This study explores the relationship between teachers' AI-Technological Pedagogical Content Knowledge (AI-TPACK) and its constituent knowledge elements. The AI-TPACK framework integrates seven components: Pedagogical Knowledge (PK), Content Knowledge (CK), AI-Technological Knowledge (AI-TK), Pedagogical Content Knowledge (PCK), AI-Technological Pedagogical Knowledge (AI-TCK), AI-Technological Content Knowledge (AI-TPK), and AI-TPACK itself. The research aims to develop and validate an AI-TPACK measurement tool and explore the relationships among these knowledge elements.
The study employs exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to validate the scale and structural model of the AI-TPACK framework. The results show that six knowledge elements serve as predictive factors for AI-TPACK variables, with varying levels of explanatory power. Core knowledge elements (PK, CK, and AI-TK) have an indirect influence on AI-TPACK, mediated by composite knowledge elements (PCK, AI-TCK, and AI-TPK). Non-technical knowledge elements (CK, PK, and PCK) have significantly lower explanatory power compared to technical knowledge elements (AI-TK, AI-TCK, and AI-TPK).
The study concludes that the AI-TPACK framework is a comprehensive guide for assessing teachers' AI-TPACK and provides insights into the interplay among its elements. These findings have significant implications for the sustainable development of teachers in the era of artificial intelligence, emphasizing the need for teacher training and effective integration of AI technology in educational practices.This study explores the relationship between teachers' AI-Technological Pedagogical Content Knowledge (AI-TPACK) and its constituent knowledge elements. The AI-TPACK framework integrates seven components: Pedagogical Knowledge (PK), Content Knowledge (CK), AI-Technological Knowledge (AI-TK), Pedagogical Content Knowledge (PCK), AI-Technological Pedagogical Knowledge (AI-TCK), AI-Technological Content Knowledge (AI-TPK), and AI-TPACK itself. The research aims to develop and validate an AI-TPACK measurement tool and explore the relationships among these knowledge elements.
The study employs exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to validate the scale and structural model of the AI-TPACK framework. The results show that six knowledge elements serve as predictive factors for AI-TPACK variables, with varying levels of explanatory power. Core knowledge elements (PK, CK, and AI-TK) have an indirect influence on AI-TPACK, mediated by composite knowledge elements (PCK, AI-TCK, and AI-TPK). Non-technical knowledge elements (CK, PK, and PCK) have significantly lower explanatory power compared to technical knowledge elements (AI-TK, AI-TCK, and AI-TPK).
The study concludes that the AI-TPACK framework is a comprehensive guide for assessing teachers' AI-TPACK and provides insights into the interplay among its elements. These findings have significant implications for the sustainable development of teachers in the era of artificial intelligence, emphasizing the need for teacher training and effective integration of AI technology in educational practices.