Beyond AI Acceptance: Development and Validation of the Generative Cognitive Resonance Scale (GCR-12)
DOI:
https://doi.org/10.65405/b1npg933Keywords:
Generative Cognitive Resonance; Generative AI; Bifactor Modeling; ESEM; Distributed Cognition; Construct ValidityAbstract
Moving beyond traditional technology acceptance models, this study investigates the internal cognitive processes occurring during students' engagement with Generative AI (GenAI) tools. We introduce Generative Cognitive Resonance (GCR) as a mediating construct that captures the iterative loop of verification, revision, and adaptation to inherently unstable generative outputs. An explanatory sequential mixed-methods design was employed, incorporating a primary sample (N=312), an external validation cohort (N=120), an experimental component (N=60), and in-depth qualitative interviews (n=15). The GCR-12 scale underwent rigorous psychometric evaluation using Exploratory Structural Equation Modeling (ESEM) and Bifactor modeling. Results demonstrated acceptable model fit (CFI=0.924, RMSEA=0.073), with a robust general factor (ωh=0.63) supporting total score interpretation. GCR accounted for significant incremental variance in academic essay quality (ΔR²=0.12, p<.001) after controlling for self-regulated learning and metacognition. Theoretical and practical implications for integrating GCR within Distributed Cognition frameworks in higher education are discussed.
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