AI-Supported Teaching in Pediatric Surgical Emergency Case Management: Effects on Nursing Students' Knowledge and Clinical Decision-Making in a Randomized Controlled Study
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 66
- 试验地点
- 1
- 主要终点
- Clinical decision-making skills
研究概览
简要总结
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Other
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 25 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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
排除标准
- •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
研究组 & 干预措施
C-CASE Group (AI-Supported Case-Based Education)
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.
干预措施: ChatGPT-Supported Case-Based AI Education (C-CASE) (Other)
Standard Education Group (Traditional Case-Based Learning)
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.
干预措施: Standard Education (Other)
结局指标
主要结局
Clinical decision-making skills
时间窗: 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.
次要结局
- Case-Specific Knowledge Test Score(Immediately after the intervention session (same day))
研究者
Gokce Naz Cakir
Graduate Research Assistant
Yeditepe University