The Formation Pathways of AI Anxiety from a Resource Conservation Theory Perspective: The Mediating Role of Self-Depletion and Machine Learning Validation

Abstract

With the rapid advancement of artificial intelligence technology, while it brings convenience, it also triggers negative emotions in individuals, among which AI anxiety has garnered increasing attention. To explore the formation mechanism of AI anxiety, this study constructs a mediation model centered on self-depletion based on the “resource conservation theory,” aiming to examine the influence pathway of AI acceptance on AI anxiety. Employing a questionnaire survey method, the study involved 730 students from multiple universities. Measurements were conducted using the UTAUT-2 scale, the Self-Regulation Fatigue Scale (SRF-S), and the Artificial Intelligence Anxiety Scale (AIAS). Data analysis utilized SPSS 25.0 and random forest models. Results indicate: (1) AI acceptance exhibits a significant negative correlation with AI anxiety and self-depletion, while self-depletion shows a significant positive correlation with AI anxiety; (2) Self-depletion partially mediates the relationship between AI acceptance and AI anxiety; (3) Random forest analysis further reveals that hedonistic motivation is the most critical factor in alleviating AI anxiety, followed by performance expectations. This study uncovers the pivotal mediating role of self-depletion in AI anxiety formation and identifies core psychological factors for alleviating anxiety from a data-driven perspective, providing crucial theoretical foundations and practical insights for understanding and intervening in AI anxiety.

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