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Future of Educational Science: Generative AI Use, Trust, and Cognitive Load as Predictors of Self-Perceived Academic
1The First Affiliated Hospital of Wenzhou Medical University.
Abstract:
This study investigates the complex relationships between generative AI usage, trust in AI, cognitive load, and academic performance among higher education students. Grounded in cognitive load theory and trust literature, the research examines how students' engagement with generative AI tools and their trust in these systems influence academic outcomes, with cognitive load as the mediating mechanism. A quantitative cross-sectional research design was employed with a stratified random sample of 390 higher education students. Data were collected using validated scales measuring generative AI usage trust in AI (human-like and functionality dimensions), cognitive load (intrinsic load, extraneous load, and self-perceived learning), and academic performance. Partial least squares structural equation modeling (PLS-SEM) with bootstrapping procedures (5,000 resamples) was used to test the hypothesized direct and mediating relationships. The results revealed that generative AI usage (β = 0.34, p < 0.001) and trust in AI (β = 0.28, p < 0.001) were significantly positively associated with cognitive load. Cognitive load was also positively associated with academic performance (β = 0.52, p < 0.001). Furthermore, cognitive load significantly mediates the relationships between generative AI usage and academic performance (β = 0.18, p < 0.001) and between trust in AI and academic performance (β = 0.15, p < 0.001). The model explained 45% of the variance in academic performance, and PLS Predict confirmed high predictive power. The findings extend cognitive load theory to AI-enhanced learning contexts and suggest that cognitive load may function as an important explanatory pathway linking AI-related factors to academic outcomes. Practically, the study underscores the importance of fostering AI literacy, designing learning environments that optimize cognitive load, and prioritizing functional reliability in AI tool development. This study provides empirical evidence that cognitive load serves as a significant mediating mechanism through which generative AI usage and trust in AI translate into academic performance.
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