Students’ Reactions Toward the Use of An AI-Powered Website for Learning Cardiovascular Biology

Ebenezer Omolafe Babalola; Charles Olubode Olumorin; Eyiyemi Veronica Omolafe

doi:10.36312/2e4ngb50

Authors

Ebenezer Omolafe Babalola University of Ilorin
Charles Olubode Olumorin University of Ilorin
Eyiyemi Veronica Omolafe University of Hull

DOI:

https://doi.org/10.36312/2e4ngb50

Keywords:

AI-powered website, Cardiovascular biology, Learning, Students’ reactions

Abstract

Artificial Intelligence-powered websites (AI-PW) leverage artificial intelligence to deliver personalised and adaptive learning experiences, enhancing engagement and accessibility. Despite their potentials, there is a shortage of empirically validated-AI resources for learning Cardiovascular Biology (CVB) in Nigeria. This study investigated students’ reactions toward the use of an AI-powered website for learning CVB in Nigeria. The study adopted a research design involving undergraduates from two purposively selected universities in Ilorin metropolis. A total of 68 students participated in the study. Data were collected using the Students’ Reaction Questionnaire (SRQ), which demonstrated a high reliability coefficient of 0.94. Descriptive statistics and t-tests were employed to analyze the data at a 0.05 level of significance. Findings revealed that students’ reactions toward the use of the AI-powered website for learning CVB were positive (x = 2.86 > 2.50). Furthermore, there was no significant difference in students’ reactions toward the use of the developed AI-powered website for learning CVB based on gender. The study concluded that the use of the BeeNCardiac AI-powered website positively influenced students’ learning experiences in cardiovascular biology. It is therefore recommended that students be encouraged to use the BeeNCardiac AI-powered website as a complementary tool for learning CVB in Nigerian universities.

References

Adams, D., & Thompson, P. (2025). Transforming School Leadership with Artificial Intelligence:Applications, Implications, and Future Directions. Leadership and Policy in Schools, 24(1), 77-89.

Almusharraf, A. I. (2024). An Investigation of University Students’ Perceptions of Learning Management Systems: Insights for Enhancing Usability and Engagement. Sustainability, 16(22), 10037.

Alyoussef, I. Y., & Omer, O. M. A. (2023). Investigating Student Satisfaction and Adoption of Technology-enhanced Learning to Improve Educational Outcomes in Saudi Higher Education. Sustainability, 15(19), 14617.

Anis, M. (2023). Leveraging Artificial Intelligence for Inclusive English Language Teaching:Strategies and Implications for Learner Diversity. Journal of Multidisciplinary Educational Research, 12(6), 54-70.

Ayeni, O. O., Al Hamad, N. M., Chisom, O. N., Osawaru, B., & Adewusi, O. E. (2024). AI in Education:A Review of Personalized Learning and Educational Technology. GSC Advanced Research and Reviews, 18(2), 261–271.

Basri, W. S. (2024). Effectiveness of AI-powered Tutoring Systems in Enhancing Learning Outcomes. Eurasian Journal of Educational Research, 110(110), 33-52.

Cinar, O. E., Rafferty, K., Cutting, D., & Wang, H. (2024). AI-powered VR for Enhanced Learning Compared to Traditional Methods. Electronics, 13(23), 4787.

Dag, K., Çavusoglu, S., & Durmaz, Y. (2024). The Effect of Immersive Experience, User Engagement and Perceived Authenticity on Place Satisfaction in the Context of Augmented Reality. Library Hi Tech, 42(4), 1331-1346.

El-Sabagh, H. A. (2021). Adaptive e-learning Environment based on Learning Styles and its Impact on Development Students' Engagement. International Journal of Educational Technology in Higher Education, 18(1), 53.

Huang, S.-P. (2018). Effects of Using Artificial Intelligence Teaching System for Environmental Education on Environmental Knowledge and Attitude. EURASIA Journal of Mathematics, Science and Technology Education, 14(7), 3277-3284. https://doi.org/10.29333/ejmste/91248

Imamguluyev, R., Hasanova, P., Imanova, T., Mammadova, A., Hajizada, S., & Samadova, Z. (2024). Ai-powered Educational Tools: Transforming Learning in the Digital era. International Research Journal of Modernization in Engineering Technology and Science, 6, 920-929.

Khan, S., & Javed, M. (2025). AI-Powered Chatbots in Education: Evaluating Their Effectiveness in Student Engagement and Learning Outcomes. ACADEMIA Tech Frontiers Journal, 1(1), 11-21.

Qazi, A., Hasan, N., Abayomi-Alli, O., Hardaker, G., Scherer, R., Sarker, Y., & Maitama, J. Z. (2022). Gender Differences in Information and Communication Technology use & skills:a Systematic Review and Meta-analysis. Education and information technologies, 27(3), 4225-4258.

Verawati, N. N. S. P., Rokhmat, J., Sukarso, A. A., Harjono, A., & Makhrus, M. (2024). Utilizing Interactive Labs Technology Resources in Science Learning:A Literature Review. International Journal of Essential Competencies in Education, 3(1), 47-76.

Yu, C., & Yao, G. (2024). Enhancing Student Engagement with Ai-driven Personalized Learning Systems. International Transactions on Education Technology (ITEE), 3(1), 1-8.

Zerovnik, A. (2024). Technology-enhanced Feedback System Usability in the Context of Self-Regulation Promotion. Education Sciences, 14(9), 948.