Integrating Artificial Intelligence in Enhancing Teaching and Learning Strategies in Junior Secondary Schools in Gumel, Jigawa State, Nigeria

Abstract

This study investigated the integration of Artificial Intelligence (AI) in enhancing teaching and learning of Basic Science among Junior Secondary School students in Gumel Local Government Area, Jigawa State, Nigeria. A convergent parallel mixed-methods design incorporating a quasi-experimental pre-test post-test non-equivalent control group was adopted. The study involved two intact classes from two public junior secondary schools comprising 166 students and 6 Basic Science teachers selected through purposive and simple random sampling techniques. One school served as the experimental group and received AI-supported instruction, while the other served as the control group and was taught using conventional methods. Data were collected using the Student Achievement Test (SAT), Artificial Intelligence Awareness and Perception Questionnaire (AIAPQ), classroom observations and semi-structured interviews. The reliability coefficient for AIAPQ and SAT are 0.83 and 0.714 respectively. Quantitative data were analysed using descriptive statistics and independent and paired samples t-tests, while qualitative data were analysed using thematic analysis. The findings of the study revealed that students exposed to AI-supported instruction achieved significantly higher post-test scores than those taught using conventional methods. Students also demonstrated positive perceptions of AI-supported learning, while qualitative findings showed improved conceptual understanding, motivation, classroom participation and personalized learning.