Implicit Expectations and Cognitive Construction: Dual Pathways Shaping Graduate Students’ Sustained Engagement With Generative AI

This study addresses the gap in understanding graduate students’ sustained engagement behavior (SEB) with generative artificial intelligence (GAI) by integrating the Technology Acceptance Model (TAM), Expectation Confirmation Theory (ECT), and Theory of Reasoned Action (TRA) into a comprehensive embedding model. It introduces the Technology Readiness Index for Innovation (TRII) and Perception-Oriented Learning Style (POLS) as key factors, analyzed through Structural Equation Modeling (SEM) and Qualitative Comparative Analysis (QCA). Data from 862 graduate students in China were tested for reliability and validity. SEM results demonstrated that TRII significantly influences usage expectations (UE), effort expectancy (EE), performance expectancy (PE), and SEB, with cognitive and affective factors mediating these relationships. QCA revealed multiple causal pathways leading to high SEB, highlighting the principle of equifinality. The integration of SEM and QCA provided insights into dual pathways—implicit expectation development and cognitive system processing—that shape GAI adoption, offering practical implications for effective implementation in higher education.