Research on the application behavior of generative artificial intelligence learning of college students based on self-determination theory
Year: 2026
Type: article
Study design: Observational Study
Abstract: Introduction: In recent years, the application of generative artificial intelligence (GAI) in higher education has gained increasing prevalence, accompanied by a concerning rise in student "over-reliance". Such excessive dependence can undermine critical thinking and hinder innovation development. Consequently, guiding college students toward a "deep processing" mode of GAI use has become a crucial issue in higher education. Methods: Grounded in self-determination theory, this study constructs a model to examine the factors influencing college students' GAI usage behaviors. To test the proposed model and its hypothesized relationships, An empirical study has been conducted,including survey instrument distribution and path analysis. Results: The main findings emerge: First, among basic psychological needs, perceived competence plays a pivotal role in curbing over-reliance and promoting deep processing, exerting a stronger influence than perceived autonomy or relatedness. Second, all three basic psychological needs (perceived autonomy, perceived competence, and perceived relatedness) indirectly influence deep processing and over-reliance through intrinsic motivation. Discussion: The hypotheses proposed in the article has been verified. Based on these findings, practical suggestions are proposed across multiple levels to foster more reflective and self-determined GAI use, including constructing a need-supportive Learning Ecosystem, building a "guided" GAI tool, establishing a hierarchical support and constraint framework, fostering a shift from instrumental to alue rationality.
Source: Frontiers in Psychology
Authors: Xu Fang, Junci Feng
Institution: Nantong University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7147740441