Student teachers' narratives on artificial intelligence (AI)-personalised learning in geography and social sciences teaching at a South African university
DOI:
https://doi.org/10.38140/ijer-2025.vol7.2.12Keywords:
Student teachers, AI-personalised learning, Geography, Social sciences teaching, digital pedagogy, technology in educationAbstract
This study investigates student experiences and perceptions of AI-personalised learning in Geography and Social Sciences teaching at a South African university in the Eastern Cape province. Operating within an interpretivist paradigm, the research adopted a qualitative approach with an explanatory case study design. Data were collected via open-ended questionnaires from a purposive sample of 15 undergraduate students who had direct experience with AI-personalised learning tools in their Geography and Social Sciences teaching modules. Thematic analysis revealed four dominant themes: enhanced understanding and simplification of concepts; personalised support and learning autonomy; accessibility and contextual gaps; and real-world application and engagement. Students perceived AI tools as personalised tutors that aided comprehension and fostered self-directed learning. However, the study also identified significant challenges, particularly the digital divide and limited technology access, which risk exacerbating existing inequalities. The study contributes to the literature by foregrounding student narratives from the Global South and emphasising the need for contextually relevant and inclusive approaches to AI integration in higher education.
References
Luckin, R., & Holmes, W. (2016). Intelligence unleashed: An argument for AI in education. Open Ideas; Pearson Education.
Maphalala, M. C., Mkhasibe, R. G., & Mncube, D. W. (2025). Exploring the roles of AI-powered e-tutors in enhancing self-directed learning in open distance e-learning courses. Interdisciplinary Journal of Education Research, 7(1), Article a12. https://doi.org/10.38140/ijer-2025.vol7.1.12
Merriam, S. B., & Tisdell, E. J. (2016). Qualitative research: A guide to design and implementation (4th ed.). Jossey-Bass.
Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x
Mpungose, C. (2020). Emergent transition from face-to-face to online learning in a South African university in the context of the Coronavirus pandemic. Humanities and Social Sciences Communications, 7, Article 113. https://doi.org/10.1057/s41599-020-00603-x
Mulaudzi, L., & Hamilton, J. (2024). Student perspectives on optimising AI tools to enhance personalised learning in higher education. Interdisciplinary Journal of Education Research, 6(S1), 1-15. https://doi.org/10.38140/ijer-2024.vol6.s1.03
Ngary, C., & Twum-Darko, M. (2024). Enhancing student performance in South African higher education through machine learning and big data analytics: A theoretical framework. Scientific Research, New Technologies and Applications, 6, 107–122. https://doi.org/10.9734/bpi/srnta/v6/2135
Omodan, B. I. (2022). A model for selecting theoretical framework through epistemology of research paradigms. African Journal of Inter/Multidisciplinary Studies, 4(1), 275–285. https://doi.org/10.51415/ajims.v4i1.1022
Opesemowo, O., & Adekomaya, V. (2024). Harnessing artificial intelligence for advancing sustainable development goals in South Africa’s higher education system: A qualitative study. International Journal of Learning, Teaching and Educational Research, 2(3), 67–86. https://doi.org/10.26803/ijlter.23.3.4
Reddy Moonasamy, A., & Naidoo, G. (2022). Digital learning: Challenges experienced by South African university students during the COVID-19 pandemic. The Independent Journal of Teaching and Learning, 17(2), 76–90. https://doi.org/10.17159/fwk81482
Saal, P. E., Chetty, K., Ntshayintshayi, N., Moosa, T., & Masuku, N. (2025). A scoping review of the integration of artificial intelligence in primary and secondary schools from 2020 to 2024: Policy implications for South Africa. Journal of Education (University of KwaZulu-Natal), 98, 62-85. https://doi.org/10.17159/2520-9868/i98a04
Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th ed.). Pearson Education Limited.
Schorr, A. (2023). The technology acceptance model (TAM) and its importance for digitalization research: A review. Proceedings TecPsy, 2023, Article 55. http://dx.doi.org/10.2478/9788366675896-005
Sun, L., Kangas, M., Ruokamo, H., & Siklander, S. (2023). A systematic literature review of teacher scaffolding in game-based learning in primary education. Educational Research Review, 40, Article 100546. https://doi.org/10.1016/j.edurev.2023.100546
Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. https://doi.org/10.1080/17439884.2020.1798995
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Zenande Nonkula
This work is licensed under a Creative Commons Attribution 4.0 International License.
