Influence of Artificial Intelligence Technologies in Assessment on Trainer-Perceived Performance in Computer Courses at National Polytechnics in Western and Nyanza Regions, Kenya

Artificial Intelligence (AI) is transforming educational assessment globally, yet its application in Technical and Vocational Education and Training (TVET) institutions in Kenya remains limited. This study examined the influence of AI technologies in assessment on trainer-perceived performance in computer courses at eight national polytechnics in Western and Nyanza regions. Guided by Rogers' Diffusion of Innovation (DOI) Theory. The study employed a convergent mixed-methods design with a cross-sectional correlational quantitative component and a qualitative interview component. Stratified random sampling selected 121 trainers, of whom 103 (85.1%) completed questionnaires. Purposive sampling selected 24 senior management staff and 8 County Quality Assurance and Standards Officers for qualitative interviews. The findings revealed moderate utilization of AI technologies in assessment (Mean = 3.00), with trainers expressing strong agreement on AI's transformative potential in improving assessment effectiveness (Mean = 4.06) and enabling personalized learning (Mean = 4.06). Regression analysis demonstrated a significant moderate positive relationship between AI technologies and trainer-perceived performance (R = 0.606, R² = 0.368, p < 0.001), leading to rejection of the null hypothesis. Qualitative data revealed that while trainers recognized AI's potential, adoption was constrained by inadequate infrastructure, limited technical capacity, lack of institutional policies, ethical concerns regarding data privacy and algorithmic bias, and the absence of national guidelines for AI-based assessments. The study concludes that AI technologies were significantly positively associated with trainer-perceived performance but require systemic support for full implementation. Recommendations include development of institutional AI assessment policies, enhanced trainer training, infrastructure investment, and establishment of national quality assurance standards for AI-based assessments in Kenyan TVET institutions.

Artificial intelligence, assessment, TVET, computer courses, trainer-perceived performance, Diffusion of Innovation

1. Adamu, I., Kanbul, S., Gambo, A., & Zanna, T. (2020). Technical and vocational education trainers' computer competencies using artificial intelligence. Journal of Advanced Research in Social Sciences and Humanities, 5(6), 256-269. [Google Scholar] [Crossref]

2. Adebayo, T. O. (2021). Challenges of implementing artificial intelligence tools in Nigerian polytechnics: A focus on rural institutions. Journal of Educational Technology and Innovation, 15(2), 45-59. [Google Scholar] [Crossref]

3. Adeoye, O., Olaleye, S., & Alabi, A. (2021). Enhancing efficiency in assessment through AI: A case study of a National Polytechnic in Lagos. Educational Management Review, 15(2), 80-98. [Google Scholar] [Crossref]

4. Alzakwani, M., Zabri, S. M., & Ali, M. H. (2025). Artificial Intelligence integrated ICT in higher education: An analysis of benefits and challenges. Learning-Gate Journal. [Google Scholar] [Crossref]

5. Baker, T., Smith, L., & Johnson, R. (2023). Integrating AI in education: Technological, organizational, and environmental perspectives. Educational Technology Research and Development, 71(3), 457-475. [Google Scholar] [Crossref]

6. Chanda, K. Z. (2023). The role of artificial intelligence in fostering workplace integrated learning at a South African TVET College during COVID-19 (Doctoral dissertation, University of Johannesburg). [Google Scholar] [Crossref]

7. Communications Authority of Kenya. (2023). First quarter sector statistics report for the financial year 2022/2023. Nairobi, Kenya: CA. [Google Scholar] [Crossref]

8. Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). Sage Publications. [Google Scholar] [Crossref]

9. Dintwe, M., Sebego, R., & Molefe, T. (2021). Enhancing efficiency in assessment through AI: A case study of a National Polytechnic in Gaborone. Educational Management Review, 15(2), 80-98. [Google Scholar] [Crossref]

10. Fischer, T., Müller, S., & Schmidt, K. (2020). AI in Technical and Vocational Education and Training: A comprehensive study in Germany. Journal of Vocational Education and Training, 72(3), 340-357. [Google Scholar] [Crossref]

11. García, A., & López, J. (2020). Adaptive assessment in TVET institutions in Spain: The role of AI. Journal of Vocational Education and Training, 38(2), 112-128. [Google Scholar] [Crossref]

12. Gardner, J., O'Leary, M., & Yuan, L. (2021). Artificial intelligence in educational assessment: 'Breakthrough? Or buncombe and ballyhoo?' Journal of Computer Assisted Learning, 37(3), 789-804. [Google Scholar] [Crossref]

13. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. [Google Scholar] [Crossref]

14. Kamau, J. M., Otieno, R. A., & Mwangi, D. N. (2021). Application of machine learning algorithms in code assessment: A case study of Kenyan polytechnics. Journal of Educational Technology and Innovation, 10(2), 34-45. [Google Scholar] [Crossref]

15. Kariuki, J., & Njoroge, P. (2021). The impact of AI-driven student support systems on student engagement in TVET education in Mombasa. Journal of Educational Technology, 25(2), 237-252. [Google Scholar] [Crossref]

16. Kenya National Bureau of Statistics. (2022). Kenya population and housing census 2019: Volume IV – Distribution of population by socio-economic characteristics. Nairobi, Kenya: KNBS. [Google Scholar] [Crossref]

17. Kenya. (2019). Data Protection Act, No. 24 of 2019. Kenya Law. [Google Scholar] [Crossref]

18. Kim, S., Brown, C., & Johnson, T. (2023). Communication strategies for AI adoption in education: A diffusion of innovation perspective. Educational Communication and Technology Journal, 45(2), 123-137. [Google Scholar] [Crossref]

19. Kunter, M., Klusmann, U., Baumert, J., Richter, D., Voss, T., & Hachfeld, A. (2013). Professional competence of teachers: Effects on instructional quality and student development. Journal of Educational Psychology, 105(3), 805-820. [Google Scholar] [Crossref]

20. Li, J., Zhao, H., & Wang, Y. (2021). Enhancing efficiency in assessment through AI: A case study of a National Polytechnic in Beijing. Educational Management Review, 14(2), 75-91. [Google Scholar] [Crossref]

21. Mboya, S. O. (2024). Impact of Artificial Intelligence on Technical and Vocational Education and Training: A case study of The Kisumu National Polytechnic, Kenya. The Kenya Journal of Technical and Vocational Education and Training, 7, 51. [Google Scholar] [Crossref]

22. Ministry of Education, Kenya. (2025). TVET a key enabler of the government's Vision 2030 actualization. Ministry of Education, Kenya. [Google Scholar] [Crossref]

23. Mukkala, A. (2024). Integrating AI with ICT for transformative education. JISEM Journal. [Google Scholar] [Crossref]

24. Müller, R., Schmidt, H., & Weber, K. (2021). Enhancing efficiency in assessment through AI: A case study of a National Polytechnic in Germany. Educational Management Review, 14(2), 75-91. [Google Scholar] [Crossref]

25. Mutua, L. K., & Kihoro, J. M. (2021). Perceptions of lecturers on AI-driven grading systems in Kenyan national polytechnics. International Journal of Technology in Education and Learning, 6(3), 112-121. [Google Scholar] [Crossref]

26. Ndaba, S., & Mthethwa, N. (2020). Automating grading and feedback using AI: A case study of a National Polytechnic in Johannesburg. Educational Management Review, 14(3), 150-168. [Google Scholar] [Crossref]

27. Ndungu, J., Wanjiku, M., & Okoth, P. (2021). Project-based learning with AI-integrated curricula in Kenyan polytechnics. Journal of Educational Technology in Higher Education, 18(3), 45-62. [Google Scholar] [Crossref]

28. Okoro, C. A., & Nwankwo, U. O. (2023). Effectiveness of AI-driven adaptive feedback in computer science education: A quasi-experimental study. Nigerian Journal of Educational Research and Development, 18(1), 22-37. [Google Scholar] [Crossref]

29. Oluwole, B., & Adebayo, T. (2020). Adaptive assessment in TVET institutions in Ibadan: The role of AI. Journal of Vocational Education and Training, 39(2), 118-135. [Google Scholar] [Crossref]

30. Onyango, E., & Kelonye, C. (2022). Artificial intelligence (AI) driven interventions in technical and vocational education and training. Commonwealth of Learning. [Google Scholar] [Crossref]

31. Owolabi, O. A., & Aladejana, F. O. (2022). Artificial intelligence and assessment efficiency in Nigerian polytechnics. African Journal of ICT in Education, 9(1), 67-79. [Google Scholar] [Crossref]

32. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press. [Google Scholar] [Crossref]

33. Shepherd, S. M., & Willis-Esqueda, C. (2018). Indigenous perspectives on violence risk assessment: a thematic analysis. Punishment and Society, 20, 599-627. [Google Scholar] [Crossref]

34. Smith, T., & Anderson, P. (2022). AI in Apprenticeship Matching: Enhancing Industry-Education Collaboration. Journal of Vocational Education Research, 55(3), 289-305. [Google Scholar] [Crossref]

35. Stryker, C., & Kavlakoglu, E. (2026, June 15). What is artificial intelligence (AI)? IBM. https://www.ibm.com/think/topics/artificial-intelligence [Google Scholar] [Crossref]

36. Walkington, C., & Bernacki, M. L. (2020). Appraising research on personalized learning: Definitions, theoretical alignment, advancements, and future directions. Journal of Research on Technology in Education, 52(3), 235-252. [Google Scholar] [Crossref]

37. Wanjiru, P. N., Musyoka, D. K., & Cheruiyot, S. M. (2022). Artificial intelligence in assessment: Enhancing academic integrity and fairness in Kenyan polytechnics. East African Journal of Educational Research, 11(4), 90-104. [Google Scholar] [Crossref]

38. Wekesa, J., & Omollo, D. (2023). Challenges affecting AI content integration into college curricula in Kenya. Journal of African Educational Research, 17(3), 67-84. [Google Scholar] [Crossref]

39. Williams, K., Martinez, L., & Thompson, R. (2023). Understanding AI adoption in education through innovation characteristics. Journal of Artificial Intelligence in Education, 33(1), 45-62. [Google Scholar] [Crossref]

40. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education--where are the trainers? International Journal of Educational Technology in Higher Education, 16(1), 1-27. [Google Scholar] [Crossref]

41. Zhang, H., & Li, L. (2023). Research status and prospects of artificial intelligence technology applied to education and teaching. Journal of Education, Humanities and Social Sciences, 14, 199-206. [Google Scholar] [Crossref]

42. Zhang, T., & Wang, L. (2020). Adaptive assessment in TVET institutions in Shanghai: The role of AI. Journal of Vocational Education and Training, 38(2), 112-128. [Google Scholar] [Crossref]

43. Zouhaier, S. (2023). The impact of artificial intelligence on higher education: An empirical study. European Journal of Educational Sciences, 10(1), 17-33 [Google Scholar] [Crossref]

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