AI in STEM Teacher Education: Inquiry into Capabilities of an Emerging Technology

Abstract

Important prerequisites for effective teaching in science, technology, engineering, and mathematics (STEM) are outlined in the refined consensus model of pedagogical content knowledge: Teachers need to become able to apply their pedagogical content knowledge in practice, called enacted pedagogical content knowledge. To support pre-service STEM teachers to develop enacted pedagogical content knowledge, scholars in STEM fields need to implement opportunities of lesson planning, teaching, and reflecting upon teaching, and provide assessments and guidance for professional development. This theoretical article reviews advances in artificial intelligence-based methods in reference to applications of these methods in pre-service STEM teacher education with a focus on the refined consensus model for pedagogical content knowledge. A number of selected studies provides valuable insights into opportunities and challenges of applying AI in STEM teacher education. We found that AI technologies, based on their differing degrees of sophistication, provide different affordances with regards to research and professional development. AI technologies already prevail STEM teacher education and STEM learning more generally. To enhance STEM teacher education with AI technologies, affordances of the technologies with reference to potentials for automation and feedback have to be recognized. The refined consensus model provides a valuable lens to conceptualize these affordances.