Journal of Education and Learning (EduLearn)
Vol 20, No 3: August 2026

Understanding AI education perceptions and teaching efficacy among pre-service teachers in Korea

Yong-Jik Lee (Changwon National University)
Seung-Hoon Jeong (Woosuk University)



Article Info

Publish Date
01 Aug 2026

Abstract

This study examines the perceptions of pre-service physical education (PE) teachers (N=31) regarding artificial intelligence (AI) education and their self-efficacy in teaching AI at a Korean university. Although participants demonstrated high interest levels and acknowledged the necessity of AI in education, their understanding of AI concepts and pedagogical applications was notably limited. Statistical analyses revealed that prior exposure to AI-related coursework significantly increased both understanding and perceived importance of AI education (p0.05). However, participants reported low confidence in designing AI-integrated lessons and applying instructional strategies, indicating a substantial gap in their readiness to teach effectively with AI. The disciplinary gap is particularly pronounced in PE, which is traditionally underrepresented in AI education research compared to STEM subjects. Unlike technology-intensive disciplines, PE curricula often emphasize physical skill development and experiential learning, leaving minimal room for the integration of emerging technologies such as AI. This underrepresentation means that PE teacher education programs lack established models or best practices for embedding AI into instruction, further widening the preparedness gap. While participants recognized the societal relevance of AI and expressed a strong sense of responsibility for student learning, they remained hesitant about emotionally intelligent AI and human–AI interactions. These findings underscore the urgent need for structured AI training programs within teacher education—particularly in non-STEM fields like PE—that combine theoretical understanding with practical teaching competence. The results provide foundational evidence to guide the development of AI-integrated curricula that can prepare future educators for an increasingly AI-driven educational landscape.

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Journal Info

Abbrev

EduLearn

Publisher

Subject

Humanities Education Library & Information Science Social Sciences Other

Description

Journal of Education and Learning (EduLearn) ISSN: 2089-9823, e-ISSN 2302-9277 is a multi-disciplinary, peer-refereed open-access international journal which has been established for the dissemination of state-of-the-art knowledge in the field of education, teaching, development, instruction, ...