HealthyNCT07490665
Impact of Artificial Intelligence Discussion on Midwifery Students
A study testing Experimental AI Case Group in people with Healthy and Student, run by Fenerbahce University at 1 site in Turkey (Türkiye).
Trial summary
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. Read more about this study
- Who is it for?
- Any age, women onlyHealthy volunteers can take part
- What is being tested?
- Experimental AI Case Group
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 81 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 16 Mar 2026to 20 Jul 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- Women only
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- 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
Who cannot join
- Using more than 20% absenteeism in field applications
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Sinem Dinmez, Assistant Professor contact
Turkey (Türkiye)1 site
Fenerbahce University
Istanbul, Atasehır, 34758
About this study
Official title: 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.
Read the 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.
- Being tested
- Experimental AI Case Group
- Keywords
- Artificial Intelligence, Midwifery, Clinical Competence, Problem-Based Learning, Health Occupations
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Experimental AI Case GroupBehavioural
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.
| Study group | Role | What they receive |
|---|---|---|
| AI Case Group | Experimental | 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. Receives: Behavioral: Experimental AI Case Group |
| Control | No intervention | Discussion of routinely implemented maintenance plan |
What is being measured
The primary outcome is the main question the study is designed to answer.
Level of artificial intelligence usage
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.
Measured: Through study completion, an average of 3 months
Usage and proficiency level
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.
Measured: Through study completion, an average of 3 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Mar 2026
- Study start16 Mar 2026 (estimated)
- Primary completion8 Jun 2026 (estimated)
- Study completion20 Jul 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for HealthyStudies of the same condition, open and under way.
- More trials run by Fenerbahce UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- As an intervention, an AI-powered maintenance plan will be discussed.
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 81 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Fenerbahce University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Ayşe Gül Bursa, Fenerbahce University
- Investigators
- Sinem Dinmez, Assistant Professor, Study director, Mudanya UniversityZeynep Ogul, Assistant Professor, Study director
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07490665
- Sponsor's protocol ID
- Fenerbahce U Midwifery
- Status last verified
- Mar 2026
- First submitted
- 13 Mar 2026
- Last updated
- 24 Mar 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: We are not considering an explosion yet because data collection has not begun.
Study at a glance
From the registry record, last updated by the sponsor on 24 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Impact of Artificial Intelligence-Assisted Case Discussion on Artificial Intelligence Attitude, Usage and Proficiency Among Midwifery Students
- ClinicalTrials.gov ID
- NCT07490665
- Acronym / sponsor ID
- Fenerbahce U Midwifery
- Keywords
- Artificial Intelligence, Midwifery, Clinical Competence, Problem-Based Learning, Health Occupations
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 81 participants (estimated)
- Lead sponsor
- Fenerbahce UniversityUniversity, hospital or other
- Study start
- 16 Mar 2026 (estimated)
- Primary completion
- 8 Jun 2026 (estimated)
- Study completion
- 20 Jul 2026 (estimated)
- First posted
- 24 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (participant)
- Number of arms
- 2
- Model description
- As an intervention, an AI-powered maintenance plan will be discussed.
| Arm | Type | Receives |
|---|---|---|
| AI Case Group | Experimental | Behavioral: Experimental AI Case GroupStudents 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. |
| Control | No intervention | –Discussion of routinely implemented maintenance plan |
Who may qualify
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (1 listed)
- Using more than 20% absenteeism in field applications
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Experimental AI Case GroupStudents 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. | Behavioural | AI Case Group |
Primary outcome
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.
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Mar 2026.
- Individual participant dataSharing planned: No. We are not considering an explosion yet because data collection has not begun.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Sinem Dinmez, Assistant Professor contact
Sinem Dinmez, Assistant Professor study director
Mudanya University
Zeynep Ogul, Assistant Professor study director
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Experimental AI Case Group in people with Healthy and Student, run by Fenerbahce University at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 24 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Ayşe G Bursa, Assistant Professor (+905062984670, aysegul.bursa@fbu.edu.tr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Level of artificial intelligence usage (time frame: Through study completion, an average of 3 months). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Mar 2026.