AccScience Publishing / IJOSI / Online First / DOI: 10.6977/IJoSI.202611_10(X).026360312
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Generative AI in Vietnamese undergraduates: Cognitive offloading and AI dependency in critical thinking disposition

Nghia Phan Tri1*
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1 Office of the Institute, Institute of Digital Technology, Thu Dau Mot University, Ho Chi Minh City , Vietnam
Received: 5 September 2026 | Revised: 7 October 2026 | Accepted: 23 October 2026 | Published online: 12 November 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract

Generative artificial intelligence (AI) tools are now commonly used in undergraduate academic work, and a growing body of evidence associates their use with lower self-reported critical thinking. This study examines whether that association runs through one cognitive mechanism or two with different origins, modeling cognitive offloading and AI dependency as parallel mediators. Data were collected from a single cross-sectional survey of a convenience sample of 448 undergraduates at five institutions in the northern area of Ho Chi Minh City, conducted when none of the institutions had issued their own rules on generative AI use. Analyses used partial least squares structural equation modeling with 10,000 bootstrap samples, two one-sided equivalence tests, and a specification test against a linear benchmark. Critical thinking was measured as a self-reported disposition, not as ability on a performance test. Both mediators are on average associated with lower critical thinking disposition to a comparable degree (−0.277 and −0.240), but their origins differ. Use frequency transmits 95% of its indirect effect through offloading and shows no substantive direct association with dependency once the remaining antecedents are controlled, whereas trust in AI transmits only 7% through offloading (descriptive point-estimate shares without intervals). The second-order contrast between the two antecedents is −0.126 (95% confidence interval [−0.191, −0.070]), establishing a double dissociation. Because the two mechanisms are associated with the outcome to a comparable degree but have different origins, intervention implications cannot be read off effect magnitude: on this reading, interventions that act on the level of use would reach only one channel. The two no-association conclusions (trust to offloading, use frequency to dependency) rest on equivalence tests with power of 0.67 and 0.69 at n = 448 and need replication with n > 600.

Keywords
Cognitive offloading
Artificial intelligence dependency
Critical thinking disposition
Generative artificial intelligence in higher education
Equivalence testing
Partial least squares structural equation modeling
Funding
This research received no specific grant from any funding agency.
Conflict of interest
The author declares no competing interests, financial or non-financial, including potential ones. The study received no specific grant, and the author has no financial or personal relationship with the developers of the AI tools discussed. The use of an AI assistant for language checking is disclosed in the “Further disclosure” section.
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