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

Background: As generative AI and other technologies reshape the educational ecosystem, teachers' data - artificial intelligent competency (DAIC) has become the core bridge connecting technological innovation with teaching practice. Methods: This study employs a mixed-methods approach to construct a DAIC framework for K-12 teachers and develop a standardized measurement scale for validation. The framework dimensions were first established through thematic mining of 33,800 teacher competency demand texts, combined with two rounds of Delphi consultations involving 28 education experts. Subsequently, a cross-sectional survey was conducted using stratified random sampling. Exploratory factor analysis was performed on 215 pre-survey data points, while confirmatory factor analysis and reliability/validity testing were applied to 2,052 formal survey responses. Results: /df=3.11, CFI=0.938, TLI=0.931, RMSEA=0.046, SRMR=0.040). Conclusion: 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.