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

A quasi-experimental study investigated the impact of lecturer-guided, AI-supported flipped learning in undergraduate Information Technology programming education. The research involved 120 students, comparing an AI-integrated flipped learning approach (n=60) with a traditional flipped classroom (n=60). The AI integration involved orchestrating multiple AI tools across pre-class, in-class, and post-class activities, emphasizing continued lecturer guidance, student reasoning, and responsible AI use.

This research provides empirical insights into integrating Artificial Intelligence tools within established educational frameworks, specifically flipped learning. It offers a structured approach to leveraging AI in technical education while maintaining critical human oversight and promoting responsible usage. The findings can inform strategic decisions regarding technology adoption, curriculum development, and pedagogical innovation in higher education and professional training.

The study focused on the pedagogical integration of multiple AI tools within a flipped learning framework in IT programming education.

Forward consideration, not a verified fact.