Evaluation of Artificial Intelligence Tools Integration among Pre- Service Biology Teachers: A Case Study of Federal University of Education, Zaria, Kaduna State, Nigeria

MUSA, Hauwa Rabiu; ISAH, Aishat Yakubu; OLAITAN, Aisha Salau

doi:10.64348/zije.2026353

Authors

MUSA, Hauwa Rabiu Author
ISAH, Aishat Yakubu Author
OLAITAN, Aisha Salau Author

DOI:

https://doi.org/10.64348/zije.2026353

Keywords:

Artificial Intelligence Tools, Pre-service Biology Teachers, Federal University of Education Zaria

Abstract

The study evaluated the integration of artificial intelligence (AI) tools among in pre-service Biology teachers in Federal University of Education, Zaria Kaduna State. The study was guided by four research objectives with corresponding research questions and null hypotheses, respectively. A descriptive survey research design was used for the study. The population of the study were all pre-service Biology students during the 2024/2025 academic session in Federal University of Education, Zaria. Kaduna State. A sample size of 225 pre-service Biology students were selected from the target population using simple random sampling technique. Data were collected using a structured instrument titled Challenges and Opportunities of Artificial Intelligence in Pre-service Biology Teacher Education Questionnaire (COAIPBTEQ) with a reliability coefficient of 0.88. inferential statistics (t-test) were used to test the null hypotheses. Findings of the study revealed that there was no statistically significant relationship between the level of awareness and the use of AI tools among pre-service Biology teachers (r = –0.101, p > 0.05). However, a significant difference was found between institutions with high and low levels of support for AI integration (p < 0.05). Additionally, a significant gender difference was observed in AI tool usage (p < 0.05), with male students showing higher usage rates than their female counterparts. Based on these findings, the study recommends that the government and educational institutions, such as the Federal University of Education, Zaria, invest in digital infrastructure, including reliable internet connectivity, smart classrooms, and AI-enabled platforms, to enhance the integration and effective use of AI in teacher education.

References

Afolabi, S. S., & Odeyemi, K. O. (2021). Challenges of adopting artificial intelligence in Nigerian teacher education. Nigerian Journal of Educational Technology, 14(2), 45–56.

Ajayi, A. O., Bakare, T. A., & Ogunleye, J. O. (2020). AI-enabled learning in science classrooms: Potentials for Nigeria’s pre-service education. African Journal of Educational Studies, 8(1), 65–78.

Alhassan, R. A., & Nwokedi, E. M. (2023). Readiness of teacher education institutions in adopting AI for instructional delivery. Contemporary Issues in Education, 21(4), 123–136.

Chen, X., Xie, H., & Hwang, G. J. (2019). A blockchain-based framework for AI-driven smart learning environments. Interactive Learning Environments, 27(8), 1046–1059.

Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers & Education, 146, 103751. https://doi.org/10.1016/j.compedu.2019.103751 DOI: https://doi.org/10.1016/j.caeai.2020.100002

Chukwudi, A. & Kalu, O. (2020). Prospects of artificial intelligence in Biology education in Nigerian colleges of education. Journal of Biology and Science Pedagogy, 7(3), 101–112.

Ede, M. O., & Ndudi, B. E. (2019). Barriers to ICT adoption in Nigerian teacher education programs. International Journal of Educational Management, 18(2), 89–101.

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Boston, MA: Center for Curriculum Redesign.

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.

Musa, R. T., & Omotayo, L. F. (2023). Virtual reality and AI tools for science teacher preparation: A case study of Nigerian universities. Journal of Digital Education and Smart Learning, 9(1), 15–29.

Nwakpa, P. O., & Udeh, G. I. (2024). Enhancing science education in Nigeria through artificial intelligence: Policy options and training models. Nigerian Journal of Science Education Research, 12(2), 33–49.

Obielodan, B. A., Oladejo, M. A., & Ayodeji, I. F. (2023). Pre-service teachers’ competence and perception of AI tools in teaching biology. Journal of Instructional Innovation in Science Education, 5(2), 47–61.

Okonkwo, C. N., Etuk, E. B., & Ebong, E. E. (2022). Artificial intelligence and intelligent tutoring systems: An opportunity for transforming teacher education. African Journal of Educational Technology, 10(1), 24–39.

Olalekan, O. A., & Salami, R. K. (2022). Curriculum gaps in pre-service teacher training on digital skills and AI integration. Journal of Education Policy and Digital Transformation, 6(4), 78–91.

Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.

Shu, H., & Guo, H. (2021). Teaching in the age of artificial intelligence: Implications for teacher education. Computers & Education, 161, 104094.

Suryawanshi, D., Bhutada, S., & Kadam, A. (2023). AI in science education: Opportunities, challenges, and future directions. International Journal of STEM Education, 10(1), 45–58.

UNESCO. (2021). AI and Education: Guidance for policy-makers. Paris: United Nations Educational, Scientific and Cultural Organization.

Williamson, B., & Piattoeva, N. (2020). Objectivity as standardization in data-scientific educational governance: Grasping the global through the local. Education Policy Analysis Archives, 28(51), 1–25.

Woolf, B. P. (2010). Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. Morgan Kaufmann.

World Bank. (2023). Transforming Education Systems with Artificial Intelligence: A Framework for Action. Washington, DC: World Bank Publications.

Yakubu, Y. S., & Bello, T. O. (2023). Ethical implications of AI in Nigerian teacher education. Journal of Educational Ethics and Innovation, 7(3), 109–122.

Zhang, Y., Lin, P., & Yu, Z. (2022). Integrating artificial intelligence in science teacher training: A systematic review. Teaching and Teacher Education, 117, 103828.