IMPACT OF AI-POWERED LEARNING ON STUDENTS’ ACADEMIC ACHIEVEMENT IN PRIMARY SCHOOLS

Abstract

This study investigated the impact of Artificial Intelligence (AI)-powered learning on the academic
achievement of primary school students in Lagos State, Nigeria. The study adopted a quasiexperimental pre-test and post-test control group design to evaluate the effectiveness of AI-based
adaptive learning platforms in comparison with traditional teacher-centered instructional
methods. A sample of 100 pupils was purposively selected and equally assigned to an experimental
group, exposed to AI-powered learning tools, and a control group, taught using conventional
methods.
Data were collected using a validated 10-item academic achievement test and analyzed using
descriptive statistics and paired-sample t-tests at a 0.05 level of significance. The findings revealed
a statistically significant difference in academic performance between the two groups, with the
experimental group (M = 7.60, SD = 1.50) outperforming the control group (M = 6.20, SD =
1.70), with a strong effect size (Cohen’s d = 0.87). The results indicate that AI-powered learning
enhances student engagement, supports individualized instruction, and improves content mastery
by reducing extraneous cognitive load through personalized pacing, immediate feedback, and
interactive scaffolding.
The study concludes that AI-powered learning is a highly effective instructional strategy for
improving academic achievement in primary education and has the potential to transform
knowledge acquisition processes. It recommends that educational stakeholders prioritize the
integration of AI-based learning technologies, invest in teacher capacity development, and
strengthen digital infrastructure to support scalable and equitable implementation in Nigerian
schools.