AI in Dissertation Examination: Opportunities for Undergraduates and Postgraduates in Zambia, Rwanda, and Kenya

Authors

  • Linety Juma Pwani University, Department of Curriculum, Instruction and Technology, 195-80108, Kilifi Kenya Author https://orcid.org/0009-0001-9037-0747
  • Petros Chavula World Agroforestry Centre, St. Eugene Office Park 39P Lake Road, P.O. Box 50977, Kabulonga, Lusaka, Zambia & African Centre of Excellence for Climate-Smart Agriculture and Biodiversity Conservation, Haramaya University, Dire-Dawa, Ethiopia Author
  • Fredrick Kayusi Department of Environmental Studies, Geography, and Planning, Maasai Mara University, 861-20500, Narok-Kenya Author https://orcid.org/0000-0003-1481-4016
  • Michael Keari Omwenga Department of Education Psychology, School of Education, Pwani University, Kilifi, Kenya Author https://orcid.org/0009-0008-7982-1114
  • Rashmi Mishra College of Economics and Business Administration, University of Technology and Applied Sciences- Al Musanna Author https://orcid.org/0009-0005-1994-0793
  • Timothy Mwewa Mukuba University, Itimpi, Kitwe, Copperbelt Province, P.O. Box 20382, Zambia Author https://orcid.org/0009-0003-2194-7332

DOI:

https://doi.org/10.62486/latia2025329

Keywords:

Artificial Intelligence, Dissertation Examination, Higher Education, Plagiarism Detection, Academic Integrity, AI Ethics, Zambia, Rwanda, Kenya

Abstract

The integration of Artificial Intelligence (AI) in dissertation examination presents a transformative opportunity for higher education institutions in Zambia, Rwanda, and Kenya. As student enrollments continue to rise, universities face challenges in efficiently evaluating dissertations while maintaining academic integrity. AI-driven tools offer innovative solutions by automating tasks such as plagiarism detection, language quality assessment, and contract cheating identification. This study aims to explore the opportunities, challenges, and impact of AI adoption in dissertation assessment across selected universities. A mixed-methods research design was employed, incorporating surveys, semi-structured interviews, and data analysis from AI-assisted dissertation evaluations at Copperbelt University (Zambia), the University of Rwanda, and Jomo Kenyatta University of Agriculture and Technology (Kenya). Findings indicate that AI enhances efficiency by reducing faculty workload and improving feedback quality for students. However, challenges such as digital literacy gaps, infrastructure limitations, and concerns over AI’s fairness and ethical implications hinder full adoption. Despite these obstacles, there is strong support among students and faculty for AI integration, provided it is complemented by human oversight. The study concludes that AI has significant potential to revolutionize dissertation evaluation but requires investment in infrastructure, faculty training, and policy frameworks to ensure responsible implementation. Collaboration among universities, policymakers, and technology providers is essential to optimizing AI-driven dissertation assessment while upholding academic rigour.

References

Almaraz-López, C., Almaraz-Menéndez, F., and López-Esteban, C. (2023). Comparative study of the attitudes and perceptions of university students in business administration and management and in education toward artificial intelligence. Educ. Sci. 13:609. https://doi.org/10.3390/educsci13060609 DOI: https://doi.org/10.3390/educsci13060609

Almossa, S. Y. & Alzahrani, S. M. (2022). Lessons on maintaining assessment integrity during COVID-19. International Journal for Educational Integrity. springer.com. https://doi.org/10.1007/s40979-022-00112-1 DOI: https://doi.org/10.1007/s40979-022-00112-1

Amani, J., Myeya, H., & Mhewa, M. (2022). Understanding the motives for pursuing postgraduate studies and causes of late completion: supervisors and supervisees' experiences. Sage Open. sagepub.com. https://doi.org/10.1177/21582440221109586 DOI: https://doi.org/10.1177/21582440221109586

Azmat, M., & Ahmad, A. (2022). Students experience in completing thesis at undergraduate level. Journal of Materials and Environmental Science, 13(3), 291-300. jmaterenvironsci.com. https://doi.org/10.56495/ejr.v2i1.285 DOI: https://doi.org/10.56495/ejr.v2i1.285

Bailey, L. & Gibson, M. T. (2023). School leaders' experiences of high-stakes assessments during the Covid-19 pandemic in England. School Leadership & Management. tandfonline.com. https://doi.org/10.1080/13632434.2023.2176482 DOI: https://doi.org/10.1080/13632434.2023.2176482

Burt, B. A., Stone, B. D., & Motshubi…, R. (2023). STEM validation among underrepresented students: Leveraging insights from a STEM diversity program to broaden participation.. Journal of Diversity in …. apa.org. https://doi.org/10.1037/dhe0000300 DOI: https://doi.org/10.1037/dhe0000300

Chang, M., Cuyegkeng, A., Breuer, J. A., Alexeeva, A., Archibald, A. R., Lepe, J. J., & Greenberg, M. L. (2022). Medical student exam performance and perceptions of a COVID-19 pandemic-appropriate pre-clerkship medical physiology and pathophysiology curriculum. BMC Medical Education, 22(1), 833. springer.com. https://doi.org/10.1186/s12909-022-03907-5 DOI: https://doi.org/10.1186/s12909-022-03907-5

Cooper, A., DeLuca, C., Holden, M., & MacGregor, S. (2022). Emergency assessment: Rethinking classroom practices and priorities amid remote teaching. Assessment in Education: Principles, Policy & Practice, 29(5), 534-554. https://doi.org/10.1080/0969594x.2022.2069084 DOI: https://doi.org/10.1080/0969594X.2022.2069084

Dou, R. & Cian, H. (2022). Constructing STEM identity: An expanded structural model for STEM identity research. Journal of Research in Science Teaching. wiley.com. https://doi.org/10.1002/tea.21734 DOI: https://doi.org/10.1002/tea.21734

Falter, M., Arenas, A. A., Maples, G. W., Smith, C. T., Lamb, L. J., Anderson, M. G., ... & Wafa, N. Z. (2022, January). Making room for Zoom in focus group methods: opportunities and challenges for novice researchers (during and beyond COVID-19). In Forum Qualitative Sozialforschung/Forum: Qualitative Social Research (Vol. 23, No. 1). https://www.qualitative-research.net/index.php/fqs/article/download/3768/4837

Gao, R., E. Merzdorf, H., Anwar, S., Cynthia Hipwell, M., & Srinivasa, A. (2023). Automatic assessment of text-based responses in post-secondary education: A systematic review. https://www.sciencedirect.com/science/article/pii/S2666920X24000079 DOI: https://doi.org/10.1016/j.caeai.2024.100206

Gardner, J., O'Leary, M., & Yuan, L. (2021). Artificial intelligence in educational assessment:‘Breakthrough? Or buncombe and ballyhoo?’. Journal of Computer Assisted Learning, 37(5), 1207-1216. wiley.com. https://doi.org/10.1111/jcal.12577 DOI: https://doi.org/10.1111/jcal.12577

Guest, G., Namey, E., O'Regan, A., Godwin, C., & Taylor, J. (2023). Comparing interview and focus group data collected in person and online. https://doi.org/10.25302/05.2020.me.1403117064 DOI: https://doi.org/10.25302/05.2020.ME.1403117064

Gwagwa, A., Kazim, E., Kachidza, P., Hilliard, A., Siminyu, K., Smith, M., & Shawe-Taylor, J. (2021). Road map for research on responsible artificial intelligence for development (AI4D) in African countries: The case study of agriculture. https://doi.org/10.1016/j.patter.2021.100381 DOI: https://doi.org/10.1016/j.patter.2021.100381

Horowitz, M. C., Kahn, L., Macdonald, J., and Schneider, J. (2024). Adopting AI: how familiarity breeds both trust and contempt. AI Soc. 39, 1721–1735. https://doi.org/10.1007/s00146-023-01666-5 DOI: https://doi.org/10.1007/s00146-023-01666-5

Itani, M., Itani, M., Kaddoura, S., & Al Husseiny, F. (2022). The impact of the Covid-19 pandemic on on-line examination: challenges and opportunities. Global Journal of Engineering Education, 24(2), 105-120. http://www.wiete.com.au/journals/GJEE/Publish/vol24no2/04-Kaddoura-S.pdf

Jones, J. E., Jones, L. L., Calvert, M. J., Damery, S. L., & Mathers, J. M. (2022). A literature review of studies that have compared the use of face-to-face and online focus groups. International Journal of Qualitative Methods, 21, 16094069221142406. sagepub.com. https://doi.org/10.1177/16094069221142406 DOI: https://doi.org/10.1177/16094069221142406

Kaswan, K. S., Dhatterwal, J. S., and Ojha, R. P. (2024). “AI in personalized learning” in Advances in technological innovations in higher education. eds. I. A. Garg, B. V. Babu and V. E. Balas. 1st ed (Boca Raton: CRC Press), 103–117. https://doi.org/10.1201/9781003376699-9 DOI: https://doi.org/10.1201/9781003376699-9

Kayan-Fadlelmula, F., Sellami, A., Abdelkader, N., & Umer, S. (2022). A systematic review of STEM education research in the GCC countries: Trends, gaps and barriers. International Journal of STEM Education, 9, 1-24. springer.com. https://doi.org/10.1186/s40594-021-00319-7 DOI: https://doi.org/10.1186/s40594-021-00319-7

Kelly, S., Kaye, S.-A., and Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telemat. Inform. https://doi.org/10.1016/j.tele.2022.101925 DOI: https://doi.org/10.1016/j.tele.2022.101925

Komendantskaya, E., Stewart, R., Duncan, K., Kienitz, D., Le Hen, P., & Bacchus, P. (2019). Neural Network Verification for the Masses (of AI graduates). https://doi.org/10.3390/electronics10040396 DOI: https://doi.org/10.3390/electronics10040396

Kuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O. M., Păun, D., & Mihoreanu, L. (2021). Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability, 13(18), 10424. mdpi.com. https://doi.org/10.3390/su131810424 DOI: https://doi.org/10.3390/su131810424

Kurysheva, A., Koning, N., Fox, C. M., van Rijen, H. V., & Dilaver, G. (2022). Once the best student always the best student? Predicting graduate study success using undergraduate academic indicators: Evidence from research masters’ programs in the Netherlands. International Journal of Selection and Assessment, 30(4), 579-595. wiley.com. https://doi.org/10.1111/ijsa.12397 DOI: https://doi.org/10.1111/ijsa.12397

Latham, A. & Goltz, S. (2019). A Survey of the General Public’s Views on the Ethics of using AI in Education. https://doi.org/10.1007/978-3-030-23204-7_17 DOI: https://doi.org/10.1007/978-3-030-23204-7_17

Medaille, A., Beisler, M., Tokarz, R. E., & Bucy, R. (2021). Honors students and thesis research: A study of information literacy practices and self-efficacy at the end of students' undergraduate careers. https://doi.org/10.5860/crl.82.1.92 DOI: https://doi.org/10.5860/crl.82.1.92

Nikoulina, A., and Caroni, A. (2024). Familiarity, use, and perception of AI- PoweredTools in higher education. Int. J. Manag. Knowl. Learn. 13, 169–181. https://doi.org/10.53615/2232-5697.13.169-181 DOI: https://doi.org/10.53615/2232-5697.13.169-181

Ochieng Alaka, B. (2017). A Dimensional student enrollment prediction model: case of Strathmore University. https://doi.org/10.1002/emt.30366 DOI: https://doi.org/10.1002/emt.30366

Owan, V. J., Abang, K. B., Idika, D. O., Etta, E. O., & Bassey, B. A. (2023). Exploring the potential of artificial intelligence tools in educational measurement and assessment. Eurasia journal of mathematics, science and technology education, 19(8), em2307. https://doi.org/10.29333/ejmste/13428 DOI: https://doi.org/10.29333/ejmste/13428

Pan, M., Wang, J., and Wang, J. (2023). Application of artificial intelligence in education: opportunities, challenges, and suggestions. In 2023 13th international conference on information Technology in Medicine and Education (ITME), 623–627. https://doi.org/10.1109/itme60234.2023.00130 DOI: https://doi.org/10.1109/ITME60234.2023.00130

Pande, K., Jadhav, V., and Mali, M. (2023). Artificial intelligence: exploring the attitude of secondary students. J. E-Learn. Knowledge Soc. 19, 43–48. https://doi.org/10.1007/978-3-031-68617-7_3 DOI: https://doi.org/10.1007/978-3-031-68617-7_3

Petricini, T., Wu, C., and Zipf, S. T. (2023). Perceptions about generative AI and ChatGPT use by faculty and college students. Pennsylvania, USA: The Pennsylvania State University. https://doi.org/10.35542/osf.io/jyma4 DOI: https://doi.org/10.35542/osf.io/jyma4

Pursnani, V., Sermet, Y., & Demir, I. (2023). Performance of ChatGPT on the US Fundamentals of Engineering Exam: Comprehensive Assessment of Proficiency and Potential Implications for Professional Environmental Engineering Practice. https://doi.org/10.1016/j.caeai.2023.100183 DOI: https://doi.org/10.1016/j.caeai.2023.100183

Race, A. I., De Jesus, M., Beltran, R. S., & Zavaleta, E. S. (2021). A comparative study between outcomes of an in‐person versus online introductory field course. Ecology and Evolution, 11(8), 3625-3635. wiley.com. https://doi.org/10.1002/ece3.7209 DOI: https://doi.org/10.1002/ece3.7209

Single, P. B. & Reis, R. M. (2023). Demystifying dissertation writing: A streamlined process from choice of topic to final text. https://doi.org/10.4324/9781003444053 DOI: https://doi.org/10.4324/9781003444053

Slade, C., Lawrie, G., Taptamat, N., Browne, E., Sheppard, K., & Matthews, K. E. (2022). Insights into how academics reframed their assessment during a pandemic: disciplinary variation and assessment as afterthought. Assessment & Evaluation in Higher Education, 47(4), 588-605. https://doi.org/10.1080/02602938.2021.1933379 DOI: https://doi.org/10.1080/02602938.2021.1933379

Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., et al. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learn. Environ. https://doi.org/10.1186/s40561-023-00237-x DOI: https://doi.org/10.1186/s40561-023-00237-x

UNESCO. (2021b). UNESCO Strategy on technological innovation in education (2021–2025). https://unesdoc.unesco.org/ark:/48223/pf0000375776

Vandenberg, S. & Magnuson, M. (2021). A comparison of student and faculty attitudes on the use of Zoom, a video conferencing platform: A mixed-methods study. Nurse Education in Practice. https://doi.org/10.1016/j.nepr.2021.103138 DOI: https://doi.org/10.1016/j.nepr.2021.103138

Vooren, M., Haelermans, C., Groot, W., & van den Brink, H. M. (2022). Comparing success of female students to their male counterparts in the STEM fields: an empirical analysis from enrollment until graduation using longitudinal register data. International Journal of STEM Education, 9, 1-17. https://doi.org/10.1186/s40594-021-00318-8 DOI: https://doi.org/10.1186/s40594-021-00318-8

Watson, C., Templet, T., Leigh, G., Broussard, L., & Gillis, L. (2023). Student and faculty perceptions of effectiveness of online teaching modalities. Nurse Education Today, 120, 105651. https://doi.org/10.1016/j.nedt.2022.105651 DOI: https://doi.org/10.1016/j.nedt.2022.105651

Whitcomb, K. M. & Singh, C. (2021). Underrepresented minority students receive lower grades and have higher rates of attrition across STEM disciplines: A sign of inequity?. International Journal of Science Education. https://doi.org/10.1080/09500693.2021.1900623 DOI: https://doi.org/10.1080/09500693.2021.1900623

World English Journal, A. & Aljuaid, H. (2024). The Impact of Artificial Intelligence Tools on Academic Writing Instruction in Higher Education: A Systematic Review. osf.io. https://doi.org/10.24093/awej/chatgpt.2 DOI: https://doi.org/10.24093/awej/ChatGPT.2

World English Journal, A. & Mohammed Ahmed Mudawy, A. (2024). Investigating EFL Faculty Members’ Perceptions of Integrating Artificial Intelligence Applications to Improve the Research Writing Process: A Case Study at Majmaah University. https://doi.org/10.24093/awej/chatgpt.11 DOI: https://doi.org/10.24093/awej/ChatGPT.11

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Published

2025-03-29

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How to Cite

1.
Juma L, Chavula P, Kayusi F, Keari Omwenga M, Mishra R, Mwewa T. AI in Dissertation Examination: Opportunities for Undergraduates and Postgraduates in Zambia, Rwanda, and Kenya. LatIA [Internet]. 2025 Mar. 29 [cited 2025 Aug. 25];3:329. Available from: https://latia.ageditor.uy/index.php/latia/article/view/329