Primary Education Undergraduates’ Competency in the Use of Artificial Intelligence for Learning in Kwara State
Keywords:
Artificial intelligence, Gender, Primary education, Undergraduate, University proprietorshipAbstract
The goal of this study is to bridge the gap created by classroom learning by incorporating the power of current technological innovation, especially the use of Artificial Intelligence (AI) among primary education undergraduates.
This study presumed that to make a successful transition to knowledge-based education, the introduction of AI would be a necessary tool to shift Nigerian education from analog to
digital. Hence, this study investigated the primary education student competency in the use of AI for learning. This study adopted descriptive research of the survey type. The findings of the study were that majority of the primary education undergraduates are not skilled and incompetent in the use
of AI for learning. There was no significant difference between male and female primary education
undergraduate’s competency in the use of AI for learning. There was no significant difference in primary education undergraduate’s competency in the use of AI for learning based on university proprietorship. Students' ability to
explore digital resources such as AI depends on their access to digital technologies. AI technologies should be made available, open, and accessible to primary education
undergraduates, to encourage them to utilize the technologies for learning to improve learners’ capability towards achieving better academic performance.
References
Abbas, P. G. (2017). A review of the literature on the integration of technology into the
learning and teaching of English language skills. International Journal of English
Linguistics, 7(5), 95-106.
Adeoye, A. A., and Adeoye, B. J. (2017). Digital literacy skills of undergraduate students in
Nigeria universities. Library Philosophy and Practice (e-journal), 1, 1-23.
Babatunde, A. A. (2020). The state of primary education in Nigeria: a critical review. South
Asian Research Journal of Arts, Language, and Literature, 2(3), 47-52.
Caplar, N., Tacchella, S., and Birrer, S. (2017). Quantitative evaluation of gender bias in
astronomical publications from citation counts. Nature Astronomy, 1(6), 1-5.
Elsayed, N., Thomas, B., Marriott, K., Piantadosi, J., and Smith, R. (2015). Situated analytics.
In 2015 Big Data Visual Analytics, 29, 1-8.
Olaniyan, D. A., and Obadara, O. E. (2008). A critical review of management of primary
education in Nigeria. International Journal of Africa and African American Studies, 7(1),
-20.
Popenici, S. A., and Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching
and learning in higher education. Research and Practice in Technology Enhanced
Learning, 12(1), 1-13.
Schölkopf, B. (2015). A review on application of artificial intelligence inteaching and learning in educational contexts. International Journal of Learning and Development, 8(4), 106-118
