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

NCT07514702 | Not Yet Recruiting

• 1 other identifier: 2025/756
• Study Type: observational
• Target: 114
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Shoulder Dystocia Training Using AI-supported Flipped Learning in Midwifery Education

Keywords

shoulder dystocia; flipped learning; midwifery education; Generative Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

• 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.

  Before the intervention and immediately after the intervention

Study Arms (3)

Generative AI-supported flipped learning

Midwifery students receiving shoulder dystocia training through a generative AI-supported flipped learning approach with interactive and scenario-based activities.

Standard flipped learning

Midwifery students receiving shoulder dystocia training through a standard flipped learning approach without AI support.

Traditional learning

Midwifery students receiving shoulder dystocia training through traditional lecture-based teaching methods.

Eligibility Criteria

• Sex: female
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

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.

You may qualify if:

• 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.

You may not qualify if:

• Having previously taken a course on shoulder dystocia
• Student's absence from class/sick leave

Sponsors & Collaborators

• Fenerbahce University: lead

Study Sites (1)

Fenerbahce University

Istanbul, Turkey (Türkiye)

Location

Related Publications (3)

• American College of Obstetricians and Gynecologists. (2017). Practice Bulletin No 178: Shoulder Dystocia.

  BACKGROUND

• Royal College of Obstetricians and Gynaecologists. (2012). Shoulder Dystocia (Green-top Guideline No. 42. 2nd Edition). https://www.rcog.org.uk/media/ewgpnmio/gtg_42.pdf

  BACKGROUND

• Majeed F, Usmani A. Effect of case-based teaching of physiology via flipped classroom method on performance of undergraduate medical students. Adv Physiol Educ. 2025 Sep 1;49(3):840-848. doi: 10.1152/advan.00161.2024. Epub 2025 Jul 28.

  PMID: 40720183 BACKGROUND

MeSH Terms

Conditions

Shoulder Dystocia

Condition Hierarchy (Ancestors)

Dystocia; Obstetric Labor Complications; Pregnancy Complications; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases

Central Study Contacts

Melek Kılıç, PhD Candidate

CONTACT

+905447324244; mlekisik@gmail.com

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: LECTURER

Study Record Dates

• First Submitted: March 31, 2026
• First Posted: April 7, 2026
• Study Start: April 1, 2026
• Primary Completion (Estimated): June 1, 2026
• Study Completion (Estimated): June 1, 2027
• Last Updated: April 16, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF
• Time Frame: De-identified individual participant data (IPD) and supporting documents (study protocol, statistical analysis plan, and informed consent form) will be available beginning 6 months after publication of the study results and will remain available for a period of 5 years. Data access will be provided upon reasonable request to the corresponding author, subject to approval by the research team.
• Access Criteria: Access to de-identified individual participant data (IPD) and supporting documents (study protocol, statistical analysis plan, and informed consent form) will be granted to qualified researchers who provide a methodologically sound research proposal. Requests should be directed to the corresponding author. Data will be shared after approval by the research team and, where applicable, the institutional ethics committee. Data will be provided in a secure format, and users will be required to agree to terms that ensure confidentiality and appropriate use of the data.

Locations

TU (1)