Artificial intelligence-supported flipped learning in information technology education: quasi-experimental evidence on programming performance, learning autonomy, reflective learning, and digital competence

This study examines lecturer-guided artificial intelligence (AI)-supported flipped learning in undergraduate Information Technology programming education. The study focuses on how multiple AI tools can be pedagogically orchestrated across pre-class preparation, in-class programming practice, and post-class reflection while maintaining lecturer guidance, student reasoning, and responsible AI use. A classroom-based non-equivalent groups quasi-experimental design was implemented with 120 undergraduate Information Technology students in a Programming Techniques course. One intact class participated in a lecturer-guided AI-supported flipped learning condition ( n = 60), while another intact class participated in a traditional flipped classroom condition without planned AI-supported learning tasks ( n = 60). Quantitative data included pretest and posttest programming scores and post-intervention self-reported measures of learning autonomy, reflective learning, and digital competence. Qualitative interview data from students and lecturers were used to contextualize participants' perceptions of AI-supported learning activities. The results showed that the AI-supported flipped learning group achieved higher posttest programming performance, showed a larger observed programming-performance gain, and reported higher post-intervention learning autonomy, reflective learning, and digital competence than the traditional flipped classroom group. Because the self-reported items referred explicitly to AI-supported activities, these differences are interpreted as condition-linked perceptions rather than as comparisons of general competence. Qualitative findings indicated that students and lecturers perceived AI as useful for preparation, debugging-related feedback, solution comparison, reflection, and lecturer-guided verification. The findings should be interpreted as classroom-based evidence of observed group differences rather than as definitive causal proof of AI tool effects. The main contribution of the study is to illustrate how multiple AI tools can be organized as a lecturer-guided pedagogical orchestration within the flipped programming learning cycle.