Assessment on Integrating Artificial Intelligence Technological and Pedagogical Competencies among Secondary School Chemistry Teachers in Kaduna State, Nigeria
DOI:
https://doi.org/10.64348/zije.202571Keywords:
Artificial Intelligence, Competence, Chemistry teachers, Secondary school.Abstract
The study assessed the Integration of Artificial Intelligence Technological and Pedagogical Competencies among Secondary School Chemistry Teachers in Kaduna State, Nigeria. Adopting a descriptive survey design, the research was guided by two research questions and two corresponding null hypotheses. A random sampling technique was employed to select a representative group of chemistry teachers from various secondary schools within the state. The sample comprised 150 chemistry teachers (72 males and 78 females), drawn from a population of 1,500 teachers across 80 secondary schools. To collect data, the researcher developed a structured questionnaire comprising two sections: Section 1, titled Artificial Intelligence Chemistry Teachers’ Technological Competence for Curriculum Implementation Questionnaire (AICTTCCIQ), and Section 2, Artificial Intelligence Chemistry Teachers’ Pedagogical Competence for Curriculum Implementation Questionnaire (AICTPCCIQ), each containing seven items. The instrument was face-validated by two data analysts and two senior chemistry educators from the Department of Science Education, Ahmadu Bello University, Zaria. Reliability testing using Cronbach’s alpha yielded coefficients of 0.71 and 0.73 respectively, indicating a satisfactory level of internal consistency. Data analysis involved the use of means to address the research questions, while t-test statistics were used to test the hypotheses at the 0.05 significance level. Key findings from the study include: (i) Both male and female chemistry teachers exhibited low levels of competence, technologically and pedagogically, in the application of AI tools for teaching. (ii) There was no statistically significant difference between male and female teachers regarding their technological competence in AI. (iii) Similarly, no significant difference was found in pedagogical competence related to AI usage between genders. Based on these findings, the study recommended that the Kaduna State government, in collaboration with relevant educational agencies, should prioritize the provision of essential AI tools and infrastructure. Such support would enable chemistry teachers to effectively integrate AI into lesson planning, delivery, and assessment, thereby enhancing the quality of chemistry education in secondary schools.
References
Aliyu, J, A, , Hamza N O, Bashirat K, Osman, S (2024), Mathematics as the Bedrock of Artificial Intelligence and Creative Thinking for National Development in Nigeria: A Systematic Review. Zamfara International Journal of Education (ZIJE) 4, (4) 164
Anaso J.N. (2024a) Comparative Study on the Effectiveness of Demonstration and Discussion Strategies on Academic Achievement and Retention of Chemistry Concepts by students in Secondary Schools in Kaduna State, Nigeria. Journal of Science Technology and Education. 12 (3) 739-748. www.atbuftejoste.com
Anaso J.N (2021) Critical Elements of Science Education. Ndahi Press and Publishers, Zaria. Reprinted January 2017 and Febuary 2021
Anaso J.N. (2024b) Enhancing Chemistry Students’ Academic Achievement using Molecular Models in teaching Nomenclature in Secondary Schools in Kaduna State, Nigeria. VUNOKLANG Multidisciplinary Journal of Science and Technology Education, 12(3) 294-303. https://vmjste.com.ng
Berber, S., Bruckner, M,. Maurer, N. & Huwer (2025) Artificial Intelligence in Chemistry Research - Implications for teaching and Learning. Journal of Chemical Education. 102, 1445-1456 DOI: https://doi.org/10.1021/acs.jchemed.4c01033
Culican, .J. (2024) The impact of AI on educational content creation: Shaping the future of learning materials. Available from https://www.linkedin.com/pulse/impactai-educationalcontent-creation
Dai, C. & Ke, F. (2022). Educational applications of artificial intelligence in simulation-based learning: A systematic mapping review. Computer and Education: Artificial Intelligence. Vol 3, 2022, 100087. ISSN 2666-920X, https://doi.org/10.1016/j.caeai.2022.100087. DOI: https://doi.org/10.1016/j.caeai.2022.100087
Daves, S. (2023) How AI can deliver personalized learning and transform academic assessment. Available from https//www.tand.fonline.com/doi/full/10.1080/03323315
Falebita, O. S. (2024). Assessing the relationship between anxiety and the adoption of Artificial Intelligence tools among mathematics preservice teachers. Interdisciplinary Journal of Education Research, 6, 1–13. https://doi.org/10.38140/ijer-2024.vol6.20 DOI: https://doi.org/10.38140/ijer-2024.vol6.20
Jiang S., McClure .J.; Mao, H.; Chen J.; Liu, y.; Zhang, Y. (2024) Integrating machine learning and Color Chemistry: Developing a High-school Curriculum toward Real-World Problem-solving. Chemical Education. 101(2) 675-681 DOI: https://doi.org/10.1021/acs.jchemed.3c00589
Koehler, M.J.; Mishra P.; Cain, W. (2013) What is Technological, Pedagogical Content Knowledge (TPACK) Journal of Education. 193 (3) 13-19 DOI: https://doi.org/10.1177/002205741319300303
Lameras, P., & Arnab, S. (2022) Power to the Teachers: An exploratory Review on Artificial Intelligence. Education Information. 13, 14 DOI: https://doi.org/10.3390/info13010014
Marr, B. (2025) How is AI used in Education-real world examples of today and apeek into the future. Available from https//bernardmarr.com/how.is-ai-used-in-education-real-world example-of-today-and-a peek-into the-future
Opesemowo, O. A. G., & Ndlovu, M. (2024). Artificial intelligence in mathematics education: The good, the bad, and the ugly. Journal of Pedagogical Research https://doi.org/10.33902/jpr.202426428 DOI: https://doi.org/10.33902/JPR.202426428
Zafrullah, Hakim, M. L., & Angga, M. (2023). ChatGPT Open AI: Analysis of Mathematics Education Students Learning Interest. Journal of Technology Global, 1(01), 1–10.
						
							


