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.
-Being a 3rd year student in the Midwifery Department of Fenerbahçe University and Istanbul-Cerrahpaşa Faculty of Health Sciences.
Having sufficient or unlimited internet access.
Having previously taken a course on shoulder dystocia
Student's absence from class/sick leave
A total of 114 third-year students who meet the inclusion criteria will be administered the "Student Information Form" and the "Shoulder Dystocia Management Knowledge Test." To determine baseline levels, participants will be stratified into three layers based on their shoulder dystocia management knowledge test scores and then assigned to G1, G2, and G3 groups using simple randomization in a 1:1:1 ratio. The randomization list will be generated by an individual independent of the researcher in order to reduce selection bias, using the randomizer.org website. Random group assignments will be performed on a stratified basis (stratification criteria: institutions and shoulder dystocia management knowledge test scores). Using this method, 114 third-year students will be randomized equally into three groups, with 38 students assigned to G1, 38 to G2, and 38 to G3.
Other
Other
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Knowledge level on shoulder dystocia management
The knowledge level of midwifery students regarding shoulder dystocia management will be assessed using a structured knowledge test developed by the researcher.
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