This study aims to clarify the association between teacher artificial intelligence literacy and professional capital, exploring the mediating roles of professional learning and interdisciplinary teaching in this association, while further identifying the potential heterogeneity across different teacher groups.
Drawing on professional capital theory and teacher learning frameworks, the proposed framework was examined using structural equation modeling and quantile regression, based on large-scale survey data from 953 K-12 teachers in Jiangsu Province, China.
AI literacy is significantly and positively associated with professional capital. Professional learning and interdisciplinary teaching serve as both independent mediators and sequential components in a serial mediation chain. Quantile regression reveals distributional heterogeneity: AI literacy and interdisciplinary teaching show stronger associations at lower professional capital quartiles, while professional learning shows stronger associations at higher quartiles.
At the individual level, differentiated AI literacy training based on teachers' current professional capital (foundational skills for lower quartiles and advanced applications for higher quartiles); at the school level, structured professional learning communities that facilitate AI-enhanced collaborative inquiry; and at the system level, investment in interdisciplinary curriculum frameworks that provide venues for teachers to enact AI-supported teaching practices.
This study integrates established theoretical frameworks to empirically examine associations between AI literacy and teacher professionalism. Findings demonstrate that AI literacy corresponds to professional capital through engagement in collaborative inquiry and interdisciplinary practice, with differentiated patterns (compensatory and incremental) across teacher subgroups, addressing concerns about technological substitution.
