시연-개발 전이형 교수학습모형을 적용한 SW·AI교육이 예비 교원의 컴퓨팅 사고력과 AI 역량에 미치는 효과 분석 = The Effects of Software and AI Education Using a Hybrid Demonstration-Development Transitional Model (DMM-DDD) on Pre-service Teachers’ Computational Thinking and AI Competence

다국어 초록 (Multilingual Abstract)

This study was conducted to explore effective instructional approaches for systematically enhancing pre-service elementary teachers’ AI competence and computational thinking in an educational context where the importance of artificial intelligence (AI) education is rapidly increasing. Contemporary AI education in schools requires not only technical application skills but also integrated competencies such as problem-solving, data interpretation, and ethical awareness. Accordingly, this study aimed to apply a Demonstration-Development Transitional Instructional Model as a practical teaching-learning design and empirically analyze its effects on pre-service teachers’ competencies.
The participants were 26 second-year students enrolled at OO National University of Education, who engaged in a 15-week instructional program. This course incorporated Entry-based block coding activities and project-oriented inquiry tasks, allowing learners to acquire basic concepts through demonstration and to deepen creative design and application through development-oriented activities. The research design employed pre-tests and post-tests with paired sample t-tests to analyze changes in computational thinking and AI competence.
The assessment domains for computational thinking included eight components: data collection and analysis, algorithmic procedure, automation, problem decomposition, simulation, data representation, abstraction, and generalization. AI competence was examined across five dimensions: knowledge representation and reasoning, data understanding and learning, machine learning, deep learning, and AI ethics. The results revealed statistically significant improvements in both competencies after the intervention compared to pre-test scores. These findings indicate that stepwise coding practice and inquiry-based learning positively influenced learners’ procedural thinking, data utilization skills, understanding of machine learning and deep learning concepts, and ethical perspectives. In particular, the combination of modeling and foundational learning through demonstration with exploration and design through development provided synergistic effects, confirming that the Demonstration- Development Transitional Instructional Model is an effective pedagogical approach for enhancing pre-service teachers’ computational thinking and AI competence.