Measuring university students’ AI dependency in the school learning contexts: a non-addictive perspective
Year: 2026
Type: article
Abstract: Background The current conceptualization and measures of artificial intelligence (AI) dependency are generally addiction-based and symptom-centered, having been directly established using the criteria framework of behavioral addiction via the confirmatory approach. Few studies have addressed the non-addictive and process-oriented aspects of AI dependency among university students in the school learning context. Objective To develop a process-centered scale to measure the AI dependency of university students specifically in the school learning contexts from the non-addictive perspective. Methods Three sub-studies were completed. In sub-study 1, an open-ended interview with 17 interviewees revealed that AI dependency in the school learning contexts was commonly understood to be a non-addictive phenomenon. In sub-study 2, the University Student AI-Dependency Scale (USAIDS) was developed based on the non-addictive definition and process-centered characteristics of AI dependency. An exploratory factory analysis of a sample of 442 university students identified three factors: excessive trust in AI (ETAI), knowledge-retrieval dependence on AI (KRDAI), and knowledge-generation dependence on AI (KGDAI). This tri-factor structure of the USAIDS was confirmed in a subsequent confirmatory factor analysis of a sample of 1,804 university students. Furthermore, error variance invariance of the USAIDS held across gender, educational level, and major type. Finally, in sub-study 3, it was confirmed that university students’ AI dependency had weak negative correlations with academic self-efficacy, and critical thinking, but positive correlations with learning burnout, anxiety, and depression. Conclusion The USAIDS showed adequate psychometric properties among university students in the school learning context. The current study sheds light on understanding the AI dependency of university students in the school learning contexts and appeals to scholars to pay more attention to non-addictive rather than addictive AI dependency among university students.
Source: Psicologia Reflexão e Crítica
Authors: Xiancai Ou, Mengzi Lu, Jingchen Zhu, Peiyuan Li
Institution: Guangxi Minzu University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,590
OpenAlex ID: W7214094134