How ready are gifted education teachers for AI integration? Evidence from BILSEM in Türkiye

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Authors: Nezih Önal, Nezih Önal, Ayşegül Oğlakçı İlhan, NAGİHAN TANIK ÖNAL, NAGİHAN TANIK ÖNAL
Cited by: 3
References: 26

Abstract

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.

Fields of Study & Subjects

Education Achievement and Giftedness Teaching and Learning Programming Gender and Technology in Education

Publication details

Journal / source
Education and Information Technologies
Publisher
Springer Science and Business Media LLC
Volume
31
Issue
10
Pages
3007–3033
Publication date
4 March 2026
Language
EN