How AI-PCK predicts AI-assisted instruction intention: a study with pre-service teachers in China / Cómo AI-PCK predice la intención de instrucción asistida por IA: un estudio con docentes en formación en China

Abstract

The development and application of artificial intelligence (AI) has accelerated the process of education and teaching transformation. Although prior studies have examined the forms of integrating AI into education, insights into the effective factors impacting pre-service teachers’ AI-assisted instruction intention (AI-AII) are rather limited. Considering this gap, this study constructed a structural model among AI-AII, AI pedagogical content knowledge (AI-PCK), AI technological knowledge (AI-TK), performance expectancy (PE), effort expectancy (EE), social influence (SI) and facilitating conditions (FC). This model explains how teachers can use pedagogical and content knowledge to improve instructional practices, and how technology can support the implementation of the pedagogical and content knowledge. Data were collected from 1,391 pre-service teachers in China. Results of the modelling effort indicate that the pre-service teachers’ AI-PCK, EE, PE, SI and FC positively predict their AI-AII. However, pre-service teachers’ AI-TK did not exert effects on their AI-AII. These insights are important for educators and policymakers to consider in designing teacher education and professional development related to foster pre-service teachers’ behavioural intention to use AI in teaching.