Generative AI, Bloom’s Higher-Order Thinking Skills, and Academic Achievement: The Mediating Role of Student Engagement in Higher Education

Abstract

The rapid integration of generative artificial intelligence (GenAI) in higher education has recontextualized cognitive development, particularly regarding Bloom’s higher-order thinking skills (HOTS). While GenAI's potential to enhance learning is widely recognized, the mechanisms through which GenAI-assisted HOTS influence academic achievement remain underexplored. This study addresses this gap by examining the mediating role of multidimensional student engagement (behavioral, emotional, and cognitive) in the relationship between GenAI-supported HOTS and academic achievement. Employing a quantitative, cross-sectional design, data were collected from 504 higher education students in Islamabad. Structural equation modeling (SEM) and bootstrap mediation analyses were utilized to test the hypothesized framework. Results indicated that GenAI-assisted HOTS significantly predicted both student engagement (β = .62, p < .001) and academic achievement (β = .28, p < .001). Crucially, student engagement partially mediated the relationship between GenAI-HOTS and academic achievement, accounting for 50.9% of the total effect (β_indirect = .29, p < .001), with cognitive engagement emerging as the strongest mediating pathway. Multi-group analysis confirmed the structural invariance of this model across academic levels and disciplines. These findings demonstrate that GenAI's impact on academic achievement is significantly channeled through enhanced student engagement rather than occurring solely through direct cognitive offloading. The study provides empirical evidence for shifting pedagogical strategies from passive AI consumption to structured, engagement-driven AI integration that explicitly targets higher-order cognitive processes.