Nursing Education ResearchNCT06999447
Impact of AI-Supported Teaching on Clinical Decision-Making in Nursing Students
A study testing ChatGPT-Supported Case-Based AI Education (C-CASE) and Standard Education in people with Nursing Education Research, run by Yeditepe University at 1 site in Turkey (Türkiye).
Trial summary
This clinical trial aims to explore whether an AI-supported teaching method can help nursing students improve their clinical decision-making skills and knowledge during case-based learning. The study focuses on third-year nursing students enrolled in an emergency care course. Read more about this study
- Who is it for?
- Ages 18 to 25 yearsHealthy volunteers can take part
- Conditions the study is for
- Nursing Education Research
- What is being tested?
- ChatGPT-Supported Case-Based AI Education (C-CASE) and Standard Education
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 66 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 26 Mar 2025to 26 Mar 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
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 18 to 25 years
- All sexes
- Healthy volunteers accepted
- Adult
Who may be able to join
- Successful completion of prerequisite courses (Fundamentals of Nursing I-II,
- Medical-Surgical Diseases Nursing, and Pediatric Nursing) along with associated clinical internships
- Enrollment in the Emergency Care course during the study period
- Volunteering to participate and providing written informed consent
- Must be a third-year undergraduate nursing student.
- Must be enrolled in the "Emergency Care Nursing" course during the 2024-2025 spring semester.
- Must be attending the Faculty of Health Sciences, Department of Nursing, at Yeditepe University.
- Completion of all data collection forms
Who cannot join
- Failure to complete prerequisite courses or required clinical internships
- Irregular attendance in the Emergency Care course
- Declining to participate or failure to provide written informed consent
- Submission of incomplete data collection forms
Trial locations and contacts
1 site in 1 country.
About this study
Official title: AI-Supported Teaching in Pediatric Surgical Emergency Case Management: Effects on Nursing Students' Knowledge and Clinical Decision-Making in a Randomized Controlled Study
This clinical trial aims to explore whether an AI-supported teaching method can help nursing students improve their clinical decision-making skills and knowledge during case-based learning. The study focuses on third-year nursing students enrolled in an emergency care course. Participants are divided into two groups: one group receives traditional case-based instruction, while the other uses ChatGPT (an AI language model developed by OpenAI- (Chat Generative Pre-trained Transformer)) to support their case-solving activities. All students complete a pretest and posttest to assess their knowledge and perceptions of clinical decision-making. The main goals are to find out whether the AI-supported group performs better than the traditional group and to evaluate the relationship between students' knowledge and their clinical decision-making scores. By comparing these two teaching methods, researchers aim to understand whether integrating AI tools into nursing education can enhance learning outcomes.
- Conditions
- Nursing Education Research
- Being tested
- ChatGPT-Supported Case-Based AI Education (C-CASE), Standard Education
- Keywords
- Clinical decision-making, Artificial intelligence, Case-based learning, Surgical nursing, Pediatric nursing
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
ChatGPT-Supported Case-Based AI Education (C-CASE)Other
In the intervention group (C-CASE), after informed consent and pretest completion (including a sociodemographic form and CDMNS), the case scenario was introduced by the course instructor. Students were divided into small groups, and each group selected a representative who accessed ChatGPT-4.0 Premium via credentials provided by the research team. Using a collaborative problem-solving format, each group worked through a structured case scenario involving pediatric surgical emergencies. Questions were distributed sequentially, with 5-minute intervals allocated per question. Students used ChatGPT to support reasoning and clinical decision-making within their group. After each interval, responses were submitted, and the next question was handed out. Sessions were proctored by research assistants, and the full implementation, including discussion, lasted approximately two hours. The intervention aimed to foster decision-making, teamwork, and AI literacy in a clinical nursing education.
Standard EducationOther
In the control group (Standard Education), students followed the same structured case-based learning session as the intervention group, without access to AI tools. After providing informed consent and completing the pretest (sociodemographic form and CDMNS), the case scenario was introduced by the instructor. Students were divided into small groups and selected a representative to use a personal computer during the session. To ensure no access to AI-based tools, the Mobile Guardian app was installed to block websites such as ChatGPT. Students were allowed to use only academic databases and the university's online library. Each group answered a series of timed case questions (5 minutes per item), submitting responses before receiving the next question. Research assistants monitored the session in both classrooms to ensure standardization and prevent external support. The session concluded with a class-wide case discussion, led by the course instructor.
| Study group | Role | What they receive |
|---|---|---|
| C-CASE Group (AI-Supported Case-Based Education) | Experimental | In this arm, participants received a behavioral intervention involving AI-supported education during a structured, classroom-based case-solving session. After informed consent and a pretest, students were divided into groups of six. Each group selected a representative with access to ChatGPT-4 Premium via researcher-provided credentials. These representatives interacted directly with the AI while others collaborated in real time to solve a pediatric surgical emergency case. The case included 10 structured questions and one open-ended item, based on the Bowtie model used in NCLEX. Each question was addressed in 5-minute intervals through team-based discussion. The intervention aimed to enhance clinical decision-making and case-specific knowledge. No drug, device, or clinical procedure was used; the AI-supported education was conducted entirely in an academic classroom setting using digital tools. Receives: Other: ChatGPT-Supported Case-Based AI Education (C-CASE) |
| Standard Education Group (Traditional Case-Based Learning) | Active comparator | Participants in this group engaged in traditional, instructor-led case-solving sessions without access to AI tools. Students were divided into groups of six and analyzed the same pediatric surgical emergency case used in the intervention group. To ensure no use of AI platforms like ChatGPT, a classroom monitoring application was implemented. Instead, students used institutional academic databases and library resources. The activity's structure, including group size, timing, question sequence, and classroom setup, mirrored the AI-supported group's experience to ensure consistency. This arm served as the control condition to compare traditional education with AI-assisted learning in terms of clinical decision-making and knowledge development. No drug, device, or clinical procedure was used; this was a classroom-based educational activity only. Receives: Other: Standard Education |
What is being measured
The primary outcome is the main question the study is designed to answer.
Clinical decision-making skills
This outcome measures clinical decision-making using the Clinical Decision-Making in Nursing Scale (CDMNS), developed by Jenkins (1983) and validated in Turkish by Durmaz (2012). The 40-item scale is rated on a 5-point Likert scale ("always" to "never") and includes four subdimensions: Search for Alternatives or Options (SAO), Canvassing of Objectives and Values (COV), Evaluation and Re-evaluation of Consequences (ERC), and Search for Information and Unbiased Assimilation of New Information (SIUANI). Each subdimension includes 10 items. Total scores range from 40 to 200; higher scores reflect stronger decision-making. Of the 40 items, 22 are positively worded and 18 are negatively worded (reverse-scored). Minimum and maximum scores for subdimensions are not explicitly defined in the original scale; instead, changes in subdimension scores were analyzed based on increase or decrease. The scale was applied at pretest and posttest.
Measured: From baseline (before intervention) to immediately after the intervention session (same day)
Secondary outcomes (1)
Case-Specific Knowledge Test Score
This measure evaluates nursing students' knowledge in pediatric surgical emergency management using a researcher-developed, scenario-based test aligned with the NCSBN "Bowtie" model. The test includes 10 structured items (one with two parts) and one open-ended question, totaling 11 questions. Items combine multiple-choice and short-answer formats, requiring students to prioritize, analyze, and justify decisions. Partial credit is awarded for correct responses and justifications; incorrect answers do not deduct points. Scores range from 0 to 100, with higher scores indicating greater case-specific knowledge. No cutoff was defined; mean scores were compared between groups at posttest. This is not a previously validated measurement tool (scale); however, content validation was conducted by five independent nurse educators with expertise in pediatric and surgical nursing. The test was developed by two PhD nurse educators and administered digitally via QR code.
Measured: Immediately after the intervention session (same day)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 May 2025
- Study start26 Mar 2025
- Primary completion26 Mar 2025
- Study completion26 Mar 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Nursing Education ResearchStudies of the same condition, open and under way.
- 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
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- This study used a parallel assignment model in which participants were randomly assigned to either an experimental group receiving AI-supported case-based education (C-CASE) using ChatGPT-4 or a control group receiving standard case-based instruction. Each participant remained in their assigned group throughout the study, and both groups completed pretest and posttest evaluations.
- Masking
- None (open label)
- Enrolment
- 66 participants (actual)
Sponsor and investigators
- Lead sponsor
- Yeditepe University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Gokce Naz Cakir, Yeditepe University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06999447
- Sponsor's protocol ID
- 202312Y0718
- Status last verified
- Jun 2025
- First submitted
- 22 May 2025
- Last updated
- 6 Jun 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: IPD (Individual Participant Data) will not be shared publicly because the dataset includes identifiable student information such as institutional email addresses and sociodemographic data. However, if requested by a relevant ethics board or regulatory authority, a de-identified version of the dataset may be made available upon justified request and appropriate approval.
Study at a glance
From the registry record, last updated by the sponsor on 6 Jun 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-Supported Teaching in Pediatric Surgical Emergency Case Management: Effects on Nursing Students' Knowledge and Clinical Decision-Making in a Randomized Controlled Study
- ClinicalTrials.gov ID
- NCT06999447
- Acronym / sponsor ID
- 202312Y0718
- Conditions
- Nursing Education Research
- Keywords
- Clinical decision-making, Artificial intelligence, Case-based learning, Surgical nursing, Pediatric nursing
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 66 participants (actual)
- Lead sponsor
- Yeditepe UniversityUniversity, hospital or other
- Study start
- 26 Mar 2025 (actual)
- Primary completion
- 26 Mar 2025 (actual)
- Study completion
- 26 Mar 2025 (actual)
- First posted
- 31 May 2025 (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 clinical trial aims to explore whether an AI-supported teaching method can help nursing students improve their clinical decision-making skills and knowledge during case-based learning. The study focuses on third-year nursing students enrolled in an emergency care course. Participants are divided into two groups: one group receives traditional case-based instruction, while the other uses ChatGPT (an AI language model developed by OpenAI- (Chat Generative Pre-trained Transformer)) to support their case-solving activities. All students complete a pretest and posttest to assess their knowledge and perceptions of clinical decision-making. The main goals are to find out whether the AI-supported group performs better than the traditional group and to evaluate the relationship between students' knowledge and their clinical decision-making scores. By comparing these two teaching methods, researchers aim to understand whether integrating AI tools into nursing education can enhance learning outcomes.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- This study used a parallel assignment model in which participants were randomly assigned to either an experimental group receiving AI-supported case-based education (C-CASE) using ChatGPT-4 or a control group receiving standard case-based instruction. Each participant remained in their assigned group throughout the study, and both groups completed pretest and posttest evaluations.
| Arm | Type | Receives |
|---|---|---|
| C-CASE Group (AI-Supported Case-Based Education) | Experimental | Other: ChatGPT-Supported Case-Based AI Education (C-CASE)In this arm, participants received a behavioral intervention involving AI-supported education during a structured, classroom-based case-solving session. After informed consent and a pretest, students were divided into groups of six. Each group selected a representative with access to ChatGPT-4 Premium via researcher-provided credentials. These representatives interacted directly with the AI while others collaborated in real time to solve a pediatric surgical emergency case. The case included 10 structured questions and one open-ended item, based on the Bowtie model used in NCLEX. Each question was addressed in 5-minute intervals through team-based discussion. The intervention aimed to enhance clinical decision-making and case-specific knowledge. No drug, device, or clinical procedure was used; the AI-supported education was conducted entirely in an academic classroom setting using digital tools. |
| Standard Education Group (Traditional Case-Based Learning) | Active comparator | Other: Standard EducationParticipants in this group engaged in traditional, instructor-led case-solving sessions without access to AI tools. Students were divided into groups of six and analyzed the same pediatric surgical emergency case used in the intervention group. To ensure no use of AI platforms like ChatGPT, a classroom monitoring application was implemented. Instead, students used institutional academic databases and library resources. The activity's structure, including group size, timing, question sequence, and classroom setup, mirrored the AI-supported group's experience to ensure consistency. This arm served as the control condition to compare traditional education with AI-assisted learning in terms of clinical decision-making and knowledge development. No drug, device, or clinical procedure was used; this was a classroom-based educational activity only. |
Who may qualify
- Ages
- 18 to 25 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
Key inclusion criteria (8 listed)
- Successful completion of prerequisite courses (Fundamentals of Nursing I-II,
- Medical-Surgical Diseases Nursing, and Pediatric Nursing) along with associated clinical internships
- Enrollment in the Emergency Care course during the study period
- Volunteering to participate and providing written informed consent
- Must be a third-year undergraduate nursing student.
Key exclusion criteria (4 listed)
- Failure to complete prerequisite courses or required clinical internships
- Irregular attendance in the Emergency Care course
- Declining to participate or failure to provide written informed consent
- Submission of incomplete data collection forms
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 |
|---|---|---|
| ChatGPT-Supported Case-Based AI Education (C-CASE)In the intervention group (C-CASE), after informed consent and pretest completion (including a sociodemographic form and CDMNS), the case scenario was introduced by the course instructor. Students were divided into small groups, and each group selected a representative who accessed ChatGPT-4.0 Premium via credentials provided by the research team. Using a collaborative problem-solving format, each group worked through a structured case scenario involving pediatric surgical emergencies. Questions were distributed sequentially, with 5-minute intervals allocated per question. Students used ChatGPT to support reasoning and clinical decision-making within their group. After each interval, responses were submitted, and the next question was handed out. Sessions were proctored by research assistants, and the full implementation, including discussion, lasted approximately two hours. The intervention aimed to foster decision-making, teamwork, and AI literacy in a clinical nursing education. | Other | C-CASE Group (AI-Supported Case-Based Education) |
| Standard EducationIn the control group (Standard Education), students followed the same structured case-based learning session as the intervention group, without access to AI tools. After providing informed consent and completing the pretest (sociodemographic form and CDMNS), the case scenario was introduced by the instructor. Students were divided into small groups and selected a representative to use a personal computer during the session. To ensure no access to AI-based tools, the Mobile Guardian app was installed to block websites such as ChatGPT. Students were allowed to use only academic databases and the university's online library. Each group answered a series of timed case questions (5 minutes per item), submitting responses before receiving the next question. Research assistants monitored the session in both classrooms to ensure standardization and prevent external support. The session concluded with a class-wide case discussion, led by the course instructor. | Other | Standard Education Group (Traditional Case-Based Learning) |
Primary outcome
Clinical decision-making skills
Time frame: From baseline (before intervention) to immediately after the intervention session (same day)
This outcome measures clinical decision-making using the Clinical Decision-Making in Nursing Scale (CDMNS), developed by Jenkins (1983) and validated in Turkish by Durmaz (2012). The 40-item scale is rated on a 5-point Likert scale ("always" to "never") and includes four subdimensions: Search for Alternatives or Options (SAO), Canvassing of Objectives and Values (COV), Evaluation and Re-evaluation of Consequences (ERC), and Search for Information and Unbiased Assimilation of New Information (SIUANI). Each subdimension includes 10 items. Total scores range from 40 to 200; higher scores reflect stronger decision-making. Of the 40 items, 22 are positively worded and 18 are negatively worded (reverse-scored). Minimum and maximum scores for subdimensions are not explicitly defined in the original scale; instead, changes in subdimension scores were analyzed based on increase or decrease. The scale was applied at pretest and posttest.
1 secondary outcome
Case-Specific Knowledge Test Score
Time frame: Immediately after the intervention session (same day)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Jun 2025.
- Individual participant dataSharing planned: No. IPD (Individual Participant Data) will not be shared publicly because the dataset includes identifiable student information such as institutional email addresses and sociodemographic data. However, if requested by a relevant ethics board or regulatory authority, a de-identified version of the dataset may be made available upon justified request and appropriate approval.
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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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing ChatGPT-Supported Case-Based AI Education (C-CASE) and Standard Education in people with Nursing Education Research, run by Yeditepe 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 8 inclusion and 4 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: finished, as of the sponsor's last update on 6 Jun 2025. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Clinical decision-making skills (time frame: From baseline (before intervention) to immediately after the intervention session (same day)). 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 6 Jun 2025.