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Systematic Review

Digital Exploitation in E-Learning: Platform Power, Datafication, Learner Agency, and The Emergence of Digital Educational Justice

Stephen Muhala Kuyuni

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Stephen Muhala Kuyuni Corresponding Author

Department of Business Innovation, Osiri University, USA.

Correspondence: stephenkuyuni@gmail.com

Received
26 Aug 2026
Published
11 Sep 2026

Abstract

The rapid expansion of digital and online education has transformed access to learning, communication, assessment, and educational resources. However, the increasing dependence of education on digital platforms raises critical questions concerning learner autonomy, datafication, privacy, platform dependency, artificial intelligence (AI), commercialization, and the distribution of value generated through digital participation. This article develops the concept of Digital Exploitation in E-Learning (DEE) and proposes a theoretical framework for understanding how apparently beneficial digital educational systems may produce extractive relationships under conditions of unequal technological and informational power. Drawing upon a critical synthesis of scholarship on platformization, datafication, learner agency, artificial intelligence, digital inequality, and educational governance, this study argues that digital exploitation cannot be reduced to financial overcharging or technological exclusion. Rather, it involves the systematic extraction of financial, temporal, informational, academic, behavioral, and attentional value from learners while their capacity to understand, negotiate, or challenge these processes remains constrained. Evidence concerning learner ownership emphasizes learner-centered design, accessible infrastructure, and resilient support ecosystems, while research in Global South contexts identifies heightened concerns surrounding access, AI readiness, privacy, agency, and systemic inequality (Sembiring, 2026, pp. 649–650; Van den Berg, 2026, pp. 807–808). The article proposes an integrated DEE Model connecting digitalization, platformization, dependency, information asymmetry, datafication, value extraction, and agency/capability effects, moderated by digital literacy, transparency, privacy protection, human oversight, accountability, and redress. Ultimately, this study contributes a theory-building framework for future empirical research and advances the conceptual foundations of Digital Educational Justice.

Keywords: Digital Exploitation in E-Learning; E-Learning, Platformization, Datafication, Learner Agency, Artificial Intelligence, Digital Inequality, Educational Governance, Digital Educational Justice.

How to Cite

APA

Kuyuni, S. M. (2026). Digital Exploitation in E-Learning: Platform Power, Datafication, Learner Agency, and The Emergence of Digital Educational Justice. Journal of Contemporary Academic Research and Methodologies, 1(7). https://doi.org/10.5281/zenodo.22704934

MLA

Kuyuni, Stephen Muhala. "Digital Exploitation in E-Learning: Platform Power, Datafication, Learner Agency, and The Emergence of Digital Educational Justice." Journal of Contemporary Academic Research and Methodologies, vol. 1, no. 7, 2026. DOI: https://doi.org/10.5281/zenodo.22704934

Chicago

Kuyuni, Stephen Muhala. "Digital Exploitation in E-Learning: Platform Power, Datafication, Learner Agency, and The Emergence of Digital Educational Justice." Journal of Contemporary Academic Research and Methodologies 1, no. 7 (2026). https://doi.org/10.5281/zenodo.22704934

References

Bulathwela, S., Pérez-Ortiz, M., Holloway, C., Cukurova, M., & Shawe-Taylor, J. (2024). Artificial intelligence alone will not democratize education: On educational inequality, techno-solutionism and inclusive tools. Sustainability, 16(2), Article 781. https://doi.org/10.3390/su16020781 Drachsler, H., & Greller, W. (2016). Privacy and analytics. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 89–98). Association for Computing Machinery. https://doi.org/10.1145/2883851.2883893 Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J., Smyth, P., Yu, R., & Warschauer, M. (2020). Mining big data in education: Affordances and challenges. Review of Research in Education, 44(1), 130–160. https://doi.org/10.3102/0091732X20903304 Organization for Economic Co-operation and Development. (2026). OECD digital education outlook 2026: Exploring effective uses of generative AI in education. OECD Publishing. Prinsloo, P., & Slade, S. (2017). Ethics and learning analytics: Charting the (un)charted. In C. Lang, G. Siemens, A. Wise, & D. Gašević (Eds.), Handbook of learning analytics (pp. 49–57). Society for Learning Analytics Research. https://doi.org/10.18608/hla17.004 Selwyn, N., & Gašević, D. (2020). The datafication of higher education: Discussing the promises and problems. Teaching in Higher Education, 25(4), 527–540. https://doi.org/10.1080/13562517.2019.1689388 Sembiring, M. G. (2026). Ownership of learning in distance education: Building accessible and resilient pathways to lifelong learning. In ICDE World Conference 2025 full paper proceedings (pp. 649–659). International Council for Open and Distance Education. Tsai, Y.-S., Perrotta, C., & Gašević, D. (2020). Empowering learners with personalized learning approaches? Agency, equity and transparency in the context of learning analytics. Assessment & Evaluation in Higher Education, 45(4), 554–567. https://doi.org/10.1080/02602938.2019.1676396 UNESCO. (2023). Guidance for generative AI in education and research. United Nations Educational, Scientific and Cultural Organization. Van den Berg, G. (2026). Artificial intelligence in sustainable teacher education: Readiness and perspectives of pre-service teachers in open distance learning. In ICDE World Conference 2025 full paper proceedings (pp. 807–821). International Council for Open and Distance Education. Williamson, B. (2017). Big data in education: The digital future of learning, policy and practice. SAGE Publications. https://doi.org/10.4135/9781529714920 Williamson, B., Eynon, R., & Potter, J. (2020). Pandemic politics, pedagogies and practices: Digital technologies and distance education during the coronavirus emergency. Learning, Media and Technology, 45(2), 107–114. https://doi.org/10.1080/17439884.2020.1761641 Yu, H., Shen, Z., Miao, C., Leung, C., Lesser, V. R., & Yang, Q. (2018). Building ethics into artificial intelligence. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (pp. 5527–5533). AAAI Press. 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 1. https://doi.org/10.1186/s41239-019-0171-0 Zhang, X., Zhang, P., Shen, Y., Liu, M., Wang, Q., Gašević, D., & Fan, Y. (2024). A systematic literature review of empirical research on applying generative artificial intelligence in education. Frontiers of Digital Education, 1(3), 223–245. https://doi.org/10.1007/s44366-024-0028-5 Zook, M., Barocas, S., Boyd, D., Crawford, K., Keller, E., Gangadharan, S. P., & Pasquale, F. (2017). Ten simple rules for responsible big data research. PLoS Computational Biology, 13(3), Article e1005399. https://doi.org/10.1371/journal.pcbi.1005399
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ISSN 3139-7247
Tracking ID JCARM_AUG_26_070
Article No. 032
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Article Info
Journal JCARM
Volume Vol 1, No 7
Year 2026
Type Systematic Review
Licence CC BY-NC-SA 4.0
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