Informatics in Education

This study addresses an implementation problem for informatics education: whether teachers’ reported participation in AI-related professional learning, interpreted as realised access to one professional learning condition for teacher AI literacy, is associated with declared need or instead follows existing patterns of digital, professional and organisational advantage. The study does not measure teacher AI literacy, AI competence, computing teaching practice, computational-thinking instruction or classroom implementation directly. Rather, it analyses reported participation in AI-related professional learning as a realised opportunity condition for developing teacher AI literacy at scale. Using TALIS 2024 data from 108,136 lower-secondary teachers nested within 10,840 schools across 55 education systems, three-level multilevel linear probability models and random slopes at the education-system level were estimated. Results showed substantial cross-system inequality in reported participation. Variance decomposition located 8.5% of total variation at the education-system level and 9.7% at the school level. Declared need was only partially associated with reported participation: teachers reporting low or moderate need were more likely to have participated in AI-related professional learning than those reporting no need, whereas teachers with the highest need showed no significant advantage. Digital self-efficacy and professional collaboration were consistently associated with higher participation. At the school level, digital resource shortages and school digital leadership support were significant predictors. Random-slope estimates showed that the association between high declared need and participation varied significantly across education systems. The findings suggest that equitable teacher AI literacy requires deliberate opportunity structures, not only competence frameworks or voluntary participation in professional learning.