Unpacking the cognitive and ethical pathways of generative AI tools in higher education: a PLS-SEM study of learning performance mediation and moderation effects

This study examines how Generative Artificial Intelligence Tools (GAIT) influence student learning performance (LP) through cognitive, affective and ethical pathways using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data were collected from 292 Indonesian university students through a structured questionnaire. The results show that GAIT has a direct positive effect on LP (β = 0.920, p < 0.001). Mediation analysis identifies AI Knowledge (AIK) as the most dominant mediator (β = 0.715, p < 0.001), followed by AI Perception (AIP), Creativity (CRE), Fairness & Ethics (FE) and Cognitive Offloading (CO). Furthermore, AIK significantly moderates the GAIT–LP relationship (β = 0.006, p = 0.048). The model demonstrates high predictive power (R2 = 0.604) and good model fit (Standardized Root Mean Square Residual (SRMR) = 0.068). These findings highlight the central role of AI literacy and ethical awareness in maximising the benefits of GAIT for learning. This study contributes theoretically by integrating cognitive, affective and normative dimensions into a unified model of GAIT adoption and offers practical implications for designing AI literacy and ethics-oriented curricula in higher education.