Introduction: With the proliferation of generative artificial intelligence (AI) in higher education, student overreliance has become a growing concern, potentially undermining critical thinking and autonomous learning. To address the lack of a comprehensive measurement tool, this study developed and validated the AI Dependence Scale (AIDep-22), a new instrument designed to assess this phenomenon across four hypothesized dimensions: emotional dependence, functional dependence, cognitive dependence, and loss of control. Methods: The scale was constructed following a rigorous two-stage process, beginning with item generation and refinement through expert reviews and cognitive interviews, followed by psychometric evaluation with two independent samples of Chinese university students ( N = 400 each). Results: An exploratory factor analysis (EFA) supported the four-factor structure, which was subsequently confirmed by a confirmatory factor analysis (CFA) on the second sample. The final 22-item scale demonstrated excellent internal consistency (Cronbach's alpha = 0.87), strong convergent and discriminant validity, and robust criterion-related validity. Preliminary analyses also identified key demographic risk factors, revealing that male students, upper-year students, those in applied majors, and more frequent AI users reported significantly higher dependence. Discussion: This study contributes a reliable and valid diagnostic tool that enables educators and researchers to identify and support students at risk, and to design targeted interventions that promote a more balanced human-AI relationship in higher education.

Background: The rapid integration of Artificial Intelligence (AI) in higher education offers transformative learning potential but has concurrently triggered psychological challenges, specifically AI anxiety and AI dependence. While these phenomena are increasingly prevalent, the psychological mechanisms converting affective strain into behavioral reliance remain underexplored. Grounded in Cognitive Behavioral Theory and Conservation of Resources Theory, this study investigates the relationship between AI anxiety and AI dependence among undergraduates. Specifically, it examines whether AI self-efficacy mediates this relationship and whether AI literacy acts as a boundary condition moderating these pathways. Methods: A cross-sectional survey was conducted with 400 undergraduates recruited via stratified sampling from a normal university in Southwest China. Participants completed validated scales measuring AI anxiety, AI self-efficacy, AI literacy, and AI dependence. Confirmatory factor analysis was performed to verify instrument validity. Hypotheses were tested using the PROCESS macro (Models 4 and 8) to analyze mediation and moderated mediation effects, utilizing bootstrapping techniques with 5,000 resamples to determine the statistical significance of direct and indirect effects. Results: The analysis revealed a significant positive association between AI anxiety and AI dependence. AI self-efficacy was found to partially mediate this relationship, indicating that anxiety exacerbates dependence by eroding students’ confidence in their capabilities. Furthermore, AI literacy served as a significant moderator. Results indicated that the negative impact of AI anxiety on AI self-efficacy was stronger for students with higher AI literacy, suggesting that high literacy may sensitize students to competency gaps under emotional strain. AI literacy did not moderate the direct relationship between anxiety and dependence. Conclusion: This study identifies AI dependence as a maladaptive coping response to AI anxiety, driven by diminished AI self-efficacy. The findings challenge the assumption that literacy acts solely as a protective buffer, revealing that without emotional regulation, high literacy may intensify the erosion of self- confidence during anxious states. To mitigate maladaptive dependence, higher education institutions should move beyond technical training to adopt holistic strategies. These should include psychological support to build resilience, mastery experiences to foster self-efficacy, and comprehensive curricula that address the emotional and cognitive dimensions of human-AI interaction.

1. Generative Artificial Intelligence (AI) has emerged as a transformative force in higher education, reshaping pedagogical possibilities and raising urgent concerns regarding instructional readine...

Generative artificial intelligence (AI), a subset of AI that produces new content in response to prompts, has rapidly become part of higher education. Students use large language models and related...