International Journal of E-Learning & Distance Education / Revue internationale du e-learning et la formation à distance

Vol. 40 No. 2 (2025)
Research Articles

Navigating Online Learning and Artificial Intelligence: Identifying and Managing Assessment Risks in Private Higher Education in South Africa

HTML PDF
Godson Chinenye Nwokocha
STADIO Higher Education
Bio
Jolanda De Villiers Morkel
STADIO Higher Education, South Africa
Bio
DOI: https://doi.org/10.55667/10.55667/ijede.2025.v40.i2.1385

Published 2025-12-08

How to Cite

Nwokocha, G. C., & Morkel, J. D. (2025). Navigating Online Learning and Artificial Intelligence: Identifying and Managing Assessment Risks in Private Higher Education in South Africa. International Journal of E-Learning & Distance Education Revue Internationale Du E-Learning Et La Formation à Distance, 40(2). https://doi.org/10.55667/10.55667/ijede.2025.v40.i2.1385
Copyright Notice

Abstract

This paper investigates assessment integrity risks in online distance learning programmes at a private higher education institution in South Africa and the strategies used to manage them. Guided by an interpretivist lens, the research applies the Committee of Sponsoring Organisations of the Treadway Commission (COSO) Enterprise Risk Management (ERM) framework and sociotechnical systems theory to explore how technological, behavioural, and institutional factors intersect to influence academic integrity. Data from staff questionnaires reveal three interrelated dimensions of risk: emergent (artificial intelligence-driven misconduct and integrity threats), behavioural (unethical and dishonest practices), and structural (technological barriers and infrastructural limitations). Synthesised through the Emergent, Behavioural, and Structural risks addressed through Tools, Practices, and Training (EBS-TPT) model, the research highlights the importance of integrating digital tools and ethical training within proactive, design-oriented assessment strategies. Rather than relying solely on detection mechanisms, institutions should foster artificial intelligence (AI) literacy, ethical awareness, and authentic assessment design to sustain credibility in digital learning environments. The paper contributes a conceptual framework and practical insights for higher education institutions seeking to balance technological innovation with academic integrity in the evolving AI era.
Keywords: online distance learning, academic integrity, assessment dishonesty, risk management protocols, private higher education.

French

Cet article examine les risques liés à l’intégrité des évaluations dans les programmes d’apprentissage à distance en ligne d’un établissement privé d’enseignement supérieur en Afrique du Sud, ainsi que les stratégies mises en œuvre pour les gérer. S’appuyant sur une approche interprétativiste, l’étude mobilise le cadre de gestion des risques d’entreprise COSO (ERM) et la théorie des systèmes sociotechniques afin d’explorer comment les facteurs technologiques, comportementaux et institutionnels interagissent et influencent l’intégrité académique. Les données recueillies auprès du personnel révèlent trois dimensions interdépendantes du risque : émergent (manquements à l’intégrité favorisés par l’IA), comportemental (pratiques malhonnêtes et non éthiques) et structurel (barrières technologiques et limitations infrastructurelles). Synthétisées dans le modèle EBS-TPT (Emergent, Behavioural and Structural risks addressed through Tools, Practices and Training) ces dimensions soulignent l’importance d’intégrer les outils numériques et la formation éthique dans des stratégies d’évaluation proactives et centrées sur la conception. Plutôt que de se reposer uniquement sur les mécanismes de détection, les établissements devraient promouvoir la littératie en matière d’IA, la conscience éthique et la conception d’évaluations authentiques afin de préserver la crédibilité des apprentissages numériques. L’article propose ainsi un cadre conceptuel et des recommandations pratiques pour aider les établissements d’enseignement supérieur à concilier innovation technologique et intégrité académique à l’ère de l’IA.

Mots-clés : Enseignement à distance, intégrité académique, fraude à l'évaluation, protocoles de gestion des risques, enseignement supérieur privé.

HTML PDF

References

  1. Abu-Ali, A. (2024) The effect of e-learning in the digital age. Creative Education, 15(12), 2486–2498. https://doi.org/10.4236/ce.2024.1512151
  2. Ali, O., Murray, P. A., Momin, M., Dwivedi, Y. K., & Malik, T. (2024). The effects of artificial intelligence applications in educational settings: Challenges and strategies. Technological Forecasting and Social Change, 199(123076), 1. https://doi.org/10.1016/j.techfore.2023.123076
  3. Alsharefeen, R., & Al Sayari, N. (2025). Examining academic integrity policy and practice in the era of AI: A case study of faculty perspectives. Frontiers in Education, 10, 1621743. https://doi.org/10.3389/feduc.2025.1621743
  4. Amzalag, M., Shapira, N., & Dolev, N. (2021). Two sides of the coin: Lack of academic integrity in exams during the Corona pandemic, students' and lecturers' perceptions. Journal of Academic Ethics, 20(2), 243–263. https://doi.org/10.1007/s10805-021-09413-5
  5. Artyukhov, A., Wołowiec, T., Artyukhova, N., Bogacki, S., & Vasylieva, T. (2024). SDG 4, Academic integrity and artificial intelligence: Clash or win-win cooperation? Sustainability, 16(19), 8483. https://doi.org/10.3390/su16198483
  6. Azionya, C. M., & Nhedzi, A. (2021). The digital divide and higher education challenge with emergency online learning: Analysis of tweets in the wake of the COVID-19 lockdown. Turkish Online Journal of Distance Education, 22(4), 164–182. https://doi.org/10.17718/tojde.1002822
  7. Bali, A. O., & Rached, K. (2023). Online education via media platforms and applications as an innovative teaching method. Education and Information Technologies, 28(1), 507–523. https://doi.org/10.1007/s10639-022-11188-0
  8. Bayram, H., & Tikman, F. (2022). Determining student teachers’ rates of plagiarism during the distance education and investigating possible reasons for plagiarism. Turkish Online Journal of Distance Education, 23(1), 210–236. https://doi.org/10.17718/tojde.1050398
  9. Bin-Nashwan, S. A., Sadallah, M., & Bouteraa, M. (2023). Use of ChatGPT in academia: Academic integrity hangs in the balance. Technology in Society, 75, 102370. https://doi.org/10.1016/j.techsoc.2023.102370
  10. Braun, V., & Clarke, V. (2012). Thematic analysis. In H. Cooper, P. M. Camic, D. L. Long, A. T. Panter, D. Rindskopf, & K. J. Sher (Eds.), APA handbook of research methods in psychology (Vol. 2, pp. 57–71). American Psychological Association. https://psycnet.apa.org/doi/10.1037/13620-004
  11. Chen, Z., Chen, C., Yang, G., He, X., Chi, X., Zeng, Z., & Chen, X. (2024). Research integrity in the era of artificial intelligence: Challenges and responses. Medicine, 103(27), e38811. http://dx.doi.org/10.1097/MD.0000000000038811
  12. Christensen, C. M., Horn, M. B., & Staker, H. (2013). Is K-12 blended learning disruptive? An introduction to the theory of hybrids. Clayton Christensen Institute for Disruptive Innovation. https://files.eric.ed.gov/fulltext/ED566878.pdf
  13. Clarke, O., Chan, W. Y. D., Bukuru, S., Logan, J., & Wong, R. (2022). Assessing knowledge of and attitudes towards plagiarism and ability to recognize plagiaristic writing among university students in Rwanda. Higher Education, 85(2), 247–263. https://doi.org/10.1007/s10734-022-00830-y
  14. Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education (8th ed.). Routledge.
  15. Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148
  16. Creswell, J. W., & Creswell, J. D. (2018). Research designs: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications.
  17. Dolgui, A., & Ivanov, D. (2020). Exploring supply chain structural dynamics: New disruptive technologies and disruption risks. International Journal of Production Economics, 229, 107886. https://doi.org/10.1016/j.ijpe.2020.107886
  18. Du Plooy-Cilliers, F., Davis, C., & Bezuidenhout, R. (2014). Research matters. Juta.
  19. Gamage, K. A., Silva, E. K. D., & Gunawardhana, N. (2020). Online delivery and assessment during COVID-19: Safeguarding academic integrity. Education Sciences, 10(11), 301. https://doi.org/10.3390/educsci10110301
  20. García-Villegas, M., Franco-Pérez, N., & Cortés-Arbeláez, A. (2016). Perspectives on academic integrity in Colombia and Latin America. In T. Bretag (Ed.), Handbook of academic integrity (1st ed.), pp. 161–185. Springer.
  21. Ghias, K., Lakho, G. R., Asim, H., Azam, I. S., & Saeed, S. A. (2014). Self-reported attitudes and behaviours of medical students in Pakistan regarding academic misconduct: A cross-sectional study. BMC Medical Ethics, 15(1), 1–14. https://doi.org/10.1186/1472-6939-15-43
  22. Guerrero-Dib, J. G., Portales, L., & Heredia-Escorza, Y. (2020). Impact of academic integrity on workplace ethical behaviour. International Journal for Educational Integrity, 16(1), 1–18. https://doi.org/10.1007/s40979-020-0051-3
  23. Habib, S., & Hamadneh, N. N. (2021). Impact of perceived risk on consumers technology acceptance in online grocery adoption amid Covid-19 pandemic. Sustainability, 13(18), 10221. https://doi.org/10.3390/su131810221
  24. Holden, O. L., Norris, M. E., & Kuhlmeier, V. A. (2021, July). Academic integrity in online assessment: A research review. Frontiers in Education, 6(639814), 1. https://doi.10.3389/feduc.2021.639814
  25. Hopkin, P., & Thompson, C. (2022). Fundamentals of risk management (6th ed.). Institute of Risk Management. ISBN 978 1 3986 0286 1.
  26. Ivascu, L., & Cioca, L. I. (2014). Opportunity risk: Integrated approach to risk management for creating enterprise opportunities. Advances in Education Research, 49(1), 77–80.
  27. Jones, C. R., & Bergen, B. K. (2024). Lies, damned lies, and distributional language statistics: Persuasion and deception with large language models. arXiv preprint arXiv:2412.17128. https://doi.org/10.48550/arXiv.2412.17128
  28. Khalil, M., & Er, E. (2023). Will ChatGPT get you caught? Rethinking of plagiarism detection. In International Conference on Human-Computer Interaction (pp. 475–487). Springer Nature Switzerland.
  29. Kivunja, C., & Kuyini, A. B. (2017). Understanding and applying research paradigms in educational contexts. International Journal of Higher Education, 6(5), 26–41. https://doi.org/10.5430/ijhe.v6n5p26
  30. Kleinheksel, A. J., Rockich-Winston, N., Tawfik, H., & Wyatt, T. R. (2020). Demystifying content analysis. American Journal of Pharmaceutical Education, 84(1), 7113. https://doi.org/10.5688/ajpe7113
  31. Kudina, O., & van de Poel, I. (2024). A sociotechnical system perspective on AI. Minds and Machines, 34(3), Article 21. https://doi.org/10.1007/s11023-024-09680-2
  32. Lembani, R., Gunter, A., Breines, M., & Dalu, M. T. B. (2020). The same course, different access: The digital divide between urban and rural distance education students in South Africa. Journal of Geography in Higher Education, 44(1), 70–84.
  33. Macfarlane, B., Zhang, J., & Pun, A. (2014). Academic integrity: A review of the literature. Studies in Higher Education, 39(2), 339–358. https://doi.org/10.1080/03075079.2012.709495
  34. Malik, M. A., Mahroof, A., & Ashraf, M. A. (2021). Online university students’ perceptions on the awareness of, reasons for, and solutions to plagiarism in higher education: The development of the AS&P model to combat plagiarism. Applied Sciences, 11(24), 12055. https://doi.org/10.3390/app112412055
  35. Maree, K. (Ed). 2016. First steps in research (2nd ed.). Van Schaik Publishers.
  36. McHaney, R., Cronan, T. P., & Douglas, D. E. (2016). Academic integrity: Information systems education perspective. Journal of Information Systems Education, 27(3):153–158.
  37. Miles, P. J., Campbell, M., & Ruxton, G. D. (2022). Why students cheat and how understanding this can help reduce the frequency of academic misconduct in higher education: A literature review. Journal of Undergraduate Neuroscience Education, 20(2), A150–A160. https://doi.org/10.59390/LXMJ2920
  38. Moya, B., Eaton, S. E., Pethrick, H., Hayden, K. A., Brennan, R., Wiens, J., McDermott, B., & Lesage, J. (2023). Academic integrity and artificial intelligence in higher education contexts: A rapid scoping review protocol. Canadian Perspectives on Academic Integrity, 7(3). http://doi.org/10.55016/ojs/cpai.v7i3/78123
  39. Mthiyane, Z. Z., van der Poll, H. M., & Tshehla, M. F. (2022). A framework for risk management in small medium enterprises in developing countries. Risks, 10(9), 173. https://doi.org/10.3390/risks10090173
  40. Muhammad, A., Shaikh, A., Naveed, Q. N., & Qureshi, M. R. N. (2020). Factors affecting academic integrity in E-learning of Saudi Arabian Universities. An investigation using Delphi and AHP. Ieee Access, 8:16259–16268. https://doi.org/10.1109/ACCESS.2020.2967499
  41. Mutongoza, B. H., & Olawale, B. E. (2022). Safeguarding academic integrity in the face of emergency remote teaching and learning in developing countries. Perspectives in Education, 40(1), 234–249. http://dx.doi.org/10.18820/2519593X/pie.v40.i1.14
  42. Nabaho, L., & Turyasingura, W. (2019). Battling academic corruption in higher education: Does external quality assurance (EQA) offer a ray of hope? Higher Learning Research Communications, 9(1). https://doi.org/10.18870/hlrc.v9i1.449
  43. Najjar, N., Rouphael, M., Bitar, T., & Hleihel, W. (2025). The rise and drop of online learning: Adaptability and future prospects. Frontiers in Education, 10, 1522905. https://doi.org/10.3389/feduc.2025.1522905
  44. Napkin AI. (2025). Napkin AI [Artificial intelligence system]. https://www.napkin.ai
  45. Ngcamu, B. S., & Mantzaris, E. (2023). Policy enforcement, corruption and stakeholder interference in South African universities. Journal of Transport and Supply Chain Management, 17, 814. https://doi.org/10.4102/jtscm.v17i0.814
  46. Nnorom, I. C. (2025). Ethical considerations in artificial intelligence and academic integrity: Balancing technology and human values. In AI and ethics, academic integrity and the future of quality assurance in higher education, pp. 93–100. Sterling Publishers.
  47. OpenAI. (2025). ChatGPT o3 [Large language model]. https://openai.com/index/introducing-o3-and-o4-mini/
  48. Perkins, M. (2023). Academic integrity considerations of AI large language models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching & Learning Practice, 20(2), 7. https://doi.org/10.53761/1.20.02.07
  49. Pike, R. K., Buck, L. A., Tsoukkas, E., & Bell, E. (2025). From collaboration to contract cheating: Exploring staff and student perceptions of the grey areas of academic outsourcing. Assessment & Evaluation in Higher Education, 50(8), 1–21. https://doi.org/10.1080/02602938.2025.2539291
  50. Pratschke, B. M. (2024). Generative AI and education: Digital pedagogies, teaching innovation and learning design. Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-67991
  51. Rane, N., Shirke, S., Choudhary, S. P., & Rane, J. (2024). Education strategies for promoting academic integrity in the era of artificial intelligence and ChatGPT: Ethical considerations, challenges, policies, and future directions. Journal of ELT Studies, 1(1), 36–59. https://doi.org/10.48185/jes.v1i1.1314
  52. Ray, P. P. (2023). ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 3, 121–154. https://doi.org/10.1016/j.iotcps.2023.04.003
  53. Rodrigues, M., Silva, R., Borges, A. P., Franco, M., & Oliveira, C. (2024). Artificial intelligence: Threat or asset to academic integrity? A bibliometric analysis. Kybernetes, 54(5), 2939–2970. https://doi.org/10.1108/K-09-2023-1666
  54. Rumyantseva, N. L. (2005). Taxonomy of corruption in higher education. Pedagogy Journal of Education, 80(1), 81–92. https://doi.org/10.1207/S15327930pje8001_5
  55. Sadeghi, M. (2019). A shift from classroom to distance learning: Advantages and limitations. International Journal of Research in English Education, 4(1), 80–88. https://doi.org/10.29252/ijree.4.1.80
  56. Sevnarayan, K., & Maphoto, K. B. (2024). Exploring the dark side of online distance learning: Cheating behaviours, contributing factors, and strategies to enhance the integrity of online assessment. Journal of Academic Ethics, 22, 51–70. https://doi.org/10.1007/s10805-023-09501-8
  57. Spiers, J., Morse, J. M., Olson, K., Mayan, M., & Barrett, M. (2018). Reflection/commentary on a past article: Verification strategies for establishing reliability and validity in qualitative research. International Journal of Qualitative Methods, 17(1), 1609406918788237. https://doi.org/10.1177/1609406918788237
  58. Stander, E., & Herman, C. (2017). Barriers and challenges private higher education institutions face in the management of quality assurance in South Africa. South African Journal of Higher Education, 31(5), 206–224. https://doi.org/10.20853/31-5-1481
  59. Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning and Teaching, 6(1), 31–40. https://doi.org/10.37074/jalt.2023.6.1.17
  60. Sun, R., Tang, M., Zhou, J., Loan, N. T. T., & Wang, C.-Y. (2025). The dark tetrad as associated factors in generative AI academic misconduct: Insights beyond personal attribute variables. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1551721
  61. Susnjak, T., & McIntosh, T. R. (2024). ChatGPT: The end of online exam integrity?. Education Sciences, 14(6), 656. https://doi.org/10.3390/educsci14060656
  62. Trist, E. L., & Bamforth, K. W. (1951). Some social and psychological consequences of the longwall method of coal-getting. Human Relations, 4(1), 3–38. https://doi.org/10.1177/001872675100400101
  63. Verhoef, A. H., & Coetser, Y. M. (2021). Academic integrity of university students during emergency remote online assessment: An exploration of student voices. Transformation in Higher Education, 6. https://doi.org/10.4102/the.v6i0.132
  64. Wach, E., & Ward, R. (2013). Learning about qualitative document analysis. Institute of Development Studies. https://opendocs.ids.ac.uk/articles/report/Learning_about_Qualitative_Document_Analysis/26442637
  65. Zarzycka, E., Krasodomska, J., Mazurczak-Mąka, A., & Turek-Radwan, M. (2021). Distance learning during the COVID-19 pandemic: Students’ communication and collaboration and the role of social media. Cogent Arts & Humanities, 8(1), 1953228. https://doi.org/10.1080/23311983.2021.1953228
  66. Züll, C. (2016). Open-Ended Questions Version 2.0. GESIS Survey Guidelines. Mannheim: GESIS - Leibniz-Institut für Sozialwissenschaften. https://doi.org/10.15465/gesis-sg_en_002