Effects of Integrating a Reasoning Grid and ChatGPT on Clinical Reasoning in Nursing Students
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 127
- 试验地点
- 1
- 主要终点
- Clinical Reasoning Performance
研究概览
简要总结
The goal of this study was to compare two approaches to using generative artificial intelligence (AI) to support clinical reasoning in undergraduate nursing students. The study examined whether a theory-guided Socratic AI scaffold based on Tanner's Clinical Judgment Model could better support clinical reasoning, case-based knowledge, and confidence than the naturalistic use of general-purpose generative AI.
Participants were undergraduate nursing students enrolled in a pediatric nursing course. Before the intervention, students' perceived barriers to clinical reasoning were identified and used to inform the theory-guided AI scaffold. Classes were then assigned to either Tanner-Structured Socratic AI Scaffolding or General-Purpose Generative AI. Both groups worked with the same pediatric fever case for the same amount of time.
Students in the Tanner-Structured Socratic AI Scaffolding group received step-by-step guidance through Noticing, Interpreting, Responding, and Reflecting using Socratic questions, hints, feedback, and prompts for reflection. Students in the General-Purpose Generative AI group used freely available generative AI tools as they normally would for learning.
The study compared the two groups on clinical reasoning performance, case-based knowledge, and confidence in clinical reasoning.
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
- Double (Investigator, Outcomes Assessor)
入排标准
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Undergraduate nursing students enrolled in the required Pediatric Nursing course.
- •Completion of core medical-surgical nursing courses.
- •No prior practicum experience in specialized pediatric units.
排除标准
- •Students repeating the Pediatric Nursing course.
- •Students with prior pediatric specialty rotation experience.
研究组 & 干预措施
Tanner-Structured Socratic AI Scaffolding
Participants used a theory-guided AI chatbot structured around Tanner's Clinical Judgment Model. The chatbot guided participants through Noticing, Interpreting, Responding, and Reflecting using Socratic questions, graduated hints, metacognitive prompts, and constructive feedback rather than providing direct answers.
干预措施: Tanner-Structured Socratic AI Scaffolding (Other)
General-Purpose Generative AI
Participants used freely accessible general-purpose generative AI tools as they normally would for learning. They formulated their own task-focused queries without a Tanner-structured sequence or standardized Socratic prompts.
干预措施: General-Purpose Generative AI (Other)
结局指标
主要结局
Clinical Reasoning Performance
时间窗: Immediately after the intervention
Clinical reasoning performance was assessed using the Clinical Reasoning Performance Rubric (CRPR), a 15-point criterion-referenced rubric. Participants identified three priority nursing problems, ranked them by urgency, and proposed three evidence-based interventions for each problem. Total scores ranged from 0 to 15, with higher scores indicating better clinical reasoning performance.
次要结局
- Case-Based Knowledge(Baseline and immediately after the intervention)
- Clinical Reasoning Confidence(Baseline and immediately after the intervention)
研究者
Ying-Mei Liu
Associate Professor
Chang Gung University of Science and Technology