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AI-Supported Learning, AI Dependency, and Academic Stress as Predictors of Student Burnout |
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| รหัสดีโอไอ | |
| Creator | Mahatthakorn Plensamai |
| Title | AI-Supported Learning, AI Dependency, and Academic Stress as Predictors of Student Burnout |
| Publisher | Phetchaburi Rajabhat University |
| Publication Year | 2569 |
| Journal Title | Interdisciplinary Research Review (IRR) |
| Journal Vol. | 21 |
| Journal No. | 4 |
| Page no. | 44-56 |
| Keyword | AI-supported learning, AI dependency, academic stress, academic burnout, university students, generative artificial intelligence |
| URL Website | https://ph02.tci-thaijo.org/index.php/jtir |
| Website title | Interdisciplinary Research Review (IRR) |
| ISSN | 2697-536X |
| Abstract | Generative artificial intelligence (GenAI) can enhance university learning by providing immediate feedback, clarifying complex concepts, supporting problem-solving, and improving access to personalized academic assistance. However, excessive reliance on AI may weaken independent reasoning, academic self-efficacy, and students’ ability to complete tasks without technological support. Previous studies suggest that constructive AI use may function as a learning resource, whereas AI dependency and persistent academic stress may contribute to exhaustion, disengagement, and reduced academic efficacy. Nevertheless, these factors have rarely been examined simultaneously in relation to academic burnout, particularly among Thai university students. This study therefore examined AI-supported learning, AI dependency, and academic stress as predictors of academic burnout among undergraduate students at Ubon Ratchathani University. Data were collected from June 2025 to March 2026 using a quantitative cross-sectional survey. Proportionate stratified sampling across academic fields, supplemented by quota-assisted convenience sampling, yielded 372 students with experience using GenAI for academic purposes. Data were analyzed using descriptive statistics, reliability and validity assessments, Pearson’s correlation, and multiple regression analysis. The model explained 46.0% of the variance in academic burnout. Academic stress was the strongest positive predictor, followed by AI dependency, whereas AI-supported learning had a negative but nonsignificant effect. The findings highlight the importance of promoting responsible AI use while reducing academic pressure and preventing excessive AI dependency. |
