Association of generative artificial intelligence dependence and academic burnout among college students
10.16835/j.cnki.1000-9817.2026162
- VernacularTitle:生成式人工智能依赖与大学生学业倦怠的关系
- Author:
GUO Xiaoyu, LI Man
1
Author Information
1. School of Computer Science and Engineering, Northeastern University, Shenyang 110819,Liaoning Province, China
- Publication Type:Journal Article
- Keywords:
Artificial intelligence；Learning；Fatigue；Mental health；Students
- From:
Chinese Journal of School Health
2026;47(5):652-655
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To understand the current status of generative artificial intelligence (GenAI) dependence among college students, explore its relationship with basic psychological needs satisfaction, artificial intelligence (AI) literacy, and academic burnout, so as to provide an empirical basis for universities to formulate scientific AI education governance strategies.
Methods:A stratified cluster sampling method was used to select 1 086 undergraduates from 5 universities in Shenyang from March to May 2025. Surveys were conducted using the GenAI Dependence Scale, Basic Psychological Needs Satisfaction Scale, Academic Burnout Scale, and AI Literacy Scale. Pearson correlation analysis was utilized to examine the correlations among variables, and the PROCESS Model was employed to test the mediating effects.
Results:The scores for college students GenAI dependence and academic burnout were (2.94±0.86) and (3.15±0.84), respectively. Pearson correlation analysis indicated that GenAI dependence was positively correlated with academic burnout ( r =0.40) and negatively correlated with both basic psychological need satisfaction and AI literacy ( r =-0.39,-0.22)(all P <0.01). Mediation analysis revealed that basic psychological need satisfaction partially mediated the relationship between GenAI dependence and academic burnout, with an effect size of 0.109 (95% CI =0.075-0.148). AI literacy moderated this mediation pathway, yielding a moderated mediation index of -0.051 (95% CI =-0.089 to -0.021). Under low AI literacy levels, the indirect effect of GenAI dependence on academic burnout through basic psychological needs satisfaction was stronger (effect size=0.159, 95% CI =0.108-0.201), whereas high AI literacy effectively buffered this indirect negative impact (effect size=0.058, 95% CI =0.012-0.095).
Conclusion:GenAI dependence not only directly predicts academic burnout but also exerts an indirect effect by thwarting psychological needs satisfaction, with AI literacy playing a moderating role.
