DOI : https://doi.org/10.18517/ijaseit.15.1.12409

Artificial Intelligence Convergence Teaching Expertise Scale for Pre-service Teachers in Korea: A Validity and Reliability Study

Seong-Won Kim (1)
(1) Department of Computer Education, Busan National University of Education, Yeonje-gu, Busan, Republic of Korea
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S.-W. Kim, “Artificial Intelligence Convergence Teaching Expertise Scale for Pre-service Teachers in Korea: A Validity and Reliability Study”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 15, no. 1, pp. 1–8, Feb. 2025.
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Artificial intelligence (AI) drives changes in various areas, including industry, society, and the economy, through technological innovation. In education, AI advancements are leading to innovations in teaching, learning, content, and assessment. Integrating AI into educational practices necessitates developing a tool to measure instructors' AI convergence teaching expertise, which is essential for implementing AI convergence education effectively. This study aimed to develop an assessment tool to measure pre-service teachers' AI convergence teaching expertise. A comprehensive and rigorous approach was adopted, involving a literature review, Delphi survey, exploratory factor analysis, and confirmatory factor analysis. The assessment tool was constructed by applying AI as a technological component within the Technological Pedagogical Content Knowledge (TPACK) framework to define AI convergence teaching expertise and derive relevant items. These items were administered to a sample of 202 pre-service teachers in Korea to validate their reliability and validity. The developed tool comprises 33 self-reported items across eight distinct factors, robustly measuring AI convergence teaching expertise. The Cronbach’s α for the tool ranged from 0.822 to 0.922, indicating high reliability. The significance of this study is its potential application within educational settings to determine pre-service teachers' AI convergence teaching expertise. Furthermore, this study offers valuable implications for designing pre-service teacher education programs and ongoing professional development for in-service teachers. By accurately measuring and addressing AI convergence teaching expertise, this tool can contribute to advancing educational practices in the era of AI.

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