Impact of Artificial Intelligent-Tutor Individualized Learning on Students’ Cognitive Load Management in Integrated Science Education in Northeast Nigeria

Emerging technologies such as Artificial Intelligence (AI) tools have been proved to influence learning experience and engagement significantly. However, its complete potential in enhancing science education in the Northeast, Nigeria, remains largely unexplored. This study addresses this gap by investigating AI-tutor-based individualised learning and its impact on students’ cognitive management. The study adopted mix method research approach design. A quasi experimental-control group design with intact class involving pretest, post-test with one experimental group and one control group and qualitative-interpretive research approach design. 55 undergraduate 300 level students that registered for the biology course titled 'General Biology for Integrated Science II in the integrated science education programme were purposely selected for the study from the two federal universities that run integrated science education programmes in the Northeast, Nigeria. Ten integrated education course lecturers also participated in the study, 5 from each of the two universities, and they serve as research assistants. The Students’ Cognitive Load Management Questionnaire (SCLMQ) was developed by the researchers and was validated by peer experts to collect information on students’ cognitive load. The instrument was subjected to a reliability test using the Cronbach alpha statistical tool, yielding a reliability coefficient of 0.894. Descriptive statistics such as mean rank, range, sum of ranks and median were used to test research questions. While the Kruskal-Wallis statistical test was employed to evaluate significant differences in gender-based cognitive load among students in selected concepts and the Mann-Whitney test to measure significant differences between AI-tutor individualised learning and the control groups. The findings indicated a significant difference in students’ cognitive load between the control and experimental groups. Additionally, students’ cognitive load management was significantly impacted by gender. Consequently, the study recommended integrating AI-tutor-based individualised learning into integrated science education courses, among other suggestions.