Akademik Veri Yönetim Sistemi
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION, 2026 (SCI-Expanded, SSCI, Scopus)
This study examined the direct and indirect effects of early childhood educators' and candidates' (ECE-C) AI self-efficacy, anxiety, literacy, positive and negative attitudes, perceived ease of use, perceived usefulness, and behavioral intention on their addiction to artificial intelligence (AI). Data were collected using the Teachers' Acceptance of AI Instrument (TAAI), the Dependence on Artificial Intelligence Scale (DAIS), the General Attitudes toward Artificial Intelligence Scale (GAAIS), and the Artificial Intelligence Literacy Scale (AILS). The results revealed that the strongest predictor of AI addiction was a positive attitude toward AI, whereas a negative attitude and anxiety increased addiction. In contrast, self-efficacy emerged as a protective factor that reduced addiction. AI self-efficacy was positively predicted by perceived ease of use, behavioral intention, AI literacy, and, to a lesser extent, perceived usefulness. While AI literacy indirectly reduced addiction via self-efficacy, it also showed an enhancing effect on addiction through anxiety.