Article Sidebar

Submitted
October 17, 2024
Accepted
April 3, 2025
Published
May 12, 2025
Download
Download PDF
Statistic
Read Counter : 30

Main Article Content

Abstract

Recent advancements in Artificial Intelligence (AI), particularly in Generative AI, have significantly impacted various domains. Innovation of models such as ChatGPT, Bard, DALL-E, Midjourney, and DeepMind has transformed academia. The widespread use of AI systems has sparked debates on how to effectively train future-relevant competencies while maintaining learning integrity. The ability of GenAI algorithms to generate specific feedback on tests, quizzes, and assessments as an instructional instrument to support student success has been a significant motivator. With current challenges of insufficient academic resources, a freeze on teacher hiring, and a shortage of practice space in developing countries, GenAI has become a handy tool to save the situation. Despite this boost, GenAI has faced major academic and ethical controversies. This study uses the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to conduct an exhaustive literature search highlighting GenAI’s applications, benefits, challenges, and future prospects in the education sector. Key findings highlight the efficacy of enhancing the theoretical basis of AI in education and offer a promising avenue for educators and AI engineers to conduct collaborative research.

Keywords

GenAI machine learning artificial intelligence education

Article Details

License

Copyright (c) 2025 Everleen Nekesa Wanyonyi, Millicent K. Murithi

Creative Commons License

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

Author Biographies

Everleen Nekesa Wanyonyi, Jaramogi Oginga Odinga University of Science and Technology, Kenya

Everleen Nekesa Wanyonyi is a full-time lecturer at the School of Computing and Information Technology, in the Department of Computer Science at Murang'a University of Technology. She is a highly skilled IT scholar and practitioner, holding both Doctorate and Master's degrees in IT Security and Audit from Jaramogi Oginga Odinga University of Technology. Additionally, she earned her Bachelor of Science degree in Information Technology from KCA University and a KNEC Diploma in Computer Studies from Kisumu National Polytechnic. Everleen is also a certified Datacom specialist, an internal auditor, and a curriculum developer. Her research interests include cybersecurity, networking, and AI applications.

Millicent K. Murithi, Murang'a University of Technology, Kenya

Millicent Kathambi Murithi is a Tutorial Fellow in the Department of Computer Science at Murang’a University of Technology in Kenya. She is currently a PhD candidate in Information Technology at the same institution. Millicent holds a Master of Science degree in Computer Systems from Jomo Kenyatta University of Science and Technology in Kenya. Her research interests include machine learning, software engineering, and natural language processing.

How to Cite
Wanyonyi, E. N., & Murithi, M. K. (2025). A Systematic Review of Gen-AI Applications in Education: Rewards, Challenges and Future Prospects. Pan-African Journal of Education and Social Sciences, 6(1), 1–13. https://doi.org/10.56893/pajes2025v06i01.01
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. AliAhn, S. (2023). The impending impacts of large language models on medical education. Korean Journal of Medical Education, 35(1), 103–107. https://doi.org/10.3946/kjme.2023.253
  2. Aigoogle. (2024). AI Studios - Text to Video Generation Platform Using AI human. https://aistudios.com/
  3. Ali, S., DiPaola, D., Lee, I., Sindato, V., Kim, G., Blumofe, R., & Breazeal, C. (2021). Children as creators, thinkers and citizens in an AI-driven future. Computers and Education: Artificial Intelligence, 2, 100040. https://doi. org/10.1016/j.caeai.2021.100040
  4. Ansari, A. N., Ahmad, S., & Bhutta, S. M. (2023). Mapping the global evidence around the use of ChatGPT in higher education: A systematic scoping review. Education and Information Technologies, 1–41. https://doi.org/10.1007/s10639-023-12223-4
  5. Bahroun, Z., Anane, C., Ahmed, V., & Zacca, A. (2023). Transforming Education: A Comprehensive Review of Generative Artificial Intelligence in Educational Settings through Bibliometric and Content Analysis. Sustainability, 15(17), 12983. https://doi.org/10.3390/su151712983
  6. Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P. S., & Sun, L. (2023). A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT (arXiv:2303.04226). https://doi.org/10.48550/arXiv.2303.04226
  7. Castillo Segura, P., Alario-Hoyos, C., Delgado-Kloos, C., & Fernández Panadero, C. (2023). Leveraging the Potential of Generative AI to Accelerate Systematic Literature Reviews: An Example in the Area of Educational Technology. 1–8. https://doi.org/10.1109/WEEF-GEDC59520.2023.10344098
  8. Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 43. https://doi.org/10.1186/s41239-023-00411-8
  9. Denny, P., Kumar, V., & Giacaman, N. (2023). Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language. Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1, 1136–1142. https://doi.org/10.1145/3545945.3569823
  10. Faiz, A., Kaneda, S., Wang, R., Osi, R., Sharma, P., Chen, F., & Jiang, L. (2024). LLM Carbon: Modeling the end-to-end Carbon Footprint of Large Language Models (arXiv:2309.14393). https://doi.org/10.48550/arXiv.2309.14393
  11. Finnie-Ansley, J., Denny, P., Becker, B. A., Luxton-Reilly, A., & Prather, J. (2022). The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming. Proceedings of the 24th Australasian Computing Education Conference, 10–19. https://doi.org/10.1145/3511861.3511863
  12. Fliki. (2025). Fliki—Turn text into videos with AI voices. Fliki. Retrieved April 9, 2025, from https://fliki.ai
  13. Ghimire, A., Prather, J., & Edwards, J. (2024). Generative AI in Education: A Study of Educators’ Awareness, Sentiments, and Influencing Factors https://www.semanticscholar.org/paper/Generative-AI-in-Education%3A-A-Study-of-Educators%27-Ghimire-Prather/b11ce8ec43b1fffa5606a253e2b4fca3912bb880
  14. https://doi.org/10.48550/arXiv.2403.15586
  15. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020). Generative adversarial networks. Commun. ACM, 63(11), 139–144. https://doi.org/10.1145/3422622
  16. Han, A., Zhou, X., Cai, Z., Han, S., Ko, R., Corrigan, S., & Peppler, K. A. (2024). Teachers, Parents, and Students’ perspectives on Integrating Generative AI into Elementary Literacy Education. Proceedings of the CHI Conference on Human Factors in Computing Systems, 1–17. https://doi.org/10.1145/3613904.3642438
  17. Heaven, W. D. (2023). The original startup behind Stable Diffusion has launched a generative AI for video. MIT Technology Review. https://www.technologyreview.com/2023/02/06/1067897/runway-stable-diffusion-gen-1-generative-ai-for-video/
  18. İpek, Z. H., Gözüm, A. İ. C., Papadakis, S., & Kallogiannakis, M. (2023). Educational Applications of the ChatGPT AI System: A Systematic Review Research. Educational Process: International Journal, 12(3),26–55. https://doi.org/10.22521/edupij.2023.123.2
  19. Jauhiainen, J. S., & Guerra, A. G. (2023). Generative AI and ChatGPT in School Children’s Education: Evidence from a School Lesson. Sustainability, 15(18), 14025. https://doi.org/10.3390/su151814025
  20. Kazemitabaar, M., Chow, J., Ma, C. K. T., Ericson, B. J., Weintrop, D., & Grossman, T. (2023). Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 1–23. https://doi.org/10.1145/3544548.3580919
  21. Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). Exploring generative artificial intelligence preparedness among university language instructors: A case study. Computers and Education: Artificial Intelligence, 5, 100156. https://doi.org/10.1016/j.caeai.2023.100156
  22. Kurtz, G., Amzalag, M., Shaked, N., Zaguri, Y., Kohen-Vacs, D., Gal, E., Zailer, G., & Barak-Medina, E. (2024). Strategies for Integrating Generative AI into Higher Education: Navigating Challenges and Leveraging Opportunities. Education Sciences, 14(5), 503. https://doi.org/10.3390/educsci14050503
  23. Lampropoulos, G., Ferdig, R. E., & Kaplan-Rakowski, R. (2023). A social media data analysis of general and educational use of ChatGPT: Understanding emotional educators. SSRN. http://dx.doi.org/10.2139/ssrn.4468181
  24. Lee, A. V. Y., Tan, S. C., & Teo, C. L. (2023). Designs and practices using generative AI for sustainable student discourse and knowledge creation. Smart Learning Environments, 10(1), 59. https://doi.org/10.1186/s40561-023-00279-1
  25. Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410. DOI: https://doi.org/10.3390/educsci13040410
  26. MacNeil, S., Tran, A., Mogil, D., Bernstein, S., Ross, E., & Huang, Z. (2022). Generating Diverse Code Explanations using the GPT-3 Large Language Model. Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 2, 37–39. https://doi.org/10.1145/3501709.3544280
  27. Mitra, A., Del Corro, L., Mahajan, S., Codas, A., Simoes, C., Agarwal, S., Chen, X., Razdaibiedina, A., Jones, E., Aggarwal, K., Palangi, H., Zheng, G., Rosset, C., Khanpour, H., & Awadallah, A. (2023). Orca 2: Teaching Small Language Models How to Reason (arXiv:2311.11045). https://doi.org/10.48550/arXiv.2311.11045
  28. OECD (2023). Generative AI in the classroom: From hype to reality? https://one.oecd.org/document/EDU/EDPC(2023)11/en/pdf
  29. OpenAI, (2022). ChatGPT: Introducing ChatGPT. https://openai.com/blog/chatgpt/
  30. Patterson, F. (2021). Understanding digitalization and educational change in school by means of activity theory and the levels of learning concept. Education and Information Technologies, 26(1), 187-204. https://doi.org/10.1007/s10639-020-10239-8
  31. Phung, T., Pădurean, V.-A., Cambronero, J., Gulwani, S., Kohn, T., Majumdar, R., Singla, A., & Soares, G. (2023). Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutors. https://doi.org/10.48550/arXiv.2306.17156
  32. Pradana, M., Elisa, H. P., & Syarifuddin, S. (2023). Discussing ChatGPT in education: A literature review and bibliometric analysis. Cogent Education, 10(2), 2243134. https://doi.org/10.1080/233118 6X.2023.2243134
  33. 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(1), 22. https://doi.org/10.1186/s41039-017-0062-8
  34. Rashid, S., Duong-Trung, N., & Pinkwart, N. (2024). Generative AI in Education: Technical Foundations, Applications, and Challenges. https://doi.org/10.5772/intechopen.1005402
  35. Rawte, V., Sheth, A., & Das, A. (2023). A Survey of Hallucination in Large Foundation Models (arXiv:2309.05922). https://doi.org/10.48550/arXiv.2309.05922
  36. Ruiz-Rojas, L. I., Acosta-Vargas, P., De-Moreta-Llovet, J., & Gonzalez-Rodriguez, M. (2023). Empowering Education with Generative Artificial Intelligence Tools: Approach with an Instructional Design Matrix. Sustainability, 15(15), 11524. https://doi.org/10.3390/su151511524
  37. Savelka, J., Agarwal, A., Bogart, C., & Sakr, M. (2023). Large Language Models (GPT) Struggle to Answer Multiple-Choice Questions about Code (Version 1). https://doi.org/10.48550/ARXIV.2303.08033
  38. Sharples, M. (2023). Towards social generative AI for education: Theory, practices and ethics. Learning: Research and Practice. https://www.tandfonline.com/doi/abs/10.1080/23735082.2023.2261131
  39. Stojanov, A. (2023). Learning with ChatGPT 3.5 as a more knowledgeable other: An autoethnographic study. International Journal of Educational Technology in Higher Education, 20(1), 35. https://doi.org/10.1186/s41239-023-00404-7
  40. Wermelinger, M. (2023). Using GitHub Copilot to Solve Simple Programming Problems. Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1, 172–178. https://doi.org/10.1145/3545945.3569830
  41. Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023). The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4, 100147. https://doi.org/10.1016/j.caeai.2023.100147
  42. Yu, H., & Guo, Y. (2023). Generative artificial intelligence empowers educational reform: Current status, issues, and prospects. Frontiers in Education, 8, 1183162. https://doi.org/10.3389/feduc.2023.1183162
  43. Zang, P., & Kamel Boulos, M. (2023). Generative AI in Medicine and Healthcare: Promises, Opportunities, and Challenges. https://www.mdpi.com/1999-5903/15/9/286