Teachers’ Readiness for Artificial Intelligence in Digital Education: Evidence from TALIS 2024 and Implications for a Competence-Based Methodology

Abstract

Purpose. To identify which factors most strongly limit Teachers’ use of artificial intelligence (AI) in digital education and to derive methodological implications for teacher training. Methods. Secondary analysis of published results of the OECD Teaching and Learning International Survey (TALIS) 2024, which covered about 280,000 educators in 55 education systems, supplemented by descriptive statistics and rank and linear correlation analysis for ten systems (n = 10). Results. On average across OECD systems, 36% of teachers reported using AI in their work, with national values ranging from 14% (France) to 75% (Singapore). Among teachers who had not used AI, 75% cited insufficient knowledge and skills, whereas 37% cited insufficient school infrastructure. Skills for using AI were the most frequently reported area of high professional learning need (29%), while 38% of teachers had taken part in AI-related professional learning. In nine of ten analysed systems, lack of knowledge was a more frequent barrier than lack of infrastructure. The association between AI use and the knowledge barrier was negative but not statistically significant (Pearson r = −0.42, p = 0.23; Spearman ρ = −0.55, p = 0.10). Conclusions. Teachers’ competence, rather than technology access, is the primary bottleneck. A competence-based methodology of digital education is therefore proposed, built on diagnostics, pedagogy-first training, institutional enablement and ethical evaluation.