Does AI enhance or replace student effort? Examining AI competence, productivity, and cognitive engagement in relation to academic outcomes and SDG 4
Bijin Philip, Nia JoshyPurpose
This study aims to examine how artificial intelligence (AI) competence is associated with university students’ academic productivity, cognitive engagement and academic outcomes within the context of Sustainable Development Goal (SDG) 4 (Quality Education). It investigates whether AI functions primarily as a learning enhancer that supports student effort or as a substitute that may reshape engagement patterns in higher education.
Design/methodology/approach
This study surveyed 412 Indian university students to examine how AI impacts overall academic outcomes. A multi-stage purposive and convenience sampling strategy was adopted. Structural equation modelling with bootstrapping was employed to test direct and indirect associations, while confirmatory factor analysis assessed measurement reliability and validity.
Findings
AI competence is positively associated with academic productivity (ß = 0.52, p < 0.001) and cognitive engagement (ß = 0.47, p < 0.001). Both productivity (ß = 0.45, p < 0.001) and engagement (ß = 0.39, p < 0.001) are significantly related to academic outcomes. The model explains 48% of the variance in academic outcomes (R2 = 0.48). Bootstrapped mediation analysis shows significant indirect effects through productivity and engagement. These findings suggest that AI competence influences academic outcomes primarily through productivity and engagement pathways.
Practical implications
The results highlight the importance of developing AI competence as part of sustainable digital literacy initiatives aligned with SDG 4. Institutions should combine AI literacy training, equitable access policies, and pedagogical strategies that encourage reflective and effortful learning to promote inclusive and quality-focused educational outcomes.
Originality/value
This study contributes to the emerging discourse on sustainable digital transformation in higher education by empirically modelling how AI competence relates to academic outcomes through productivity and engagement mechanisms. It offers a structured framework for integrating AI into higher education in ways that align with equitable, quality-focused and sustainable learning systems.