Bridging the Digital Divide: Equitable Access to AI-Enhanced Geographical Work Integrated Learning in Marginalised Landscapes
DOI:
https://doi.org/10.38140/obp5-2026-02Keywords:
WAI-enhanced learning, geo-digital divide, mobile-first hybrid placements, spatial justice, work-integrated learningAbstract
This chapter confronts the alluring promise of Artificial Intelligence (AI) in geographical Work-Integrated Learning (WIL) with the stark reality of its potential to perpetuate and deepen existing socio-spatial inequalities. It addresses the critical issue of how the integration of AI risks exacerbating the very disparities it claims to resolve. While AI purports to democratise access through virtual placements and sophisticated analytics, its implementation often assumes the presence of robust digital infrastructure and high AI literacy, thereby creating a new "geo-digital divide" for students in marginalised geographies, including rural, low-income, and Global South contexts. Through a systematic literature review of relevant academic scholarship, framed by a synthesis of Critical Digital Pedagogy and Spatial Justice theories, this chapter interrogates the normative assumptions underpinning "high-tech" WIL models. Findings reveal that AI tools can perpetuate epistemic injustice without deliberate intervention by marginalising local knowledge and reinforcing technological dependency. Conversely, the chapter identifies emergent, innovative strategies such as mobile-first hybrid placements, community-partnered projects employing participatory geospatial technologies, and embedded "critical AI literacy" modules that leverage appropriate technology to foster inclusive and contextually relevant geographical WIL. Thus, this study provides a robust, theoretically grounded framework for the equitable design of AI-enhanced geographical WIL. The chapter advocates for asset-based partnerships among educators and policymakers, the co-design of curricula with local communities, and a pedagogical reorientation that utilises AI to amplify, rather than replace, situated geographical understanding in order to empower spatial citizenship for all.
References
Abdelwahab, H. R., Rauf, A., & Chen, D. (2023). Business students' perceptions of Dutch higher educational institutions in preparing them for artificial intelligence work environments. Industry and Higher Education, 37(1), 22-34. https://doi.org/10.1177/09504222221087614
Adam, T. (2019). Digital neocolonialism and massive open online courses (MOOCs): Colonial pasts and neoliberal futures. Learning, Media and Technology, 44(3), 365-380. https://doi.org/10.1080/17439884.2019.1640740
Addie, J. P. D., Glass, M. R., & Nelles, J. (2020). Regionalizing the infrastructure turn: A research agenda. Regional Studies, Regional Science, 7(1), 10-26. https://doi.org/10.1080/21681376.2019.1701543
Ames, C. J., Shaw, M., O'Driscoll, C. A., & Mackay, A. (2020). A multi-user mobile GIS solution for documenting large surface scatters: An example from the Doring River, South Africa. Journal of Field Archaeology, 45(6), 394-412. https://doi.org/10.1080/00934690.2020.1753321
Antoninis, M., Alcott, B., Al Hadheri, S., April, D., Fouad Barakat, B., Barrios Rivera, M., & Weill, E. (2023). Global Education Monitoring Report 2023: Technology in education: A tool on whose terms? https://doi.org/10.54676/aatw1274
Aragani, V. M., Anumolu, V. R., & Selvakumar, P. (2025). Democratization in the age of algorithms: Navigating opportunities and challenges. Democracy and Democratization in the Age of AI, 39-56. https://doi.org/10.4018/979-8-3693-8749-8.ch003
Arora, A., Barrett, M., Lee, E., Oborn, E., & Prince, K. (2023). Risk and the future of AI: Algorithmic bias, data colonialism, and marginalization. Information and Organization, 33(3), 100478. https://doi.org/10.1016/j.infoandorg.2023.100478
Ash, J., Kitchin, R., & Leszczynski, A. (2018). Digital turn, digital geographies? Progress in Human Geography, 42(1), 25-43. https://doi.org/10.1177/0309132516664800
Assumpção, T. H., Popescu, I., Jonoski, A., & Solomatine, D. P. (2018). Citizen observations contributing to flood modelling: Opportunities and challenges. Hydrology and Earth System Sciences, 22(2), 1473-1489. https://doi.org/10.5194/hess-22-1473-2018
Baroud, J., & Dharamshi, P. (2020). A collaborative self-study of critical digital pedagogies in teacher education. Studying Teacher Education, 16(2), 164-182. https://doi.org/10.1080/17425964.2020.1739639
Birhane, A., Steed, R., Ojewale, V., Vecchione, B., & Raji, I. D. (2024). AI auditing: The broken bus on the road to AI accountability. In 2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) (pp. 612-643). IEEE. https://doi.org/10.1109/satml59370.2024.00037
Cegielski, O. M. (2023). Making sense of technological change in the post-COVID era: Digital leadership and equitable learning opportunities in Colorado's PK-12 schools. University of Colorado- Colorado Springs.
Chanda, K. Z. (2023). The role of artificial intelligence in fostering workplace integrated learning at a South African TVET college during COVID-19. University of Johannesburg (South Africa).
Cilia, L. (2020). 'We don't know much about bees!' Techno‐optimism, techno‐scepticism, and denial in the American large‐scale beekeeping industry. Sociologia Ruralis, 60(1), 83-103. https://doi.org/10.1111/soru.12280
Connell, R. (2020). Southern theory: The global dynamics of knowledge in social science. Routledge. https://doi.org/10.4324/9781003117346
Dean, B. A., & Campbell, M. (2020). Reshaping work-integrated learning in a post-COVID-19 world of work. International Journal of Work-Integrated Learning, 21(4), 355-364.
Earnshaw, R. (2019). Visual computing. In Data science and visual computing (pp. 33-38). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-24367-8_3
Fainstein, S. S. (2016). Spatial justice and planning. Readings in planning theory, 4, 258-272. https://doi.org/10.1002/9781119084679.ch13
Figaredo, D. D. (2020). Data-driven educational algorithms pedagogical framing. RIED-Revista Iberoamericana de Educación a Distancia, 23(2), 65-84. https://doi.org/10.5944/ried.23.2.26470
Govender, C. M., & Våland, T. I. (2021). Work integrated learning for students: Challenges and solutions for enhancing employability. Cambridge Scholars Publishing.
Gram-Hansen, B. J., Helber, P., Varatharajan, I., Azam, F., Coca-Castro, A., Kopackova, V., & Bilinski, P. (2019). Mapping informal settlements in developing countries using machine learning and low resolution multi-spectral data. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (pp. 361-368). https://doi.org/10.1145/3306618.3314253
Gutiérrez-Ujaque, D. (2024). Towards a critical digital literacy and consciousness in higher education: The emancipatory role of critical digital pedagogy. Pedagogies: An International Journal, 19(3), 337-371. https://doi.org/10.1080/1554480x.2024.2379787
Hafeznia, M. R., & Ghaderi-Hajat, M. (2015). Conceptualization of spatial justice in political geography. Geopolitics Quarterly, 11(40), 32-60.
Halvorsen, S. (2017). Spatial dialectics and the geography of social movements: The case of Occupy London. Transactions of the Institute of British Geographers, 42(3), 445-457. https://doi.org/10.1111/tran.12179
Hunter, F. D., Mitchard, E. T., Tyrrell, P., & Russell, S. (2020). Inter-seasonal time series imagery enhances classification accuracy of grazing resource and land degradation maps in a savanna ecosystem. Remote Sensing, 12(1), 198. https://doi.org/10.3390/rs12010198
Hutson, J. (Ed.). (2024). The rise of AI in academic inquiry. IGI Global.https://doi.org/10.4018/979-8-3693-69050
Kakhkharova, M., & Tuychieva, S. (2024). AI-enhanced pedagogy in higher education: Redefining teaching-learning paradigms. In 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS) (Vol. 1, pp. 1-6). IEEE. https://doi.org/10.1109/ickecs61492.2024.10616893
Kiriri, P. N. (2019). An assessment of the quality of a work-integrated learning internship program in Kenya. International Journal of Work-Integrated Learning, 20(3), 257-271.
Lane, R. (2025). Mitigating risks, embracing potential: A framework for integrating generative artificial intelligence in geographical and environmental education. International Research in Geographical and Environmental Education, 1-18. https://doi.org/10.1080/10382046.2025.2458561
Liu, B., Zeng, W., Liu, W., Peng, Y., & Yao, N. (2025). AI-enhanced design and application of high school geography field studies in China: A case study of the Yellow (Bohai) Sea migratory bird habitat curriculum. Algorithms, 18(1), 47. https://doi.org/10.3390/a18010047
Markham, A. N. (2019). Critical pedagogy as a response to datafication. Qualitative Inquiry, 25(8), 754-760. https://doi.org/10.1177/1077800418809470
Masood, M. M., & Haque, M. M. (2021). From critical pedagogy to critical digital pedagogy: A prospective model for the EFL classrooms. Saudi Journal of Language Studies, 1(1), 67-80. https://doi.org/10.1108/sjls-03-2021-0005
Matli, W., & Ngoepe, M. (2020). Capitalising on digital literacy skills for capacity development of people who are not in education, employment or training in South Africa. African Journal of Science, Technology, Innovation and Development, 12(2), 129-139. https://doi.org/10.1080/20421338.2019.1624008
Mohr, S., & Kühl, R. (2021). Acceptance of artificial intelligence in German agriculture: An application of the technology acceptance model and the theory of planned behaviour. Precision Agriculture, 22(6), 1816-1844. https://doi.org/10.1007/s11119-021-09814-x
Morris, S. M., & Stommel, J. (2018). An urgency of teachers: The work of critical digital pedagogy. Hybrid Pedagogy. https://doi.org/10.7208/chicago/9780226469591.003.0012
Ndaka, A., Legun, K., & Campbell, H. (2025). Toward sustainable AI techno-futures? In Oxford Intersections: AI in Society. https://doi.org/10.1093/9780198945215.003.0089
Nost, E., & Colven, E. (2022). Earth for AI: A political ecology of data-driven climate initiatives. Geoforum, 130, 23-34. https://doi.org/10.1016/j.geoforum.2022.01.016
Nyamtiga, B. W., Hermawan, A. A., Luckyarno, Y. F., Kim, T. W., Jung, D. Y., Kwak, J. S., & Yun, J. H. (2022). Edge-computing-assisted virtual reality computation offloading: An empirical study. IEEE Access, 10, 95892-95907. https://doi.org/10.1109/access.2022.3205120
Pfotenhauer, S., & Jasanoff, S. (2017). Panacea or diagnosis? Imaginaries of innovation and the 'MIT model' in three political cultures. Social Studies of Science, 47(6), 783-810. https://doi.org/10.1177/0306312717706110
Pierce, J. (2022). How can we share space? Ontologies of spatial pluralism in Lefebvre, Butler, and Massey. Space and Culture, 25(1), 20-32. https://doi.org/10.1177/1206331219863314
Pimenow, S., Pimenowa, O., Prus, P., & Niklas, A. (2025). The impact of artificial intelligence on the sustainability of regional ecosystems: Current challenges and future prospects. Sustainability, 17(11), 4795. https://doi.org/10.3390/su17114795
Poquet, O., & De Laat, M. (2021). Developing capabilities: Lifelong learning in the age of AI. British Journal of Educational Technology, 52(4), 1695-1708. https://doi.org/10.1111/bjet.13123
Raghubar, A. (2021). Relevance of work integrated learning (WIL) in the geomatics programme at the Durban University of Technology [Doctoral dissertation, Durban University of Technology]. https://doi.org/10.51415/10321/4462
Rakuasa, H. (2023). Integration of artificial intelligence in geography learning: Challenges and opportunities. Sinergi International Journal of Education, 1(2), 75-83. https://doi.org/10.61194/education.v1i2.71
Riveros, A., & Nyereyemhuka, N. (2023). Conceptualizing space in educational administration and leadership research: Towards a spatial justice perspective. International Journal of Leadership in Education, 26(4), 666-679. https://doi.org/10.1080/13603124.2020.1842509
Şanlı, C. (2025). Artificial intelligence in geography teaching: Potentialities, applications, and challenges. International Journal of Current Educational Studies, 4(1), 47-76. https://doi.org/10.46328/ijces.170
Sarkar, A., Pick, J. B., & Johnson, J. (2015). Africa's digital divide: Geography, policy, and implications.
Scott, S. B. (2015). Spatial justice: Measuring justice outcomes from regeneration programmes. Conference on New Urban Languages, TU Delft, Netherlands.
Sey, A., & Mudongo, O. (2021). Case studies on AI skills capacity building and AI in workforce development in Africa. Research ICT Africa.
Shin, E. E., & Bednarz, S. W. (2019). Spatial citizenship education. New York: Routledge.
Simon, J. P. (2019). Artificial intelligence: Scope, players, markets and geography. Digital Policy, Regulation and Governance, 21(3), 208–237. https://doi.org/10.1108/dprg-08-2018-0039
Trento-Oliveira, L., Kuffer, M., Schwarz, N., & Pedrassoli, J. C. (2023). Capturing deprived areas using unsupervised machine learning and open data: A case study in São Paulo, Brazil. European Journal of Remote Sensing, 56(1), 2214690. https://doi.org/10.1080/22797254.2023.2214690
Trianasari, N., & Permadi, T. A. (2024). Analysis of product recommendation models at each fixed broadband sales location using K-means, DBSCAN, hierarchical clustering, SVM, RF, and ANN. Journal of Applied Data Sciences, 5(2), 636–652. https://doi.org/10.47738/jads.v5i2.210
Tuomi, I., Cachia, R., & Villar-Onrubia, D. (2023). On the futures of technology in education: Emerging trends and policy implications. Publications Office of the European Union, 10, 079734.
Vahidi, M., Shafian, S., Thomas, S., & Maguire, R. (2023). Estimation of bale grazing and sacrificed pasture biomass through the integration of Sentinel satellite images and machine learning techniques. Remote Sensing, 15(20), 5014. https://doi.org/10.3390/rs15205014
Walker, S. W. (2013). The relationship of critical digital literacy to the learning design conceptual map. In IEEE 63rd Annual Conference International Council for Educational Media (ICEM). https://doi.org/10.1109/CICEM.2013.6820235
Walshe, N., & Healy, G. (2020). Geography education in the digital world. Routledge. https://doi.org/10.4324/9780429274909
Wang, N., Naz, I., Aslam, R. W., Quddoos, A., Soufan, W., Raza, D., & Ahmed, B. (2024). Spatio-temporal dynamics of rangeland transformation using machine learning algorithms and remote sensing data. Rangeland Ecology & Management, 94, 106-118. https://doi.org/10.1016/j.rama.2024.02.008
Won, A. S. (2015). The virtual human interaction lab. XRDS: Crossroads, The ACM Magazine for Students, 22(1), 64-65. https://doi.org/10.1145/2830324
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Copyright (c) 2026 Tolulope Ayodeji Olatoye, Thulasizwe Fredrick Mkhize, Raymond Nkwenti Fru
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