Purpose In this context, the present study aims to address three significant gaps in the literature. First, it investigates the predictive relationship between AI literacy and attitudes toward AI, aligning with the call by the Beijing Consensus (UNESCO, 2019) for evidence-based policy making in teacher education. Second, it examines the potential differences based on gender, academic year, and department, providing multidimensional data. Third and most importantly, it provides empirical data from the Turkish context to evaluate the universal applicability of global AI competency standards. In summary, this study focuses on the intersection of cognitive (AI literacy) and affective (attitudes toward AI) domains among pre-service teachers, aiming to contribute to the restructuring of teacher education programs. Problem Statement Despite the global push for AI integration led by organizations like UNESCO and the European Commission, a significant “competency-attitude gap” persists in teacher education. Although curriculum developers aim to enhance digital skills, the mechanism by which AI literacy translates into professional attitudes remains a “black box.” Without understanding how specific dimensions of literacy especially ethical and critical evaluation – predict attitudes across different academic years and departments, teacher training programs risk remaining superficial and disconnected from the complex demands of the AI-driven classroom Research Questions (1) What are the levels of pre-service teachers' attitudes toward artificial intelligence? (2) What are the levels of pre-service teachers' artificial intelligence literacy? (3) Do pre-service teachers' attitudes toward artificial intelligence significantly differ according to gender, academic year, and department? (4) Do pre-service teachers' AI literacy levels significantly differ according to gender, academic year, and department? Design/methodology/approach This study was designed within the framework of a correlational research design. This quantitative method aims to statistically examine the direction and strength of relationships between two or more variables. Findings The results indicated that AI literacy levels collectively explained a substantial 57% of the total variance in attitudes toward AI (R2 = 0.568). Notably, while the regression model was highly significant, individual sub-dimensions awareness, usage, evaluation, and ethics did not function as independent predictors, suggesting that AI literacy operates as a holistic competency. Furthermore, significant differences were identified across academic years and departments, with a “mid-program peak” in third-year students and higher engagement in science education compared to mathematics. No significant gender-based differences were observed. These findings underscore the necessity of an integrated, 4-year curriculum that aligns with the UNESCO (2024) AI Competency Framework, moving beyond isolated technical skills toward a transdisciplinary pedagogical approach. The study provides critical insights for teacher educators aiming to foster evidence-based digital dispositions in the AI era. Originality/value First, it investigates the predictive relationship between AI literacy and attitudes toward AI, aligning with the call by the Beijing Consensus (UNESCO, 2019) for evidence-based policy making in teacher education. Second, it examines the potential differences based on gender, academic year, and department, providing multidimensional data. Third and most importantly, it provides empirical data from the Turkish context to evaluate the universal applicability of global AI competency standards. In summary, this study focuses on the intersection of cognitive (AI literacy) and affective (attitudes toward AI) domains among pre-service teachers, aiming to contribute to the restructuring of teacher education programs.
Tuğba TAŞKIN (2026) studied this question.