Implicit Expectations and Cognitive Construction: Dual Pathways Shaping Graduate Students’ Sustained Engagement With Generative AI DOI
Hongfeng Zhang, Fanbo Li, Xiaolong Chen

et al.

Journal of Educational Computing Research, Journal Year: 2025, Volume and Issue: unknown

Published: April 17, 2025

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

Language: Английский

Implicit Expectations and Cognitive Construction: Dual Pathways Shaping Graduate Students’ Sustained Engagement With Generative AI DOI
Hongfeng Zhang, Fanbo Li, Xiaolong Chen

et al.

Journal of Educational Computing Research, Journal Year: 2025, Volume and Issue: unknown

Published: April 17, 2025

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

Language: Английский

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