Algorithmic Culture and AI-Augmented Learning: Examining Student Learning Quality through Cognitive Engagement and Perceived Learning Effectiveness

Abstract

In higher education, Artificial Intelligence has been widely adopted to improve learning environments and expand mediated academic practices. The present study examines the impact of algorithmic culture on learning quality with perceived learning efficiency and cognitive engagement as mediators. The study adopts a cross sectional quantitative approach using primary data collected from students of higher educational institutions across India. Exploratory factor analysis found a robust four-factor structure  and demonstrated satisfactory sampling adequacy and variance explanation. Structural equation modeling using PLS-SEM revealed that algorithmic culture significantly affects cognitive engagement, perceived learning effectiveness, and learning quality. The findings highlighted the significant impact of both mediators on student learning quality. There is substantial variance explained in the quality of learning within the model. The mediation was found partial in the mediation analysis, with significant indirect impact through perceived learning effectiveness and cognitive engagement. AI-based learning improves academic outcomes with dual perceptual and cognitive pathways.