Development and validation of a framework and scale for primary and secondary school teachers’ data-artificial intelligent competence

This study integrates relevant theories to reveal the intrinsic logic of merging data literacy with AI literacy, overcoming the fragmented limitations of existing research that analyzes them separately. Besides, it supplements localized evidence of teachers' DAIC within the Chinese context, specifically addressing cultural adaptability and low-resource environment suitability issues in international frameworks. The developed scales and low-threshold application solutions adapted to urban-rural disparities provide actionable pathways for teacher professional development in resource-constrained regions. This framework and scales balance theoretical rigor with practical applicability, offering scientific tools for differentiated teacher training and regional educational informatization assessment. They also provide reference for localizing international teacher digital literacy frameworks, thereby advancing equitable educational digitalization.