Exploring pre-service biology teachers’ intention to teach genetics using an AI intelligent tutoring - based system

Adelana, Owolabi Paul; Ayanwale, Musa Adekunle and Sanusi, Ismaila Temitayo (2024). Exploring pre-service biology teachers’ intention to teach genetics using an AI intelligent tutoring - based system. Cogent Education, 11(1), article no. 2310976.

DOI: https://doi.org/10.1080/2331186x.2024.2310976


This study addresses the challenge of teaching genetics effectively to high school students, a topic known to be particularly challenging. Leveraging the growing importance of artificial intelligence (AI) in education, the research explores the perspectives, attitudes, and behavioral intentions of pre-service teachers regarding the integration of AI-based applications in high school genetics education. As these pre-service teachers, commonly denoted as digital natives, are expected to seamlessly integrate technology into their future classrooms in our technology-dependent society, understanding their viewpoints is crucial. The research involved 90 teacher candidates specializing in biology from Nigerian higher education institutions. Employing the Theory of Planned Behavior, survey responses were analyzed using structural equation modeling and independent sample t-test methods. The results indicate that perceived usefulness and subjective norms are significant predictors of AI use, with subjective norms strongly influencing pre-service teachers’ behavioral intentions. Notably, perceived behavioral control does not significantly predict intentions, paralleling the observation that perceive usefulness does not guarantee AI adoption. Gender differentially affects subjective norms, particularly among female pre-service teachers, while no significant gender differences are observed in other variables, suggesting comparable attitudes. The study underscores the pivotal role of attitudes and social norms in shaping pre-service teachers’ decisions regarding AI technology integration. Detailed discussions on implications, limitations, and potential future research directions are also discussed.

Viewing alternatives

Download history


Public Attention

Altmetrics from Altmetric

Number of Citations

Citations from Dimensions

Item Actions