Clinical trial NCT06999447: Impact of AI-Supported Teaching on Clinical Decision-Making in Nursing Students
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
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Nursing Education Research
Keywords: Clinical decision-making
Artificial intelligence
Case-based learning
Surgical nursing
Pediatric nursing
Lead sponsor: Yeditepe University
Sponsor class: OTHER
First posted: 2025-05-31
Enrollment: 66
Sex: ALL
Minimum age (years): 18
Maximum age (years): 25
Standard ages: ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention 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.
Primary purpose: OTHER

Brief 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. 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.


Eligibility criteria: Inclusion Criteria:

* 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

Exclusion Criteria:

* 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

Primary outcomes:
measure: Clinical decision-making skills
timeFrame: From baseline (before intervention) to immediately after the intervention session (same day)
Secondary outcomes:
measure: Case-Specific Knowledge Test Score
timeFrame: Immediately after the intervention session (same day)
Other outcomes:
Locations:
city: Istanbul
country: Turkey
facility: Yeditepe University
state: Atasehir
Study officials:
References: