Can They Spot the Fake? AI Literacy and Synthetic Media Detection among Zimbabwean University Students

This study examined the extent to which AI literacy predicts Zimbabwean university students’ ability to detect synthetic media. A cross-sectional survey of 518 students at the National University of Science and Technology combined a self-report AI literacy scale adapted from Ng, Leung, Chu and Qiao (2021) with an objective six-item detection task. The central finding is a structural mismatch between confidence and competence. Students rated their literacy at moderate to high levels (overall M = 3.58 out of 5.00), yet correctly classified only 60.3% of stimuli, with accuracy on AI-generated items barely above chance. Multiple regression showed that critical literacy was the strongest predictor of detection accuracy (beta = 0.33, p < 0.001), while the frequency of AI tool use predicted nothing once evaluative skill was controlled for. The most consequential structural finding concerns learning channels. Social media taught detection skills to 62.0% of students, whereas formal education reached only 15.1%, meaning an unregulated informal ecology is doing the work universities have not taken up. The findings support critiques that self-report AI literacy frameworks overstate practical preparedness and indicate that detection is a trainable evaluative competency that should be embedded in curricula across disciplines rather than treated as a by-product of digital exposure.

AI literacy, synthetic media, deepfakes, detection accuracy, Zimbabwe

1. Biagini, G. (2024). Assessing the assessments: Toward a multidimensional approach to AI literacy. Media Education, 15(1), 91-101. https://doi.org/10.36253/me-15831 [Google Scholar] [Crossref]

2. Braun, V. and Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa [Google Scholar] [Crossref]

3. Chesney, B. and Citron, D. (2019). Deep fakes: A looming challenge for privacy, democracy, and national security. California Law Review, 107(6), 1753-1820. https://doi.org/10.15779/Z38RV0D15J [Google Scholar] [Crossref]

4. DataReportal. (2024). Digital 2024: Zimbabwe. https://datareportal.com/reports/digital-2024-zimbabwe [Google Scholar] [Crossref]

5. Groh, M., Epstein, Z., Firestone, C. and Pierson, E. (2022). Deepfake detection by human crowds, machines, and machine-informed crowds. Proceedings of the National Academy of Sciences, 119(1), e2110013119. https://doi.org/10.1073/pnas.2110013119 [Google Scholar] [Crossref]

6. Hancock, J. T., Naaman, M. and Levy, K. (2020). AI-mediated communication: Definition, research agenda, and ethical considerations. Journal of Computer-Mediated Communication, 25(1), 89-100. https://doi.org/10.1093/jcmc/jzz022 [Google Scholar] [Crossref]

7. Köbis, N. and Mossink, L. D. (2021). Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry. Computers in Human Behavior, 114, 106553. https://doi.org/10.1016/j.chb.2020.106553 [Google Scholar] [Crossref]

8. Kruger, J. and Dunning, D. (1999). Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments. Journal of Personality and Social Psychology, 77(6), 1121-1134. https://doi.org/10.1037/0022-3514.77.6.1121 [Google Scholar] [Crossref]

9. Lazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., Metzger, M. J., Nyhan, B., Pennycook, G., Rothschild, D., Schudson, M., Sloman, S. A., Sunstein, C. R., Thorson, E. A., Watts, D. J. and Zittrain, J. L. (2018). The science of fake news. Science, 359(6380), 1094-1096. https://doi.org/10.1126/science.aao2998 [Google Scholar] [Crossref]

10. Long, D. and Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1-16. https://doi.org/10.1145/3313831.3376727 [Google Scholar] [Crossref]

11. Mare, A. (2020). State-ordered Internet shutdowns and digital authoritarianism in Zimbabwe. International Journal of Communication, 14, 4244-4263. [Google Scholar] [Crossref]

12. Mare, A., Mabweazara, H. M. and Moyo, D. (2019). “Fake news” and cyber-propaganda in Sub-Saharan Africa: Recentering the research agenda. African Journalism Studies, 40(4), 1-12. https://doi.org/10.1080/23743670.2020.1788295 [Google Scholar] [Crossref]

13. Masunda, O. (2024). Peace Education 4.0: A Curriculum Framework for Africa. Academia Lasalliana Journal of Education and Humanities, 6(1). https://doi.org/10.55902/SJCE7127 [Google Scholar] [Crossref]

14. Metzger, M. J. and Flanagin, A. J. (2013). Credibility and trust of information in online environments: The use of cognitive heuristics. Journal of Pragmatics, 59, 210-220. https://doi.org/10.1016/j.pragma.2013.07.012 [Google Scholar] [Crossref]

15. Moyo, D. (2009). Citizen journalism and the parallel market of information in Zimbabwe’s 2008 election. Journalism Studies, 10(4), 551-567. https://doi.org/10.1080/14616700902797291 [Google Scholar] [Crossref]

16. Ng, D. T. K., Leung, J. K. L., Chu, S. K. W. and Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041 [Google Scholar] [Crossref]

17. Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill. [Google Scholar] [Crossref]

18. Pennycook, G. and Rand, D. G. (2019). Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition, 188, 39-50. https://doi.org/10.1016/j.cognition.2018.06.011 [Google Scholar] [Crossref]

19. Vaccari, C. and Chadwick, A. (2020). Deepfakes and disinformation: Exploring the impact of synthetic political video on deception, uncertainty, and trust in news. Social Media and Society, 6(1). https://doi.org/10.1177/2056305120903408 [Google Scholar] [Crossref]

20. Wardle, C. and Derakhshan, H. (2017). Information disorder: Toward an interdisciplinary framework for research and policy making. Council of Europe. [Google Scholar] [Crossref]

21. Wasserman, H. and Madrid-Morales, D. (2019). An exploratory study of “fake news” and media trust in Kenya, Nigeria and South Africa. African Journalism Studies, 40(1), 107-123. https://doi.org/10.1080/23743670.2019.1627230 [Google Scholar] [Crossref]

22. Westerlund, M. (2019). The emergence of deepfake technology: A review. Technology Innovation Management Review, 9(11), 39-52. https://doi.org/10.22215/timreview/1282 [Google Scholar] [Crossref]

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