Ai-based Intelligent Tutoring Systems for Facilitating the Mastery of Difficult Biology Concepts among Special Educational Needs Learners

Abstract

This study probed the potentials of AI-based Intelligent Tutoring Systems (AI-ITS) in facilitating the mastery of difficult Biology concepts among special educational needs secondary school two (SS2) students. It was guided by three research questions and one hypothesis. A quasi-experimental (non-equivalent pretest–posttest control group) design, augmented with a survey approach was utilized. The population comprised all SS2 learners offering Biology in government secondary schools in Delta state, from which a sample of 240 students was selected using multistage sampling techniques. Two instruments ‘Biology Achievement Test (BAT) and Questionnaire on AI-Based Intelligent Tutoring Systems (QAITS) were utilized for data gathering. They were validated by experts, while reliability coefficients of 0.82 and 0.86 were got for BAT and QAITS utilizing KR-20 and Cronbach Alpha respectively. Data were analyzed using mean, standard deviation, and Analysis of Covariance (ANCOVA). Findings suggest a statistically significant difference in favour of AI-ITS over conventional methods with convincing effect size (η² = .668). This implies that AI-ITS can enhance learners’ learning outcome. Secondly, the potentials and challenges of AI-ITS in enhancing participants understanding of difficult Biology concepts were acknowledged. Hence, adequate infrastructure, in-service training for teachers, and policy rectification is recommended to facilitate AI adoption/implemented in Biology instruction.