EEFFECT OF AI-DRIVEN ADAPTIVE LEARNING PLATFORM ON SECONDARY SCHOOL STUDENTS’ ACADEMIC ACHIEVEMENT IN CHEMISTRY IN ANAMBRA STATE, NIGERIA

Abstract

Learning Chemistry at the secondary-school level requires students to understand, construct, connect, and apply abstract concepts. Yet, conventional instruction often provides relatively uniform learning experiences without taking differences in learners’ prior knowledge and learning readiness into account. To ameliorate the latter, artificial intelligence adaptive learning platforms offer a potential means of individualizing instruction and providing responsive feedback to students. The study investigated the effect of an AI-driven adaptive learning platform on secondary school students’ academic achievement in Chemistry in Onitsha Education Zone of Anambra State, Nigeria. Two research questions guided the study and two corresponding null hypotheses were tested at 0.05 level of significance. A quasi-experimental pre-test, post-test, non-randomized control group research design was adopted using intact classes. The study population comprised 548 SS 2 Chemistry students from the 22 co-educational public secondary schools in Onitsha Education Zone. The sample size comprised 61 Chemistry students (28 males and 33 females) from two co-educational schools. The schools were drawn from the 22 co-educational public secondary schools in Onitsha Education Zone using a purposive sampling technique. Data were collected using a 50-item Chemistry Achievement Test (CAT) validated by three experts. The reliability of the instrument was established using Kuder–Richardson KR-20, which yielded a coefficient value of 0.82. Research questions were answered using mean and standard deviation, while Analysis of Covariance (ANCOVA) was used to test the hypotheses at 0.05 level of significance. Findings showed that students taught Chemistry using AI-DALP achieved significantly higher scores than students taught using conventional teaching method (CTM), and that there is no significant difference between the achievement scores of male and female students, thereby making AI-DALP gender friendly. The study concludes that AI-DALP is effective for improving achievement in Chemistry among students and recommends that the Anambra State Ministry of Education and school administrators should provide the appropriate computer infrastructure and provide training seminars to equip students with the digital skills needed for using AI-DALP in learning to improve achievement in Chemistry.