Impact of Generative-Artificial-Intelligence Strategy on Retention and Performance in Biology among Federal University of Education Zaria Students, Kaduna state, Nigeria
DOI:
https://doi.org/10.64348/zije.202536Keywords:
Generative Artificial Intelligence, Biology, Retention, Performance, GenderAbstract
This study determines the Impact of Generative Artificial Intelligence (GAI) on Academic Performance Among Undergraduate Students in Biology Concept at Federal University of Education, Zaria, Nigeria. The study employed a quasi-experimental, non-randomized pre-test, post-test, post posttest, control group design involving 205 Undergraduate Biology Students from Federal University of Education, Zaria. The experimental group engaged in Generative Artificial Intelligence (GAI) Strategy for six weeks, while the control group received traditional classroom instruction. Data were collected using the instrument (Fixation Concept Performance Test FCPT), a 30-item multiple-choice assessment, and analyzed using mean, standard deviation, and t-test. Results indicated that using Generative Artificial Intelligence (GAI) strategy significantly improved students' Academic Performance. The experimental group achieved a higher mean post-test score (M = 28.36) compared to the control group (M = 18.99), with a statistically significant effect (p < 0.05) which is line with the study of Ransome., Fati & Okoli (2025). In the aspect of gender differences male students performed slightly higher than females, with no statistical difference. Suggesting that the treatment is gender friendly. The study recommends that the institution should formally integrate GAI applications such as ChatGPT, DALL·E, and Codex into biology instruction. These tools can simplify complex biological topics by offering dynamic explanations, creating visual content, and simulating biological processes, among others.
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