Developing a competency framework for smart natural and social sciences teaching in primary teacher education: A Delphi study

Abstract

The rapid rise of artificial intelligence (AI), learning analytics, and intelligent educational technologies has created new competency demands for pre-service primary teachers. Current teacher competence frameworks, however, provide extensive guidance on digital competence but limited support for subject-specific teaching in the natural and social sciences, where inquiry, interdisciplinary integration, evidence-informed pedagogy, and technology-enhanced instruction are essential. This study developed and validated a competency framework for Smart Natural and Social Sciences Teaching based on a three-round modified Delphi method. The framework was developed from an extensive literature review and iteratively evaluated and refined by 20 experts in primary education, science education, educational technology, and educational assessment. Expert consensus was examined using descriptive statistics, agreement percentages, the I-CVI, and the S-CVI/Ave. The resulting framework comprises five competency domains, eighteen component competencies, and 112 performance indicators covering professional knowledge, pedagogical design, instructional implementation, assessment and learning analytics, and professional reflection and innovation. The mean scores in the last Delphi round ranged from 4.42 to 4.76; the percentages of agreement ranged from 90% to 100%; the I-CVI values ranged from 0.90 to 1.00; and the S-CVI/Ave was 0.96, indicating strong expert consensus and excellent content validity. The framework defines smart teaching competence as the integration of digital technologies, AI, and learning analytics into core professional teaching practices. The basis for curriculum development, competency-based assessment, teacher preparation, and future empirical research in Smart Education is validated and operationalized by experts.