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Effect of Artificial Intelligence-Supported Problem-Based Learning on Employability Skills and Academic Achievement of Vocational Agricultural Education Students in Nigerian Universities

Evaristus Ogochukwu Nwali 1,
Chiamaka Chikammara Eya 1,
Oluchi Erinma Okafor 2,
Toochukwu Collins Nwakile 1 *
1 University of Nigeria, Nsukka, NIGERIA2 Alvan Ikoku Federal University of Agriculture, Owerri, Imo State, NIGERIA* Corresponding Author
Educational Point, 3(2), 2026, e176, https://doi.org/10.71176/edup/19767
Publication date: Nov 05, 2026
Research Article
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ABSTRACT

The integration of artificial intelligence into higher education has created opportunities for innovative instructional approaches that may enhance students' employability skills and academic achievement. This study investigated Artificial Intelligence-Supported Problem-Based Learning (AI-PBL) in relation to the self-reported employability skills and percentage academic achievement of Vocational Agricultural Education students in selected Nigerian universities. A quasi-experimental pretest-posttest nonequivalent control group design was adopted. The study involved 138 students drawn from two federal universities in South East Nigeria using multistage sampling procedures. The experimental group was taught using AI-PBL for six weeks with the support of ChatGPT, Microsoft Copilot, and other approved artificial intelligence resources, while the control group received instruction using the conventional lecture method. Data were collected using the Employability Skills Assessment Scale (ESAS) and the Agricultural Education Achievement Test (AEAT). Raw scores obtained from the AEAT were converted to percentage scores before analysis. The instruments were validated by experts, while reliability coefficients of 0.89 and 0.86 were obtained for the ESAS and AEAT, respectively. The mean and standard deviation were used to answer the research questions, whereas Analysis of Covariance was employed to test the hypotheses at the 0.05 level of significance after confirming that the assumptions underlying ANCOVA were satisfied. The findings revealed that students exposed to AI-PBL obtained significantly higher self-reported employability skills and percentage academic achievement than those taught using the conventional lecture method (p < .001), with large effect sizes observed for both outcomes. No statistically significant gender differences were found among students within the AI-PBL group. The findings suggest that AI-PBL is associated with higher workplace-related competencies and academic learning outcomes among Vocational Agricultural Education students under the conditions of the present study. The study recommends the integration of AI-PBL into university curricula alongside institutional support for the ethical and responsible use of artificial intelligence in teaching and learning.

KEYWORDS

artificial intelligence problem-based learning employability skills academic achievement vocational agricultural education higher education

CITATION (APA)

Nwali, E. O., Eya, C. C., Okafor, O. E., & Nwakile, T. C. (2026). Effect of Artificial Intelligence-Supported Problem-Based Learning on Employability Skills and Academic Achievement of Vocational Agricultural Education Students in Nigerian Universities. Educational Point, 3(2), e176. https://doi.org/10.71176/edup/19767
Harvard
Nwali, E. O., Eya, C. C., Okafor, O. E., and Nwakile, T. C. (2026). Effect of Artificial Intelligence-Supported Problem-Based Learning on Employability Skills and Academic Achievement of Vocational Agricultural Education Students in Nigerian Universities. Educational Point, 3(2), e176. https://doi.org/10.71176/edup/19767
Vancouver
Nwali EO, Eya CC, Okafor OE, Nwakile TC. Effect of Artificial Intelligence-Supported Problem-Based Learning on Employability Skills and Academic Achievement of Vocational Agricultural Education Students in Nigerian Universities. Educational Point. 2026;3(2):e176. https://doi.org/10.71176/edup/19767
AMA
Nwali EO, Eya CC, Okafor OE, Nwakile TC. Effect of Artificial Intelligence-Supported Problem-Based Learning on Employability Skills and Academic Achievement of Vocational Agricultural Education Students in Nigerian Universities. Educational Point. 2026;3(2), e176. https://doi.org/10.71176/edup/19767
Chicago
Nwali, Evaristus Ogochukwu, Chiamaka Chikammara Eya, Oluchi Erinma Okafor, and Toochukwu Collins Nwakile. "Effect of Artificial Intelligence-Supported Problem-Based Learning on Employability Skills and Academic Achievement of Vocational Agricultural Education Students in Nigerian Universities". Educational Point 2026 3 no. 2 (2026): e176. https://doi.org/10.71176/edup/19767
MLA
Nwali, Evaristus Ogochukwu et al. "Effect of Artificial Intelligence-Supported Problem-Based Learning on Employability Skills and Academic Achievement of Vocational Agricultural Education Students in Nigerian Universities". Educational Point, vol. 3, no. 2, 2026, e176. https://doi.org/10.71176/edup/19767

REFERENCES

  1. Adukaite, A., van Zyl, I., Er, Ş., & Cantoni, L. (2017). Teacher perceptions on the use of digital gamified learning in tourism education: The case of South African secondary schools. Computers & Education, 111, 172–190. https://doi.org/10.1016/j.compedu.2017.04.008
  2. Ary, D., Jacobs, L. C., Sorensen, C., & Walker, D. (2019). Introduction to research in education (10th ed.). Cengage Learning. https://www.cengage.com/c/introduction-to-research-in-education-10e-ary-jacobs-sorensen-walker/9781337566001/
  3. Biggs, J., & Tang, C. (2018). Teaching for quality learning at university (4th ed.). McGraw-Hill Education. https://cetl.ppu.edu/sites/default/files/publications/-John_Biggs_and_Catherine_Tang-_Teaching_for_Quali-BookFiorg-.pdf
  4. Carmi, G. (2025). Learning with generative AI: An empirical study of students in higher education. Education Sciences, 15(12), Article 1696. https://doi.org/10.3390/educsci15121696
  5. Ekenta, L. U., Okadi, A. O., Ugwuoke, C. U., Ali, C. C., Nwakile, T. C., Bassey, N. N., & Ikehi, M. E. (2026). Seeding dreams, harvesting uncertainty: Graduate aspirations and labour market realities in Nigeria’s vocational agricultural education. Higher Education, Skills and Work-Based Learning, 16(2), 293-310. https://doi.org/10.1108/HESWBL-07-2025-0275
  6. Federal Republic of Nigeria. (2013). National policy on education (6th ed.). Nigerian Educational Research and Development Council (NERDC) Press. https://nipc.gov.ng/assets/resources/national-policy-on-education-2013/national-education-policy-2013.pdf
  7. Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review, 16(3), 235–266. https://doi.org/10.1023/B:EDPR.0000034022.16470.f3
  8. Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. https://curriculumredesign.org/wp-content/uploads/AI-in-Education.pdf
  9. Jackson, D. (2016). Re-conceptualising graduate employability: The importance of pre-professional identity. Higher Education Research & Development, 35(5), 925–939. https://doi.org/10.1080/07294360.2016.1139551
  10. Jonassen, D. H. (2017). Learning to solve problems: A handbook for designing problem-solving learning environments. Routledge. https://staibabussalamsula.ac.id/wp-content/uploads/2024/03/LEARNING-TO-SOLVE-PROBLEM-staibabussalamsula.ac_.id_.pdf
  11. Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Prentice Hall. https://www.researchgate.net/publication/235701029_Experiential_Learning_Experience_As_The_Source_Of_Learning_And_Development
  12. Lioutas, E. D., Charatsari, C., & De Rosa, M. (2021). Digitalisation of agriculture: A way to solve the food problem or a trolley dilemma? Technology in Society, 67, Article 101744. https://doi.org/10.1016/j.techsoc.2021.101744
  13. Nwachukwu, C. U., Onah, F. C., Obijiofor, E. O., Nwankwo, C. U., & Nwakile, T. C. (2020). Effects of multiple teaching methods on academic achievement and interest of primary school pupils in agricultural science in Anambra State. International Journal of Multidisciplinary and Current Research, 8, 415–420. https://doi.org/10.14741/ijmcr/v.8.3.10
  14. Nwakile, T. C., Izuakor, C. F., Omeh, C. B., & Chukwu, D. U. (2026). Readiness of TVET educators for AI-supported instruction: A multi-dimensional assessment of competencies and pedagogical adaptation. TVET@Asia, 26, 1–20. https://tvet-online.asia/wp-content/uploads/2026/03/TVET@Asia-Issue-26_paper-Nwakile-et-al-1.pdf
  15. Nwakile, T.C., Nwankwo C.U., Ekenta, L.U., Ameh H.I., & Nwokolo E. (2022). Comparative effects of demonstration method and blending learning on academic achievement and interest of agricultural science students in Nsukka Education Zone of Nigeria. International Journal of Research in Social Science and Humanities, 3(6), 9–15. https://doi.org/10.47505/IJRSS.2022.V3.6.2
  16. Nworgu, B. G. (2015). Educational research: Basic issues and methodology (3rd ed.). University Trust Publishers.
  17. OECD. (2021). OECD skills outlook 2021: Learning for life. OECD Publishing. https://doi.org/10.1787/0ae365b4-en
  18. Okoli, T. O., Ejiofor, T. E., Ali, C. C., & Nwakile, T. C. (2020). 21st century TVET policies and practices in Nigeria: Challenges and enhancement measures. Vocational and Technical Education Journal, 2(1), 13–21. https://vte.unn.edu.ng/wp-content/uploads/sites/60/2020/06/Faculty-of-VTE-Journal-Abstracts.pdf
  19. Piaget, J. (1972). The psychology of the child. Basic Books. https://unesdoc.unesco.org/ark:/48223/pf0000397150
  20. Reicho, M., Otrel-Cass, K., Ebner, M., Brünner, B., Freinhofer, D., & Irfan, J. (2026). Generative AI literacy across education and business: Competencies, obstacles, and benefits—A systematic literature review. International Journal of Educational Technology in Higher Education, 23, Article 23. https://doi.org/10.1186/s41239-026-00596-8
  21. Umeh, C. R., Izuakor, C. F., Onah, F.C., Eya, C. C., Ameh, H. I., Nwakile, T. C., & Onyia, C.C. (2025). Green skill training needs of agricultural education students in universities for employment in precision agriculture in Enugu State. Vocational And Technical Education Journal, 5(1), 70 - 77. https://www.researchgate.net/publication/396045753
  22. UNESCO. (2023). Guidance for generative AI in education and research. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000386693
  23. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press. https://www.hup.harvard.edu/books/9780674576292
  24. World Bank. (2020). Spotlight two: Making the most of young Nigerians’ economic potential: The case for more and better managed international labour migration from Nigeria. World Bank. https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099820406212269200
  25. Wut, T. M., Chan, E. A. H., Wong, H. S. M., & Chan, J. K. Y. (2025). Perceived artificial intelligence literacy and employability of university students. Education + Training, 67(2), 258–274. https://doi.org/10.1108/ET-06-2024-0272

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