Impact of Meta Artificial Intelligence on Chemical Literacy of Undergraduate Chemistry Students in Sokoto State University, Sokoto
*1Bilal Abdullahi Usman, 2Faruku Aliyu, and 3Muhammad Nasiru Hassan
*1&2Department of Science Education, Faculty of Education, Sokoto State University. Email: bilalabdullahiusman@gmail.com*1, faruku.aliyu@ssu.edu.ng2, nashas4joy@gmail.com3
Abstract
This study investigated the impact of Meta Artificial Intelligence on the chemical literacy of undergraduate chemistry students at Sokoto State University. The study was guided by three research objectives, three research questions, and three null hypotheses on the chemical literacy of students. The study used a quasi-experimental design with pre- and post-test control and experimental group structure and had a population of 169 undergraduate chemistry students, out of which 118 were purposively sampled. Data was collected using a validated instrument (chemical literacy test CLT) with a reliability coefficient of 0.77. The collected data was analyzed using both descriptive statistics, including mean, mean difference, and standard deviation, and inferential statistics, including paired sample t-tests and independent sample t-tests. The findings of the research showed that Meta AI has a positive impact on students’ chemical literacy with comparable outcomes across gender. These findings emphasised the need for integrating Meta Artificial Intelligence technology into chemistry education to improve student chemical literacy.
Keywords
Science Literacy, Chemical Literacy, Meta Artificial Intelligence
Reference
Avargil, S. (2019). Learning chemistry: Selfefficacy, chemical understanding, and graphing skills. Journal of Science Education and Technology, 28(4), 285298.
Cigdemoglu, C., & Geban, O. (2015). Improving students’ chemical literacy levels on thermochemical and thermodynamics concepts through a context-based approach. Chemistry Education Research and Practice, 16(2), 302-317.
Cooper, M. M., & Stowe, R. L. (2018). Chemistry education research—From personal empiricism to evidence, theory, and informed practice. Chemical reviews, 118(12), 60536087.
Dibner, K. A., & Snow, C. E. (Eds.). (2016). Science literacy: Concepts, contexts, and consequences.
Dierking, R. (2015). Using nooks to hook reluctant readers. Journal of Adolescent & Adult Literacy, 58(5), 407-416.
Fahmina, S. S., Masykuri, M., Ramadhani, D. G., & Yamtinah, S. (2019, December). Content validity uses Rasch model on computerized testlet instrument to measure chemical literacy capabilities. In AIP Conference Proceedings (Vol. 2194, No. 1). AIP Publishing.
Feinberg, M. (2019). Foundations of chemical reaction network theory.
Gombe, S. K., Turiman, S., Ismi Arif, I., & Zohara, O. (2015). Empowering youth through volunteerism: The importance of global motivating factors. IOSR J. Humanit. Soc. Sci.(IOSRJHSS), 20(11), 35-39.
Jegstad, K. M., & Sinnes, A. T. (2015). Chemistry teaching for the future: A model for secondary chemistry education for sustainable development. International Journal of Science Education, 37(4), 655683.
Posselt, J. R. (2016). Inside graduate admissions: Merit, diversity, and faculty gatekeeping. Harvard University Press.
Pross, A. (2016). What is life?: How chemistry becomes biology. Oxford University Press.
Rahayu, P. P., & Masykuri, M. (2018, April). Analysis on the science literacy ability of vocational school physics teacher using NOSLiT indicators. In Journal of Physics: Conference Series (Vol. 1006, No. 1, p. 012030). IOP Publishing.
Sharon, A. J., & Baram‐Tsabari, A. (2020). Can science literacy help individuals identify misinformation in everyday life?. Science Education, 104(5), 873894.
T Ababio, B. (2013). Nature of teaching: What teachers need to know and do.
Yustin, D. L., & Wiyarsi, A. (2019, December). Students’ chemical literacy: A study in chemical bonding. In Journal of Physics: Conference Series (Vol. 1397, No. 1, p. 012036). IOP Publishing.