. (2026). Navigating the future of education: A review on telecommunications and AI technologies, ethical implications, and equity challenges. Telecom.
Langeveldt, D. C., & Pietersen, D. (2024a). Decolonising artificial intelligence in education. Interdisciplinary Journal of Education Research, 6(s1), Article 7.
Langeveldt, D. C., & Pietersen, D. (2024b). Work integrated learning in a democratic South African context. Journal of Comparative and International Higher Education, 16(3), 208–218. https://doi.org/10.32674/jcihe.v16i3.5985
Littlejohn, A., & Pammer Schindler, V. (2022). Technologies for professional learning. In Research approaches on workplace learning: Insights from a growing field (pp. 321–346). Springer.
Min, A. (2023). Artificial intelligence and bias: Challenges, implications, and remedies. Journal of Social Research, 2(11).
Mnguni, L. (2025). A qualitative analysis of South African pre-service life sciences teachers' behavioural intentions for integrating AI in teaching. Journal for STEM Education Research, 8(2), 230–256.
Mohammed, S., & Malhotra, N. (2025). Ethical and regulatory challenges in machine learning based healthcare systems: A review of implementation barriers and future directions. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 5(1), Article 100215. https://doi.org/10.1016/j.bench.2024.100215
Mohseni, S., Zarei, N., & Ragan, E. D. (2021). A multidisciplinary survey and framework for design and evaluation of explainable AI systems. ACM Transactions on Interactive Intelligent Systems (TiiS), 11(3–4), 1–45. https://doi.org/10.1145/3387168
Mokoena, O. P., & Seeletse, S. M. (2025). AI in rural classrooms: Challenges and perspectives from South African educators. International Journal of Current Educational Studies, 4(2), 30–52.
Mulenga, R., & Shilongo, H. (2025). Hybrid and blended learning models: Innovations, challenges, and future directions in education. Acta Pedagogia Asiana, 4(1), 1–13.
Nazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. (2022). Teachers' trust in AI-powered educational technology and a professional development program to improve it. British Journal of Educational Technology, 53(4), 914–931. https://doi.org/10.1111/bjet.13214
Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B.-P. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221–4241. https://doi.org/10.1007/s10639-022-11316-w
Nwaimo, C. S., Oluoha, O. M., & Oyedokun, O. (2023). Ethics and governance in data analytics: Balancing innovation with responsibility. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 9(3), 823–856.
Omodan, B. I., & Marongwe, N. (2024). The role of artificial intelligence in decolonising academic writing for inclusive knowledge production. Interdisciplinary Journal of Education Research, 6(s1), 1–14.
Poth, C. N., & Searle, M. (2021). Media review: 30 essential skills for the qualitative researcher. Qualitative Research Journal, 21(3), 355–357.
Preiksaitis, C., & Rose, C. (2023). Opportunities and challenges of generative artificial intelligence in education. JMIR Medical Education, 9(1), e48785. https://doi.org/10.2196/48785
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210.
Rane, N. L., Chaudhari, R. A., & Rane, J. (2025). Critical pedagogies and artificial intelligence: Teaching, curriculum, and sustainable education. Deep Science Publishing.
Robson, C. (2024). Real world research (5th ed.). John Wiley & Sons.
Sajja, R., Sermet, Y., Cwiertny, D., & Demir, I. (2025). Integrating AI and learning analytics for data-driven pedagogical decisions and personalised interventions in education. Technology, Knowledge and Learning, Advance online publication, 1–31. https://doi.org/10.1007/s10758-024-09690-7
Taheri Hosseinkhani, N. (2025). Evaluating the economic impact, equity implications, and long-term prospects of AI-powered personalised learning in education by mid-century. Journal Name, 12(3), 45–67.
Umar, M. O., Oladimeji, O., Ajayi, J. O., Akindemowo, A. O., Eboseremen, B. O., Obuse, E., Ayodeji, D. C., & Erigha, E. D. (2021). Building technical communities in low-infrastructure environments: Strategies, challenges, and success metrics. International Journal of Multidisciplinary Futuristic Development, 2(1), 51–62.
Walker, L. (2024). Artificial narrow intelligence-driven diagnostics: Impacts, inequities, and policy imperatives in global healthcare (Doctoral dissertation). Technische Universität Wien.
Wang, T. (2026). AI literacy and the future of education. In Academic learning vs. everyday learning? (pp. 138–153). Routledge.
Williamson, B., Bayne, S., & Shay, S. (2020). The datafication of teaching in higher education: Critical issues and perspectives. In Vol. 25 (pp. 351-365). Taylor & Francis.
Yan, Y., Liu, H., & Chau, T. (2025). A systematic review of AI ethics in education: Challenges, policy gaps, and future directions. Journal of Global Information Management, 33(1), 1–50.
Yin, R. K. (2018). Case study research and applications (6th ed.). Sage.
Zembylas, M. (2023). A decolonial approach to AI in higher education teaching and learning: Strategies for undoing the ethics of digital neocolonialism. Learning, Media and Technology, 48(1), 25–37. https://doi.org/10.1080/17439884.2022.2052312
Zha, D., Bhat, Z. P., Lai, K.-H., Yang, F., Jiang, Z., Zhong, S., & Hu, X. (2025). Data-centric artificial intelligence: A survey. ACM Computing Surveys, 57(5), 1–42. https://doi.org/10.1145/3643364
Zhu, H., Sun, Y., & Yang, J. (2025). Toward responsible artificial intelligence in education. Humanities and Social Sciences Communications, 12(1). https://doi.org/10.1057/s41599-025-05252-6
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