Teachers’ AI Belief Profiles and Their Associations with AI Use, Pedagogical Applications, and Barriers: A Person-Centered Analysis of TALIS 2024

Teachers’ beliefs about the utility and risks of artificial intelligence (AI) may shape AI-related practice, yet these beliefs are often treated as a single attitude and practice as simple use. This study used a person-centered approach to identify configurations of AI utility and risk beliefs and examine their associations with AI use, pedagogical applications among users, and barriers among non-users. Data were drawn from 40,680 lower-secondary teachers across 49 education systems in TALIS 2024. Following fixed-alignment assessment of approximate measurement invariance, latent profile analysis, R3STEP, and posterior-probability-weighted comparisons were conducted. Four profiles emerged: Indifferent (6.13%), Skeptical (21.41%), Measured Endorsement (56.29%), and Risk-Aware Strong Endorsement (16.18%). AI-related professional learning was most consistently associated with profile membership. Reported AI use ranged from 12.8% in the Indifferent Profile to 76.3% in the Risk-Aware Strong Endorsement Profile, which also showed the highest rates across all eight pedagogical applications. Among non-users, pedagogical reservations were most prevalent in the Indifferent and Skeptical Profiles, knowledge-and-skills constraints in the Measured Endorsement Profile, and both knowledge-and-skills and resource constraints in the Risk-Aware Strong Endorsement Profile. These findings show that distinct belief configurations correspond to meaningful heterogeneity in AI-related practice and support profile-sensitive professional learning and institutional support.