Effect of Artificial Intelligence in Cooperative Learning on Biology Students’ Performance in Rivers State, Nigeria

Abstract

This study investigated the effect of artificial intelligence (AI)-supported cooperative learning on the academic performance of secondary school Biology students in Port Harcourt Local Government Area, Rivers State, Nigeria. A quasi-experimental pretest–posttest design was adopted. The study involved 92 Biology students drawn from two purposively selected co-educational public secondary schools. Students in the experimental group were taught using an AI-supported cooperative learning strategy, specifically the think-pair-share approach, while those in the control group were taught using the conventional lecture method. Data were collected using a Biology Performance Test (BPT), which was validated by two experts and yielded a reliability coefficient of 0.76 using the Kuder–Richardson 20 (KR-20) procedure. Two research questions and two null hypotheses guided the study. Data were analysed using mean, standard deviation, and analysis of covariance (ANCOVA) at the 0.05 level of significance. The findings indicated a statistically significant effect of AI-supported cooperative learning on students’ academic performance in Biology compared with the lecture method (F = 11.530, p < .05, partial η² = .089). Although female students recorded a slightly higher mean gain than male students under the AI-supported cooperative learning strategy, the analysis revealed no statistically significant difference in performance attributable to gender (p > .05). The study concludes that AI-supported cooperative learning can enhance students’ academic performance in Biology and provides a learner-centred approach that promotes active participation and collaborative learning. Biology teachers are therefore encouraged to integrate appropriately designed AI-supported cooperative learning activities into classroom instruction.