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The Digital Divide in Wellness: Unpacking the Effects of Artificial Intelligence on University Student Mental Health

Authors

Cephas Mandirahwe

Faculty of Development Studies, Midlands State University, Zimbabwe (ZW)

Rosemary Guvhu

epartment of Educational Policy Studies and Leadership, Midlands State University, Zimbabwe (ZW)

Effort Musvutisa

Department of Science, Technology and Design Education, Midlands State University, Zimbabwe (ZW)

Article Information

DOI: 10.51583/IJLTEMAS.2026.150400106

Subject Category: Management

Volume/Issue: 15/4 | Page No: 1219-1236

Publication Timeline

Submitted: 2026-05-20

Published: 2026-05-19

Abstract

The accelerated spread of Artificial Intelligence (AI) within higher education institutions such as universities signifies a profound technological advancement with dual implications for sustainable development. While AI promises unique opportunities for youth empowerment, its application demands a critical examination of its effect on student wellbeing.  This study investigates the influence of AI-mediated educational processes on university students’ mental health through the lens of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Using a convergent parallel mixed-methods design at a selected public university in Zimbabwe, the study combined quantitative data (n=303 students) with qualitative insights from focus group discussions with students and lecturers. Findings showed a inflexible "Hardware Hierarchy," where 85.5% of students recognise the laptop as an vital "academic station" for critical AI confirmation, while mobile-only users experience a "technologically hollowed-out" state. Even though AI is highly cherished for its usefulness among students using tools like ChatGPT AND Google Gemini as "always-on" tutors, it is somewhat linked with adverse mental health outcomes. These manifest as "Turnitin Anxiety," "Temporal Anxieties" connected to computer laboratory access, and ethical panic emanating from a "legal vacuum" in institutional AI policy. Furthermore, qualitative narratives demonstrate "Techno-Exhaustion" among faculty, particularly female lecturers  endeavouring to balance domestic work with the rigours of AI-output verification. Overall, the study concludes that the digital divide has evolved from a matter of connectivity to a "Divide in Wellness." It recommends institutional innovation beyond simple technological application and proposes application of robust ethical AI policies and subsidised hardware and WIFI data support to ease psychological risks while promoting resilient human capital development within the Education 5.0 framework.

Keywords

Artificial Intelligence, Higher Education, Mental Health, UTAUT2, Wellness Equity, Zimbabwe.

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References

1. Government of Zimbabwe. (2026). Digital learning leadership and the fourth industrial revolution: A strategic framework for Zimbabwean higher education. Ministry of Higher and Tertiary Education, Innovation, Science and Technology Development. [Google Scholar] [Crossref]

2. Crawford, J. (2025). Epistemic parochialism: Single institution studies in the age of Artificial Intelligence large language models. Journal of University Teaching and Learning Practice, 22(7), 1–17. [Google Scholar] [Crossref]

3. Lindebaum, D., & Fleming, P. (2024). ChatGPT undermines human reflexivity, scientific responsibility and responsible management research. British Journal of Management, 35(2), 566–575. https://doi.org/10.1111/1467-8551.12704 [Google Scholar] [Crossref]

4. Khalid, S. (2025). Ethical concerns of generative AI in assignments. Journal of Academic Integrity, 12(4), 201–218. [Google Scholar] [Crossref]

5. Government of Zimbabwe. (2024). National Artificial Intelligence policy: Driving technological utility and academic output. Ministry of Information Communication Technology, Postal and Courier Services. [Google Scholar] [Crossref]

6. Ministry of Higher and Tertiary Education, Innovation, Science and Technology Development. (2020). Education 5.0: The philosophy for Zimbabwe's higher education transformation. Government Printers. [Google Scholar] [Crossref]

7. Wang, L., & Wang, L. (2025). Intelligent evaluation method of students' mental health based on Internet of Things. IoT and Big Data Technologies for Health Care, 521, 213–228. [Google Scholar] [Crossref]

8. Larson, R. W., Moneta, G., Richards, M. H., & Wilson, S. (2002). Continuity, stability, and change in daily emotional experience across adolescence. Child Development, 73(4), 1151–1165. [Google Scholar] [Crossref]

9. Khan, G. M., Ali, Z., & Khalid, A. (2025). The impact of artificial intelligence based personalized learning on students' motivation and self-regulated learning. Review of Applied Management and Social Sciences, 8(4), 1633–1644. [Google Scholar] [Crossref]

10. Huma, T., et al. (2025). Investigating the impact of AI-driven feedback systems on student autonomy and self-directed learning. Social Science Review Archives, 3(4), 2169–2179. [Google Scholar] [Crossref]

11. Achuthan, K. (2025). Artificial intelligence and learner autonomy: A meta-analysis of self-regulated and self-directed learning. Frontiers in Education, 10, Article 1738751. https://doi.org/10.3389/feduc.2025.1738751 [Google Scholar] [Crossref]

12. Crawford, J., Cowling, M., & Allen, K. A. (2025). Conceptualising artificial intelligence as an apprentice. In Handbook of Artificial Intelligence in Higher Education (pp. 67–75). Edward Elgar Publishing. [Google Scholar] [Crossref]

13. Izak, M., et al. (2025). Generative artificial intelligence and learning: At the dawn of idiocracy? Management Learning, 56(3), 407–415. [Google Scholar] [Crossref]

14. Barros, A. (2025). The atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Academy of Management Learning & Education, 24(1). https://doi.org/10.5465/amle.2024.0122 [Google Scholar] [Crossref]

15. Alghamdi, L. H., & Alghizzi, T. M. (2025). Educators’ reflections on AI-automated feedback in higher education: A structured integrative review of potentials, pitfalls, and ethical dimensions. Frontiers in Education, 10, Article 1704820. [Google Scholar] [Crossref]

16. Christodoulou, E., & Zembylas, M. (2026). Artificial intelligence as a site of global educational governance: The case of UNESCO. Journal of Education Policy, 1–24. https://doi.org/10.1080/02680939.2026.2314567 [Google Scholar] [Crossref]

17. Salimi, F. (2025). Aligning policy and practice: The World Bank’s approach to EdTech in Sub-Saharan Africa. Policy Futures in Education, 23(6), 1134–1156. [Google Scholar] [Crossref]

18. Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1–38. [Google Scholar] [Crossref]

19. McDonald, N., et al. (2025). Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines. Computers in Human Behavior: Artificial Humans, 3, Article 100121. [Google Scholar] [Crossref]

20. Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 22. [Google Scholar] [Crossref]

21. Crawford, J., et al. (2023). Artificial intelligence and authorship editor policy: ChatGPT, Bard, Bing AI, and beyond. Journal of University Teaching and Learning Practice, 20(5), 1–11. [Google Scholar] [Crossref]

22. Saleh, E. H. (2025). Empowering learners: Exploring teaching strategies, AI integration, and motivation tools for fostering autonomous learning in higher education. East Journal of Human Science, 1(6), 14–34. [Google Scholar] [Crossref]

23. Amiri, S. M. H. (2025). The digital divide revisited: Connectivity, devices, and the hidden barriers to global EdTech equity. Indonesian Journal of Innovation and Applied Sciences (IJIAS), 5(3), 254–267. [Google Scholar] [Crossref]

24. 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(1), 39. [Google Scholar] [Crossref]

25. McDonald, N., et al. (2025). AI in educational equity and student mental resilience. Computers in Human Behavior: Artificial Humans, 4, Article 100125. [Google Scholar] [Crossref]

26. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. [Google Scholar] [Crossref]

27. Creswell, J. W., & Inoue, M. (2025). A process for conducting mixed methods data analysis. Journal of General and Family Medicine, 26(1), 4–11. [Google Scholar] [Crossref]

28. Younas, A., Fàbregues, S., & Creswell, J. W. (2023). Generating metainferences in mixed methods research: A worked example in convergent mixed methods designs. Methodological Innovations, 16(3), 276–291. [Google Scholar] [Crossref]

29. Baker, R. S., Martin, T., & Rossi, L. M. (2016). Educational data mining and learning analytics. In The Wiley Handbook of Cognition and Assessment (pp. 379–396). John Wiley & Sons. [Google Scholar] [Crossref]

30. Cheng, Y. P., et al. (Eds.). (2024). Innovative Technologies and Learning. Springer Nature. [Google Scholar] [Crossref]

31. Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1–38. [Google Scholar] [Crossref]

32. Oben, A. I., et al. (2025). Teaching with Generative Artificial Intelligence (GenAI): Should educators trust AI to guide student learning? Innovations in Education and Teaching International, 1–14. [Google Scholar] [Crossref]

33. Crawford, J., et al. (2023). Artificial intelligence and authorship editor policy: ChatGPT, Bard, Bing AI, and beyond. Journal of University Teaching and Learning Practice, 20(5), 1–11. [Google Scholar] [Crossref]

34. Government of Zimbabwe. (2024). National Artificial Intelligence policy: Driving technological utility and academic output. Ministry of Information Communication Technology, Postal and Courier Services. [Google Scholar] [Crossref]

35. Republic of Zimbabwe. (2026). National Artificial Intelligence Strategy (2026-2030): Charting Zimbabwe’s Future. Government Printers. [Google Scholar] [Crossref]

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