Artificial Intelligence and Learning: Exploring its Potential among Biology Education Students in Nigerian Colleges of Education

Saadiya Ibrahim, Najmuddeen Alhassan, Abubakar Ibrahim

Abstract


AI has appeared as a groundbreaking power in science education worldwide, making adaptive learning, intelligent tutoring and data driven decision making. Nevertheless, its consolidation into life science education especially in higher institutions remains underexplored. This paper examined the Artificial Intelligence and Learning: Exploring its Potential among Biology Education Students in Nigerian Colleges of Education. AI's Potential to Enhance Learning among Students of Biology in Education in Nigerian College of Education. The study outlines five aims, poses five research questions and test five hypotheses. A quantitative survey was explored. The target population consisted of 1,568 Biology students, from which a sample of 306 was randomly selected using Krejcie and Morgan’s formula. Data were collected using a researcher-developed structured questionnaire (QAITEBIOS). The instrument was validated by subject experts from biology department, Federal University of Education, Zaria and tested for reliability (r = 0.78). Descriptive statistics and independent t-test were employed using SPSS version 23 at 0.05 level of significance. Findings revealed significant adoption of AI tools for biology learning (t= 28.595 < 0.05) and positive perception of their usefulness in enhancing content delivery and student engagement (t=22.123, p< 0.05). AI integration significantly improved students’ performance and motivation (t= 23.435, p< 0.05). However, infrastructural deficits including unreliable electricity, poor internet connectivity and limited devices were major barriers to effective implementation (t=10.436, p< 0.05). Key recommendations include strengthen IT infrastructure (stable electricity, high-speed internet, cloud platforms) and provide continuous training for Biology lecturers and students on AI literacy, ethics, and pedagogy.


Full Text:

PDF

References


Al Darayseh, A. S. (2023). Acceptance of artificial intelligence in teaching science: Science teachers’ perspective. Computers and Education: Artificial Intelligence, 4(100132), 100132. https://doi.org/10.1016/j.caeai.2023.100132

Bali, B., Garba, E. J., Ahmadu, A. S., Takwate, K. T., & Malgwi, Y. M. (2024). Analysis of emerging trends in artificial intelligence for education in Nigeria. Discover Artificial Intelligence, 4(1). https://doi.org/10.1007/s44163-024-00163-y

Barde, A., Thakur, R., Patel, S., Sinah, N., & Barde, S. (2024). AI-Based Smart Education System to Enhanced the Learning of Students. 2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET), 1–7. https://doi.org/10.1109/acroset62108.2024.10743859

Eunkyung, H. (2024). AI-Based Edutech for Adaptive Teaching and Learning. In Artificial Intelligence. IntechOpen. https://doi.org/10.5772/intechopen.1004952

Festus, O., & Emmanuel, O. B. (2024). Sociocultural and digital communication challenges in AI adoption for classroom communication: Insights from Nigerian colleges of education. Language, Technology, and Social Media, 3(1). https://doi.org/10.70211/ltsm.v3i1.115

Hakimi, M., & Shahidzay, A. K. (2024). Transforming Education with Artificial Intelligence: Potential and Obstacles in Developing Countries. https://doi.org/10.20944/preprints202407.2542.v1

Ioannou-Sougleridi, E., Kopsidas, S., Vavougios, D., Simos, C., Avramopoulos, A., & Kanapitsas, A. (2024). Revolutionizing Learning Management Systems: Architecture of an AI-Based LMS with Instructor-driven Personalized Content Generation. International Journal of Advanced Multidisciplinary Research and Studies, 4(4), 1222–1226. https://doi.org/10.62225/2583049x.2024.4.4.3169

Krejcie, R. V., & Morgan, D. W. (1970). Determining Sample Size for Research Activities. Educational and Psychological Measurement, 30(3), 607–610. https://doi.org/10.1177/001316447003000308

Lavin, A., Gilligan-Lee, C. M., Visnjic, A., Ganju, S., Newman, D., Ganguly, S., Lange, D., Baydin, A. G., Sharma, A., Gibson, A., Zheng, S., Xing, E. P., Mattmann, C., Parr, J., & Gal, Y. (2022). Technology readiness levels for machine learning systems. Nature Communications, 13(1), 6039. https://doi.org/10.1038/s41467-022-33128-9

Lestariningrum, A., Abu, Wanof, M. I., Pramono, S. A., & Syamsuri, S. (2024). The Impact of AI Use in Learning and Digital Material Accessibility on Students’ Academic Achievement through Technology Engagement as A Mediating Variable : The Perspective of Theory of Planned Behaviour and UTAUT Theory. Jurnal Kependidikan Jurnal Hasil Penelitian Dan Kajian Kepustakaan Di Bidang Pendidikan Pengajaran Dan Pembelajaran, 10(4), 1317–1317. https://doi.org/10.33394/jk.v10i4.12896

Ma’amor, H., Achim, N., Ahmad, N. L., Roszaman, N. S., Kamarul Anuar, N. N., Khairul Azwa, N. C. A., Abd Rahman, S. N., & Aqilah Hamjah, N. A. (2024). The Effect of Artificial Intelligence (AI) on Students’ Learning. Information Management and Business Review, 16(3S(I)a), 856–867. https://doi.org/10.22610/imbr.v16i3s(i)a.4178

Marrone, R., Zamecnik, A., Joksimovic, S., Johnson, J., & De Laat, M. (2024). Understanding Student Perceptions of Artificial Intelligence as a Teammate. Technology Knowledge and Learning. https://doi.org/10.1007/s10758-024-09780-z

Ngonso, B. F., Egielewa, P. E., Egenti, G., Uduehi, I., Sunny-Duke, F., Ukhurebor, K. E., Onwusinkwue, S., Odezuligbo, I., Abiodun, A. O., Talabi, A. A., Jokthan, G., Opateye, J., Nwankwo, U. C., Eneche, B. M., & Osemengbe, U. O. (2025). Influence of artificial intelligence on educational performance of Nigerian students in tertiary institutions in Nigeria. Journal of Infrastructure Policy and Development, 9(1), 9949–9949. https://doi.org/10.24294/jipd9949

Olaseni, V. M. (2024). Teachers’ Perception Towards Integration of Artificial Intelligence Tutoring-Based System in the School Curriculum: A Survey. E-Journal of Humanities, Arts and Social Sciences, 2242–2251. https://doi.org/10.38159/ehass.202451319

Onesi-Ozigagun, O., Ololade, J., Eyo-Udo, L., & Ogundipe, O. (2024). Revolutionising education through AI: A comprehensive review of enhancing learning experiences. International Journal of Applied Research in Social Sciences, 6(4), 589–607. https://doi.org/10.51594/ijarss.v6i4.1011

Singh, P. (2024). Artificial Intelligence and Student Engagement. In Advances in educational technologies and instructional design book series (pp. 201–232). IGI Global. https://doi.org/10.4018/979-8-3693-5633-3.ch008

Thota, R. C. (2021). AI driven infrastructure automation-enhancing cloud efficiency with MLOps and DevOps. Global Journal of Engineering and Technology Advances, 8(3), 101–108. https://doi.org/10.30574/gjeta.2021.8.3.0140

Wu, R. (2024). The Impact of Artificial Intelligence-Assisted Teaching on Teachers’ Instructional Development. Journal of Education, Humanities and Social Sciences, 45, 19–23. https://doi.org/10.54097/7821c148

Zhang, Z. (2024). Research on the impact of artificial intelligence on college students’ learning. Computer Life, 12(3), 23–25. https://doi.org/10.54097/0mwt0e03.


Refbacks

  • There are currently no refbacks.