The mechanisms of AI acceptance drivers on learning outcomes in higher education
Bibliographic Data
| ID | 22437317 |
|---|---|
| Authors | Yubin Xu (0000-0003-0340-4425, Guangdong University of Education), Shuxian Chen (Guangdong University of Education, corresponding author), Zhuoran Jiang (0000-0003-4734-5535, Guangdong University of Education) |
| Year | 2026 |
| Volume | 5 |
| Issue | 1 |
| Publication date | 2026-05-04 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Discover Education (JOURNAL) |
| Journal identifiers | ISSN: 2731-5525 • E-ISSN: 2731-5525 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s44217-026-01553-3 |
| OpenAlex | W7160081012 |
| Language | EN |
| References cited | 47 |
In the context of the increasing integration of artificial intelligence (AI) within higher education and its significant influence on educational paradigms, there has been a notable rise in the acceptance of AI among university students in China. Nevertheless, there is a lack of empirical research concerning the specific impacts of AI on learning processes and outcomes. This study aims to address the critical research gap related to “post-adoption outcomes” in educational technology by utilizing an extended version of the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The investigation focuses on how various drivers of AI acceptance influence learning satisfaction, motivation, and capability. Through the analysis of 522 survey responses using Partial Least Squares Structural Equation Modeling (PLS-SEM), the study uncovers several intricate mechanisms: (1) Performance expectancy, social influence, and facilitating conditions significantly contribute to enhanced learning satisfaction; (2) Effort expectancy, social influence, facilitating conditions, and learning satisfaction collectively bolster learning motivation; (3) Performance expectancy, social influence, facilitating conditions, and learning satisfaction directly promote the capability, with facilitating conditions demonstrating the most substantial effect. Importantly, (4) neither effort expectancy nor learning motivation shows a significant direct impact on learning capability. These findings offer a solid empirical foundation for educators seeking to optimize AI-driven pedagogy, for developers aiming to create effective learning tools, and for policymakers tasked with establishing evidence-based evaluation frameworks.
Higher education · Qualitative research · AI in Service Interactions · Artificial Intelligence in Healthcare and Education · Online Learning and Analytics
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| Highly cited | No |
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