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Ecology is an important component of secondary school biology because it provides learners with knowledge for understanding organisms, ecosystems, environmental interactions, and sustainability. However, ecology can involve complex and interrelated concepts that require learners to integrate multiple relationships and processes. This study investigated the effect of artificial intelligence (AI)-powered personalized learning on students’ achievement and retention at secondary school-level ecology in Ugba Town, Logo Local Government Area of Benue State, Nigeria. The study adopted a non-randomized pretest-posttest control-group quasi experimental design. The population comprised SS II Biology students in the selected secondary schools, while a sample of 40 students from two intact classes was used, with 20 students assigned to the AI-powered personalized learning group and 20 to the conventional-method group. Data were collected using the Ecology Achievement Test (EAT) and Ecology Retention Test (ERT). Mean and standard deviation were used to answer the research questions, while independent-samples t tests were used to test the null hypotheses at the 0.05 significance level. The achievement result showed no statistically significant difference between the AI-powered personalized learning group (M = 13.85, SD = 3.93) and the conventional group (M = 12.25, SD = 3.30), t(38) = 1.39, p = 0.175, 95% CI [−0.75, 3.95]. In contrast, the retention result showed a statistically significant difference, with the AI group (M = 16.45, SD = 2.58) outperforming the conventional group (M = 11.55, SD = 3.47), t(38) = 5.06, p =0.000, 95% CI [2.94, 6.86]. The study concludes that AI-powered personalized learning may be particularly valuable for strengthening the retention of difficult ecological concepts, although the evidence from this study does not demonstrate a statistically significant advantage for immediate achievement.
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Aondoaseer Kundushima Daniel (2026) studied this question.
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