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

Abstract

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.

Keywords:

• Pre-service teachers
• AI-based technology
• genetics
• attitude
• behavioral intention

REVIEWING EDITOR:

• Sirui Wang
• UCLA: University of California Los Angeles
• Los Angels
• CA, USA

SUBJECTS:

• Information & Communication Technology (ICT)
• Internet & Multimedia - Computing & IT
• Human Computer Intelligence

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.

Notes on contributors

Owolabi Paul Adelana

Owolabi Paul Adelana is currently a postgraduate researcher with the Institute of Educational Technology at The Open University, England, UK. His research centres predominantly on exploring artificial intelligence in Education (AIED) and the ethical implications of artificial intelligence applications in education with a specific focus on AI ethics in STEM/STEAM education.

Musa Adekunle Ayanwale

Musa Adekunle Ayanwale, PhD, currently serves as a Senior Postdoctoral Research Fellow at the Department of Science and Technology Education, and a recipient of the Global Excellence & Stature Fellowship Award at the University of Johannesburg, South Africa. With a PhD in educational research, measurement, and evaluation, he possesses a wealth of expertise in the field of assessment and psychometrics. His research interests encompass a broad spectrum, including the development and validation of assessment tools, psychometrics, reliability analysis based on generalizability theory, Q-Methodology, structural modelling, and various aspects of computing education such as artificial intelligence in education, programming education, machine learning, and big data.

Ismaila Temitayo Sanusi

Ismaila Temitayo Sanusi, is a Researcher at the School of Computing, University of Eastern Finland. His research interests include educational technology and computing education.