AI Stimulations In Teaching Cell Division: A Case Study From Hassan Usman Katsina Polytechnic

This study tends to investigate how AI-powered stimulated tools enhanced students' understanding of cell division. Using a quasi-experimental design of pre-test/post-test experimental/control groups. The study utilizes two (2) objectives, research questions, and a null hypothesis. Forty (40) students from Microbiology unit, Department of Basic and Applied Sciences at Hassan Usman Katsina Polytechnic, Katsina State, Nigeria formed the population. Using simple random sampling by balloting, the participants were equally selected to form (n=20) each. Data was collected through a pre-test and post-test approach. Both groups underwent a pre-test using a researchermade validated five simple questions (two-stage diagnostic test on cell division). The experimental group was then exposed to AI-stimulated learning, while the control group received conventional lecture method for a period of four (4) weeks. Data was then analyzed using Independent samples t-test and one-way ANOVA. The results showed a significant difference in post-test scores between the experimental and control groups, with a mean difference of 0.882 and statistical significance at p ≤ 0.05. Findings of the study revealed that students who used AI-powered simulation tools showed higher post-test scores in understanding cell division compared to those who received traditional lectures. The study recommends integrating AI-powered technology-enhanced tools into biology practical aspects in science curricula to promote teaching and learning processes.