K–12 Pre-service Teachers' Perspectives on AI Models and Computational Thinking: The Insights from an Interpretative Research Inquiry

Abstract

<p>Computational thinking (CT) has emerged as a pivotal component of K–12 education for fostering problem-solving skills among the next generation of learners. However, CT integration remains an arduous challenge for K–12 teachers due to their limited preparation, prior knowledge, and relevant expertise in CT. To respond to this challenge in Hong Kong, we designed and implemented an introductory CT course employing plugged and unplugged CT approaches alongside AI technology to prepare pre-service teachers. To inform the design of our future course iterations, we conducted an interpretative research inquiry within the course to explore how these teacher trainees learn CT through different teaching and learning activities. Our data analysis accentuated the emergence of three core themes, encompassing numerous subthemes within our data. The three core themes are delineated as themes of (1) CT Knowledge, (2) CT Perspectives, and (3) Potential Barriers. This paper disseminates part of our findings on the trainees' CT Perspectives <em>only</em>: It delves into their post-course perspectives on AI models and CT, seeking to elucidate the pedagogical implications of integrating AI models and CT into K–12 education. These perspectives provide new insights into teaching and learning CT through prompt engineering, which could emerge as a novel approach to democratizing CT education and could be the conduit to bridge the divide between CT and general education.</p>