Ai literacy and academic integrity in a globalised educational system: A systematic review.

Authors

  • Albert Byiringiro Faculty of Education, Bishop Stuart University, Mbarara-Uganda Author

DOI:

https://doi.org/10.51168/x2x7q303

Keywords:

Artificial intelligence, AI literacy, ; Academic integrity, ; Higher education, Globalisation

Abstract

The rapid integration of AI in higher education has revolutionized teaching, evaluation, and learning mechanisms, but at the same time, there are global concerns about the AI-related threats to academic integrity. Background: The increased usage of generative AI in higher education has further fueled the debate on the application of good AI practices and the need for AI literacy to ensure academic integrity in various educational settings. Objective: This systematic review was conducted to identify the definition of AI literacy and its association with AI-related integrity challenges in higher education settings. Methods: The qualitative thematic synthesis was conducted on 29 peer-reviewed articles (2015 to 2025) following the guidelines of the PRISMA 2020 protocol, utilizing literature from prominent scholarly databases. Results: The results of the systematic review indicate that there are various definitions of AI literacy in various settings, and there is no unified definition of AI literacy in learning settings. The results further indicate that there are various AI-related threats to academic integrity in various settings, and the usage of ChatGPT is on the rise, leading to plagiarism and authorship issues in various settings. Regional analysis: The results indicate that there are more studies in the Global North compared to the Global South. Conclusion: The results indicate that there is an association between AI literacy and AI-related integrity challenges in various settings. Implications for future research: The results of the systematic review indicate the need for further research to investigate the effect of AI literacy on good AI practices in various settings.

Author Biography

  • Albert Byiringiro, Faculty of Education, Bishop Stuart University, Mbarara-Uganda

    is a part-time lecturer at Mount Kigali University and the Institut Catholique de Kabgayi (Rwanda) and offers lectures and supervises student teachers for education-related fields of study. He has vast knowledge and experience in education planning and management, and education research, focusing on the innovations that exist and are applicable in the learning processes and methodologies. He is currently pursuing his Ph.D. studies in Education Planning, Management, and Administration at Bishop Stuart University (Uganda). He has also published papers that focus on the involvement of the stakeholders in the education system of Rwanda, aside from others that are relevant to global education. Albert Byiringiro is conversant with English, French, Swahili, and Kinyarwanda. He also undertakes research and consultancy work that focuses on his area of interest, with the objective of enhancing educational success and the digital transformation process that supports social development and the process of globalization.

References

1. Baidoo-Anu, D., & Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits and challenges of ChatGPT in education. Computers and Education: Artificial Intelligence, 4, 100118. https://doi.org/10.2139/ssrn.4337484

2. Bond, M., Zawacki-Richter, O., & Nichols, M. (2024). Meta systematic review of research on artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21(1), 1- 30. https://doi.org/10.1186/s41239-023-00436-z

3. Bretag, T., Harper, R., Burton, M., Ellis, C., Newton, P., van Haeringen, K., Saddiqui, S., & Rozenberg, P. (2019). Contract cheating: A survey of Australian university students. Assessment & Evaluation in Higher Education, 44(5), 1-17. https://doi.org/10.1080/02602938.2018.1527892

4. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264-75278.

https://doi.org/10.1109/ACCESS.2020.2988510

5. Chen, X., Xie, H., & Hwang, G. J. (2022). A multi-perspective study on artificial intelligence in personalized learning. Educational Technology Research and Development, 70, 1-24.

6. Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 60(6), 1-12. https://doi.org/10.35542/osf.io/mrz8h

7. Curtis, G. J., & Clare, J. (2017). How prevalent is contract cheating, and to what extent are students repeat offenders? Higher Education Research & Development, 36(3), 1-15. https://doi.org/10.1007/s10805-017-9278-x

8. Dwivedi, Y. K., et al. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges, and implications of generative conversational AI for research, practice, and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642

9. Eaton, S. E. (2023). Academic integrity in the age of artificial intelligence. Journal of Academic Ethics, 21, 1-17.

https://doi.org/10.1007/978-981-287-079-7_188-1

10. Floridi, L., Cowls, J., Beltrametti, M., et al. (2018). AI4People: An ethical framework for a good AI society. Minds and Machines, 28(4), 689-707. https://doi.org/10.1007/s11023-018-9482-5

11. Heffernan, N. T., & Heffernan, C. L. (2014). The ASSISTments ecosystem: Building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. International Journal of Artificial Intelligence in Education, 24(4), 470-497. https://doi.org/10.1007/s40593-014-0024-x

12. Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4), 542-570. https://doi.org/10.1111/ejed.12533

13. Holmes, W., Porayska-Pomsta, K., Holstein, K., et al. (2021). Ethics of AI in education: Towards a community-wide framework. Computers and Education: Artificial Intelligence, 2, 100019.

14. Kasneci, E., et al. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Computers and Education: Artificial Intelligence, 4, 100114.

15. Kuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O. M. D., Păun, D., & Mihoreanu, L. (2021). Exploring opportunities and challenges of artificial intelligence in higher education. Sustainability, 13(18), 10424.

https://doi.org/10.3390/su131810424

16. Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping review. Computers and Education: Artificial Intelligence, 3, 100055.

https://doi.org/10.1016/j.caeai.2022.100101

17. Lim, W. M., et al. (2023). Generative AI and the future of education: Rethinking pedagogy, assessment, and policy. Computers in Human Behavior, 145, 107772.

18. Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1-28. https://doi.org/10.1145/3313831.3376727

19. Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). AI literacy: Definition, teaching, evaluation, and ethical issues. Computers and Education: Artificial Intelligence, 2, 100015.

https://doi.org/10.1016/j.caeai.2021.100041

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

https://doi.org/10.1186/s41039-017-0062-8

21. Rahman, M. M., Watanobe, Y., & Nakamura, K. (2023). ChatGPT for education: Opportunities, challenges, and policy implications. Education and Information Technologies, 28, 1-25.

22. Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning & Teaching, 6(1), 1-22. https://doi.org/10.37074/jalt.2023.6.1.9

23. Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. British Journal of Educational Technology, 50(3), 1-14.

24. Spector, J. M. (2022). Conceptualizing artificial intelligence in education. Journal of Educational Computing Research, 60(5), 1107-1130.

25. Su, J., Ng, D. T. K., & Zhang, H. (2022). Artificial intelligence literacy in education: A systematic review. Education and Information Technologies, 27, 12725-12746.

26. Tlili, A., et al. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 1-23. https://doi.org/10.1186/s40561-023-00237-x

27. Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223-235. https://doi.org/10.1080/17439884.2020.1798995

28. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 1-27. https://doi.org/10.1186/s41239-019-0171-0

29. Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, J. M., Liu, J.-B., Yuan, J., & Li, Y. (2021). A review of artificial intelligence. https://doi.org/10.1155/2021/8812542

30. (AI) In education from 2010 to 2020. Complexity, 2021, 1-18.

Downloads

Published

2026-04-01

Issue

Vol. 3 No. 4 (2026): APRIL 2026 ISSUE

Section

Original Peer-Reviewed Articles

License

Copyright (c) 2026 Albert Byiringiro (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

SJ-Education publishes under the Attribution-Noncommercial-NoDerivatives 4.0 international (CCBY-NC-ND 4.0) license which allows you to Share, Copy, and redistribute the materials in any medium or format. The licensor cannot revoke these freedoms as long as you follow the license terms; 1. Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. 2. Non-commercial: You may not use the material for commercial purposes. Commercial use is one primarily intended for commercial advantage or monetary compensation. 3. No Derivatives: if you remix, transform, or build upon the material, you may not distribute the modified material. 4. No additional restrictions: You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

How to Cite

Byiringiro, A. (2026). Ai literacy and academic integrity in a globalised educational system: A systematic review. SJ Education Research Africa, 3(4), 13. https://doi.org/10.51168/x2x7q303

Most read articles by the same author(s)

Similar Articles

81-90 of 90

You may also start an advanced similarity search for this article.