EFFECT OF AI-DRIVEN ADAPTIVE LEARNING PLATFORM ON SECONDARY SCHOOL STUDENTS’ RETENTION IN CHEMISTRY IN ANAMBRA STATE

Okoli, Leonard Maduabuchi; Obiageli Ifeoma Ikwuka

doi:10.5281/zenodo.23159960

Okoli, Leonard Maduabuchi Department of Educational Foundations, Faculty of Education, Nnamdi Azikiwe University, Awka,
Obiageli Ifeoma Ikwuka, Prof. Department of Educational Foundations, Faculty of Education, Nnamdi Azikiwe University, Awka,

DOI: https://doi.org/10.5281/zenodo.23159960

Keywords: Artificial Intelligence, Adaptive Learning, Chemistry Education, Delayed Retention, Secondary Education, Gender, Onitsha Education Zone.

Abstract

This study investigated the effect of an Artificial Intelligence-Driven Adaptive Learning Platform (AI-DALP) on secondary school students’ retention in Chemistry within the Onitsha Education Zone of Anambra State, Nigeria. The study specifically assessed the difference in delayed retention between students taught using AI-DALP and those taught using the Conventional Teaching Method (CTM) after controlling for immediate posttest performance, as well as whether retention differed by gender within the adaptive learning environment. Guided by two research questions and two null hypotheses tested at the 0.05 level of significance, the study adopted a quasi-experimental non-randomised pretest–posttest control-group design. The population comprised 548 Senior Secondary School II (SS2) Chemistry students across 22 public co-educational schools. Through purposive sampling based on ICT infrastructure availability, a sample of 59 students (30 in the AI-DALP group and 29 in the CTM group) was selected. The intervention lasted six weeks, covering Oxygen, Hydrogen, and Halogens, followed by a delayed retention test administered two weeks after the posttest. Data were collected using a 50-item Chemistry Achievement Test (CAT), validated by three experts, with an internal consistency reliability coefficient of 0.82 established using the Kuder–Richardson 20 (KR-20) formula. Data were analyzed using mean, standard deviation, and Analysis of Covariance (ANCOVA) with posttest scores as the covariate. The results indicated that students exposed to AI-DALP achieved significantly higher adjusted retention mean scores than those taught via CTM after controlling for posttest performance. Additionally, no statistically significant difference was observed in retention scores between male and female students taught using AI-DALP after accounting for posttest performance. The study concluded that AI-DALP effectively enhances durable Chemistry learning among secondary school students regardless of gender.

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