Understanding AI adoption among secondary education teachers: A pls-sem approach

This study investigates the factors influencing the adoption of Artificial Intelligence (AI) by secondary school teachers in Catalonia. Using a Partial Least Squares Structural Equation Modelling (PLS-SEM) methodology, a conceptual model was analyzed that includes AI perception, AI knowledge, General data use, Applied data use, and STEM training as predictors of AI adoption. The results reveal that AI knowledge (β = .482, p < .001) and General data use (β = .288, p = .001) are the most significant and positive predictors of AI adoption. In contrast, AI perception shows a weak but statistically significant negative relationship (β = -.105, p = .022), while applied data use and STEM training do not present a significant direct effect. The model explains 30.5 % of the variance in AI adoption. These findings suggest that developing specific knowledge on how to use AI for content creation and competence in general data use is crucial to fostering AI adoption among secondary school teachers in the Catalan context. In addition, this explorative work provides the research community with evidence that key Data Literacy competencies significantly shape AI adoption.