ABSTRACT Computational thinking (CT) and programming are essential competencies in the Fourth Industrial Revolution, spurring interest in learner‐centered and artificial intelligence (AI)‐based education. Combining the active environment of flipped learning with the personalized support of generative AI is a promising new strategy for programming education; however, research integrating these elements remains limited. This study investigated the effects of programming education that incorporates both flipped learning and generative AI on students' CT and problem‐solving skills, with particular attention to variations by university size and educational setting. Participants were students enrolled in introductory software courses at two universities (K and J). Programming lessons integrating generative AI and flipped learning were developed, and changes in students' CT and problem‐solving skills were evaluated using pre‐ and post‐surveys and analyses of programming assignments. The study also examined the relationship between programming‐related CT components (problem decomposition, abstraction, algorithm design, automation, and simulation) and students' programming difficulties, as well as key factors contributing to differences in learning outcomes between institutes. The results revealed significant improvements in the CT and problem‐solving skills of students. Based on these findings, pedagogical strategies were proposed to optimize the use of generative AI and flipped learning in diverse educational contexts. This study provides valuable insights into the application of active, personalized learning in various settings, including introductory software education and online learning.
Jang et al. (2026) studied this question.