Generative art literacy for university art and design teachers: conceptualisation, operationalisation, and pedagogical application

Generative AI is reshaping art and design education, yet systematic evidence on teachers’ generative art literacy remains limited. This study conceptualises generative art literacy as a domain-specific competence configuration for university art and design teachers and examines the factors associated with its pedagogical application. Using a framework-integration approach, we developed a generative art literacy framework comprising seven dimensions and 23 sub-competencies, and tested a C-TAM-TPB-informed structural model with data from 310 art and design teachers across 47 Chinese universities. Partial least squares structural equation modelling (PLS-SEM) revealed a differentiated pattern of associations rather than a uniform relationship between competence and pedagogical application. Generative Art Knowledge and Skills showed its strongest association with Human–AI Interaction (f² = 1.773), whereas its direct association with Professional Development was comparatively weak. Perceived Behavioural Control in Generative Art, Professional Development, and Perceived Ethical Risk were positively associated with self-reported pedagogical application. Descriptive variation was also observed across teaching-experience groups, particularly in confidence, prompt-based interaction, and application-related dimensions. The contribution of the study lies not simply in showing that competence, control, and ethical awareness are differentially associated with pedagogical application, but in specifying how these elements are organised in generative visual pedagogy. The findings offer a more differentiated account of teacher readiness for generative AI in creative education and provide a basis for future comparative research across disciplinary and educational contexts.