首页|Data from East China Normal University Update Knowledge in Arti- ficial Intelligence (Teachers' AI-TPACK: Exploring the Relationship between Knowledge Elements)
Data from East China Normal University Update Knowledge in Arti- ficial Intelligence (Teachers' AI-TPACK: Exploring the Relationship between Knowledge Elements)
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Data detailed on artificial intelligence have been presented. According to news origi- nating from Shanghai, People's Republic of China, by NewsRx editors, the research stated, "The profound impact of artificial intelligence (AI) on the modes of teaching and learning necessitates a reexamination of the interrelationships among technology, pedagogy, and subject matter. Given this context, we endeavor to construct a framework for integrating the Technological Pedagogical Content Knowledge of Artificial Intelligence Technology (Artificial Intelligence-Technological Pedagogical Content Knowledge, AI-TPACK) aimed at elucidating the complex interrelations and synergistic effects of AI technology, pedagogical meth- ods, and subject-specific content in the field of education." The news editors obtained a quote from the research from East China Normal University: "The AI- TPACK framework comprises seven components: Pedagogical Knowledge (PK), Content Knowledge (CK), AI-Technological Knowledge (AI-TK), Pedagogical Content Knowledge (PCK), AI-Technological Pedagog- ical Knowledge (AI-TCK), AI-Technological Content Knowledge (AI-TPK), and AI-TPACK itself. We developed an effective structural equation modeling (SEM) approach to explore the relationships among teachers' AI-TPACK knowledge elements through the utilization of exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The result showed that six knowledge elements all serve as predictive factors for AI-TPACK variables. However, different knowledge elements showed varying levels of explana- tory power in relation to teachers' AI-TPACK. The influence of core knowledge elements (PK, CK, and AI-TK) on AI-TPACK is indirect, mediated by composite knowledge elements (PCK, AI-TCK, and AI- TPK), each playing unique roles. Non-technical knowledge elements have significantly lower explanatory power for teachers of AI-TPACK compared to knowledge elements related to technology. Notably, con- tent knowledge (C) diminishes the explanatory power of PCK and AI-TCK. This study investigates the relationships within the AI-TPACK framework and its constituent knowledge elements."
East China Normal UniversityShanghaiPeople's Republic of ChinaAsiaArtificial IntelligenceEmerging TechnologiesMachine LearningTechnology
NETL
NSTL
Mdpi
