Impact of Artificial Intelligence Discussion on Midwifery Students

Fenerbahce University

Status: Not yet enrolling

Conditions: Healthy; Student

Treatments: Behavioral: Experimental AI Case Group

Study type: Interventional

Funder types: Other

Identifiers

NCT07490665

Fenerbahce U Midwifery

Details and patient eligibility

About

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.

Full 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.

Enrollment: 81 estimated patients

Sex: Female

Volunteers: Accepts Healthy Volunteers

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

Trial design

Primary purpose: Health Services Research

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

---

81 participants in 2 patient groups

AI Case Group

Experimental group

Description:

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.

Treatment:

Behavioral: Experimental AI Case Group

Control

No Intervention group

Description:

Discussion of routinely implemented maintenance plan