Evalution of the Effectiveness of a Shoulder Dystocia Training Program Designed With Inverted Learning Supported by Generative Artificial Intelligence

With the ongoing digital transformation in health education, the importance of learner-centered and technology-integrated approaches has been increasing. Particularly in teaching complex and unpredictable clinical situations in healthcare, the limited effectiveness of traditional methods has led to a growing need for innovative approaches such as flipped learning and self-directed learning. Generative artificial intelligence (GAI)-supported educational applications have the potential to enhance academic achievement by making the learning process more interactive. However, research evaluating the effectiveness of these technologies in education remains limited. In this context, GAI-supported flipped learning enables students to gain individualized learning experiences through simulated classroom or clinical environments and to be better prepared for clinical encounters and specific clinical skill training. One of the critical clinical topics that should be addressed within this framework is shoulder dystocia. Shoulder dystocia is an obstetric emergency, most cases of which are unpredictable and unavoidable. Accordingly, this study aims to evaluate the effects of a GAI-supported flipped learning approach applied to shoulder dystocia training on midwifery students' knowledge levels and self-directed learning skills through effective use of technology.