ASSESSING THE EEFECTIVENESS OF AI-BASED INSTRUCTIONAL TOOLS IN TEACHING OF AGRICULTURAL SCIENCE IN COLLEGES OF EDUCATION IN DELTA STATE: LECTURERS’ PERCEPTION
Abstract
This study assessed the effectiveness AI-Based instructional tools in teaching of Agricultural Sciences in Colleges of Education (COEs) in Delta State, looking at the lecturers’ perception. Three research questions guided the study. The study adopted a descriptive survey research design. The estimated population for this study comprised all the 68 Agricultural Science lecturers in the three public (one Federal and two State Government-owned) Colleges of Education in Delta State (that is: Federal College of Education (Technical), Asaba – 25 lecturers; Delta State College of Education, Mosogar – 26 lecturers; and Delta State College of Education, Warri – 17 lecturers). Sample size for the study comprised all the 68 lectures from the three COEs selected using a purposive sampling technique. The instrument for data collection was a researcher self-structured questionnaire titled “Effectiveness of AI-Based Instructional Tools in Teaching of Agricultural Science Questionnaire (EAIBITTASQ)” containing 29 items. Both validity and reliability of the questionnaire was established. Data collated were analyzed using mean (x̅) statistics and standard deviation (SD) statistics in order to answer the research questions. Findings of the study revealed among others that the AI-based instructional tools such as the intelligent tutoring systems, adaptive learning platforms, Agricultural simulation and visualization tools and learning analytics dashboards can be effectively utilized in teaching Agricultural Science in COEs in Delta State. Based on the findings, it was recommended among others that the Government (both federal and state), educational policymakers and other important education stakeholders should assist the colleges of education in Delta State through adequate resource mobilization to provide the necessary digital infrastructure (reliable internet, functional ICT labs, intelligent tutoring systems, adaptive learning platforms, virtual simulations, VR/AR tools, AI-enhanced LMS, and mobile AI tools, among others) alongside clear policies for ethical and sustainable use of AI-based instructional tools in teaching of Agricultural Science. This will ensure equitable access and maximize the positive impact of AI tools towards promoting effectiveness of teaching and learning in Agricultural Science in the COEs.
Downloads
References
Abrahamson, D., DiSessa, A. A. & Stamper, J. (2024). Have learning analytics dashboards lived up to the hype? A systematic review. Proceedings of the ACM on Human-Computer Interaction, 8 (LAK), 1–36. https://doi.org/10.1145/3636555.3636884.
Adekunle, O. & Fatunsin, O. (2019). Agricultural science education and sustainable development in Nigeria. Journal of Agricultural Extension, 23 (2), 45–56.
Adeyemi, A. E., Ma, S., Xu, Z. & Landaverde, R. (2024). Outcome-based use of simulation in agricultural sciences: A systematic literature review. Journal of Agricultural Education, 65 (3), 75–98. https://files.eric.ed.gov/fulltext/EJ1450442.pdf.
Aguilar, S. J. & John, M. (2023). Learning analytics dashboards are increasingly becoming about learning and not just analytics: A systematic review. Education and Information Technologies, 28, 12963–12994. https://doi.org/10.1007/s10639-023-12401-4.
Amanze, P. C. (2025). AI-powered agro-science simulations: Enhancing comprehension and application among university students in the Niger Delta region of Nigeria. Asian Journal of Education and Social Studies, 51 (6), 200–213. https://doi.org/10.9734/ajess/2025/v51i61987.
Azcona, G., Balakrishnan, P., Maestrales, J., Mirchi, N., & others. (2019). [Studies on AI and personalized feedback in education]. In Xu, Z., Adeyemi, A. E., Landaverde, R., Kogut, A. & Baker, M. (2023). A scoping review on the impact of educational technology in agricultural education. Educ. Sci., 13 (9), 910. https://doi.org/10.3390/educsci13090910MDPI.
Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W. & Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21(1). https://doi.org/10.1186/s41239-023-00436-z.
Boulmetis, J. & Dutwin, P. (2011). The ABCs of evaluation: Timeless techniques for program and project managers (2nd ed.). Jossey-Bass.
Bruner, J. (1996). The culture of education. Harvard University Press.
Chen, X., Li, Y. & Zhang, H. (2025). Unraveling the mechanisms and effectiveness of AI-assisted feedback in higher education: A systematic review. Computers and Education: X Reality, 5, 100196. https://doi.org/10.1016/j.caeo.2025.100196.
Copeland, B. J. (2020). Artificial intelligence. Encyclopedia Britannica. https://www.britannica.com/ technology/artificial-intelligence.
Creswell, J. W. & Creswell, J. D. (2023). Research design: Qualitative, quantitative, and mixed methods approaches (6th ed.). Sage Publications.
Di Mitri, D. & Martinez-Maldonado, R. (2023). A current overview of the use of learning analytics dashboards. Education Sciences, 14 (1), 82. https://doi.org/10.3390/educsci14010082.
Federal Republic of Nigeria (FRN, 2014). National policy on education (6th edition). Abuja: Nigerian Educational Research and Development Council (NERDC).
Financial Times (2025, July). Chatbots in the classroom: How AI is reshaping higher education. Financial Times
Frontiers in Plant Science (2024). Revolutionizing agriculture with artificial intelligence: Plant disease detection and related applications. Frontiers in Plant Science. https://www.frontiersin.org/articles/.
Gamage, D. & Whitelock-Wainwright, A. (2025). Chatbots in education: Hype or help? A meta-analysis. Computers & Education: Artificial Intelligence, 6, 100222. https://doi.org/10.1016/j.caeai.2025.100.
Ha, T. & Lee, S. (2025). The effect of ChatGPT on students’ learning performance, perception, and higher-order thinking: A meta-analysis. Humanities and Social Sciences Communications, 12, 220. https://doi.org/10.1057/s41599-025-04787-y.
Johnson, D. M., Doss, W. & Estepp, C. M. (2024). Agriculture students’ use of generative artificial intelligence for microcontroller programming. https://doi.org/10.1002/nse2.20155help.
Kim, S. & Park, J. (2024). AI-assisted assessment in higher education: A systematic review. Journal of Educational Technology and Innovation, 4 (2), 55–89. https://jeti.thewsu.org/index.php/cieti/article/.
Koć-Januchta, M., Siddaway, J., Lamb, R., Ledesma, R., García, P. & others. (2020). [Meta-analyses on AI in science education]. In a systematic review (2024) showing improvements in academic performance and engagement. Research in Science Education. https://link.springer.com/.
Luo, J., Zheng, C., Yin, J. & Teo, H. H. (2025). Design and assessment of AI-based learning tools in higher education: A systematic review. International Journal of Educational Technology in Higher Education, (22) 42, 1-27. https://doi.org/10.1186/s41239-025-00540-2.
Nworgu, B. G. (2015). Educational research. Basic issues and methodology, third edition. Nsukka: University Trust Publishers.
Ofojebe, W. N. (2014). Teacher education and professionalism: Issues and challenges. Journal of Teacher Education, 15 (2), 102–113.
Ogunbameru, B. O. (2001). Practical agricultural communication. Daily Graphics Nigeria Ltd.
Okolie, U. C. & Egbon, T. N. (2025). Artificial intelligence and teaching-learning process in education institutions in Nigeria: A study of undergraduate students of Delta State University, Abraka. Journal Plus Education, 37 (1). https://uav.ro/jour/index.php/jpe/article/view/2273 UAV.
Perculiar, O. A. & Okoh, O. (2025). Integrating technology in agricultural education: A case study of University of Delta, Agbor, Delta State, Nigeria. Asian Journal of Research and Review in Agriculture, 7 (1), 17–25. https://doi.org/10.56557/ajrra/2025/v7i1151.
Russell, S. & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
Samuel, D. J., Skarga-Bandurova, I., Sikolia, D. & Awais, M. (2025). AgroLLM: Connecting farmers and agricultural practices through large language models for enhanced knowledge transfer and practical application. https://arxiv.org/.
Sanyal, N. & Kuri, J. (2024). Effects of generative AI on K-12 and higher education learning. Journal of Educational Computing Research. https://doi.org/10.1177/07356331251329185.
Sharma, S. & Gupta, P. (2025). Effectiveness of virtual learning systems in agricultural education: A review. Frontiers in Education, 10, 1588899. https://doi.org/10.3389/feduc.2025.1588899
show/123080374.cms.
Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48 (6), 1273–1296. https://doi.org/10.1007/s11165-016-9602-2.
Times of India (2025). AI, but verify: Navigating future of learning (IIT Delhi report on GenAI use). https://timesofindia.indiatimes.com/city/delhi/ai-but-verify-navigating-future-of-learning/article
UNESCO (2024). AI competency framework for teachers. UNESCO. https://www.unesco.org/en/articles/ai-competency-framework-teachers.
World Bank (2024). Exploring AI capacity building programs in Nigerian higher education. Policy note on adopting a digital literacy framework in Nigeria (World Bank policy note). https://thedocs.worldbank.org/...
Xu, Z., Adeyemi, A. E., Landaverde, R., Kogut, A. & Baker, M. (2023). A scoping review on the impact of educational technology in agricultural education. Educ. Sci., 13 (9), 910. https://doi.org/10.3390/educsci13090910 MDPI.
Zhou, D., Fan, Y., Deng, G., He, F. & Wang, M. (2020). A new design of sugarcane seed cutting systems based on machine vision. Computers and Electronics in Agriculture, 175. https://doi.org/10.1016/j.compag.2020.105611.
Author(s) and co-author(s) jointly and severally represent and warrant that the Article is original with the author(s) and does not infringe any copyright or violate any other right of any third parties, and that the Article has not been published elsewhere. Author(s) agree to the terms that the GPH Journal will have the full right to remove the published article on any misconduct found in the published article.
