Impact of Artificial Intelligence Discussion on Midwifery Students

March 18, 2026 updated by: Ayşe Gül Bursa, Fenerbahce University

The Impact of Artificial Intelligence-Assisted Case Discussion on Artificial Intelligence Attitude, Usage and Proficiency Among Midwifery Students

This study aimed to examine the effect of artificial intelligence-assisted case discussions on midwifery students' use and proficiency of artificial intelligence technologies and their clinical competency levels. With the rapid development of artificial intelligence, its integration into healthcare education has become increasingly important. Supporting case-based learning with AI tools may enhance students' clinical decision-making, problem-solving, and critical thinking skills. Therefore, this study evaluates the contribution of AI-assisted educational approaches to the professional development of midwifery students.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

Artificial intelligence (AI), which refers to computer-supported systems capable of performing tasks that require human intelligence, has become increasingly popular in all fields in recent years. AI is a technological system that can think, learn, perceive, make predictions, communicate, and make decisions like humans-or even better than humans. In short, AI can be described as a broad scientific field that simulates the natural intelligence demonstrated by humans through artificial means.

The ability of AI to process the data presented to the system, perform data analyses, generate new ideas, and reach different conclusions has increased the use of AI. These features may surpass human problem-solving and decision-making abilities in terms of speed, efficiency, and quality. Due to these advantages, the use of artificial intelligence in midwifery education has become inevitable.

The Australian College of Midwives (ACM) established the Select Committee on Adopting Artificial Intelligence in March 2024 to support and regulate the adoption of AI. This committee emphasized the necessity and priority of using AI in midwifery education. It also highlighted that midwives should be trained in the use of AI and should be an integral part of the design, implementation, and evaluation of all AI tools used in maternity care (ACM, 2024).

Regarding the use of AI in healthcare, the World Health Organization (WHO) has identified three strategic plans: enabling evidence-based standards, governance, policies, and guidance; facilitating shared investments and a global community of expertise; and implementing sustainable models for the adoption of AI programs at the country level (WHO, 2024). In line with these strategies, it is necessary to integrate AI applications into the midwifery profession.

With the influence of rapidly evolving technology, it has become inevitable to improve midwifery education. In traditional education methods, the instructor plays an active role while students remain passive. However, for learning to be effective, opportunities should be created for students to actively practice their skills and develop critical thinking abilities. Case-based learning in midwifery education is a method that can improve students' logical, clinical, and participatory skills while increasing their knowledge levels.

Supporting case-based learning with artificial intelligence tools can contribute to more accurate diagnosis and the development of clinical decision-making and problem-solving skills. In this way, it becomes possible to educate competent midwives who are confident and capable of using technology effectively.

The aim of this study is to examine the effect of artificial intelligence-assisted case discussions on midwifery students' use and proficiency of artificial intelligence technologies and their clinical competency levels.

Study Type

Interventional

Enrollment (Estimated)

81

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

  • Name: Sinem Dinmez, Assistant Professor
  • Phone Number: +905063568804

Study Locations

  • Turkey (Türkiye)
    • Atasehır
      • Istanbul, Atasehır, Turkey (Türkiye), 34758
        • Fenerbahçe University

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

Inclusion Criteria:

  • Being a 3rd/4th year student in the Midwifery department
  • Having previously taken courses on Healthy and High-Risk Pregnancy
  • Having prepared and presented at least one midwifery care plan

Exclusion Criteria:

  • Using more than 20% absenteeism in field applications

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Health Services Research
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Single

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: AI Case Group
Students in the experimental group will be presented with a case scenario and given 30 minutes to review it. During this time, they will be asked to develop a care plan for the case. Subsequently, within a 60-minute session, the researcher will present a care plan prepared with artificial intelligence assistance for the same case. A case discussion will then be conducted by comparing the care plans developed by the students with the AI-assisted care plan.
Behavioral: Experimental AI Case Group
Students in the experimental group will be presented with a case scenario and given 30 minutes to review it. During this time, they will be asked to develop a care plan for the case. Subsequently, within a 60-minute session, the researcher will present a care plan prepared with artificial intelligence assistance for the same case. A case discussion will then be conducted by comparing the care plans developed by the students with the AI-assisted care plan.
No Intervention: Control
Discussion of routinely implemented maintenance plan

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Level of artificial intelligence usage
Time Frame: Through study completion, an average of 3 months
Changes in the level of AI usage among midwifery students after training, compared to baseline values. The Student Attitudes toward Artificial Intelligence Scale (SATAI), developed in 2025, will be used for measurement.The scale, developed using a five-point Likert scale (1=Strongly disagree and 5=Strongly agree), does not contain any reverse-coded items. The highest possible score on the scale is 130, and the lowest is 26, with higher scores reflecting more positive attitudes towards artificial intelligence.
Through study completion, an average of 3 months
Usage and proficiency level
Time Frame: Through study completion, an average of 3 months
This will be measured using the Generative Artificial Intelligence Use and Proficiency (GAAP) Scale, developed in 2024. Planned as a five-point Likert scale (fully reflecting = 5 points - not reflecting = 1 point), an increase in the score obtained from this scale indicates a high level of artificial intelligence use and proficiency. The minimum possible score on the scale is 19, and the maximum is 95.
Through study completion, an average of 3 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Fenerbahce University

Investigators

  • Study Director: Sinem Dinmez, Assistant Professor, Mudanya University
  • Study Director: Zeynep Ogul, Assistant Professor

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

March 16, 2026

Primary Completion (Estimated)

June 8, 2026

Study Completion (Estimated)

July 20, 2026

Study Registration Dates

First Submitted

March 13, 2026

First Submitted That Met QC Criteria

March 18, 2026

First Posted (Actual)

March 24, 2026

Study Record Updates

Last Update Posted (Actual)

March 24, 2026

Last Update Submitted That Met QC Criteria

March 18, 2026

Last Verified

March 1, 2026

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • Fenerbahce U Midwifery

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

We are not considering an explosion yet because data collection has not begun.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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