AI-Powered Learning Tools on Measurement of Student Engagement Across Academic Disciplines: Implications of Age and gender

Abstract This study examined the effect of AI-powered learning tool metrics on student engagement and academic performance across various disciplines, age groups, and genders in higher education. Using a quantitative correlational and causal-comparative design, 760 undergraduate students from STEM and non-STEM disciplines were sampled via multi-stage sampling. Data were collected through AI-generated engagement metrics and a structured questionnaire that measured behavioural, cognitive, and emotional engagement, as well as self-reported academic performance. Analyses included one-way and two-way ANOVA and decision tree regression to examine differences and predictive relationships. Results from a one-way ANOVA revealed significant differences in engagement across AI tool types (F(2,757)=588.24, p