Grounded in Self-Efficacy Theory, the Technological Pedagogical Content Knowledge (TPACK) framework, the Technology Acceptance Model (TAM), and AI literacy principles, this mixed methods study examines the self-efficacy perceptions of teachers working in gifted education institutions (BILSEMs) in Türkiye regarding the integration of artificial intelligence (AI). Despite the growing global interest in artificial intelligence integration in education, empirical research specifically examining AI self-efficacy among teachers working in gifted education institutions remains limited. An explanatory sequential mixed methods design was employed. Quantitative data were collected from 191 teachers using a 5-point Likert-type Artificial Intelligence Self-Efficacy Scale, followed by semi-structured interviews with five teachers to further elaborate the quantitative findings. Quantitative data were analyzed using multivariate analysis of variance (MANOVA), while qualitative data were analyzed through content analysis. The findings indicate that BILSEM teachers’ AI self-efficacy perceptions were generally at a relatively high level, with higher scores observed in the assistance and technological skills dimensions. Educational level and self-reported AI knowledge were found to be significantly associated with variations in self-efficacy perceptions, whereas gender and age did not yield statistically significant differences. Qualitative findings revealed that teachers primarily viewed AI as a supportive tool for material development and time efficiency, while expressing cautious attitudes toward anthropomorphic interaction and ethical considerations. Overall, the results highlight the importance of targeted professional development for teachers working in gifted education contexts (BILSEMs). The study adopts a descriptive and comparative perspective rather than testing causal relationships, providing context-specific insights into AI self-efficacy in gifted education settings.
Önal et al. (Wed,) studied this question.