ChatGPT-Assisted PBL Teaching Model in Pediatric Nursing Undergraduate Internship: A Randomized Controlled Pilot Study (Preprint)

BACKGROUND Traditional Problem-Based Learning (PBL) in pediatric nursing education often uses static cases and lacks personalized, real-time feedback. The integration of generative AI like ChatGPT could address these limitations, yet its systematic application in nursing internships remains understudied. OBJECTIVE To explore the effectiveness and feasibility of a ChatGPT-assisted Problem-Based Learning (PBL) model in pediatric nursing undergraduate internship education, providing empirical evidence for artificial intelligence(AI) nursing education integration. METHODS A single-center, assessor-blinded randomized controlled pilot study was conducted. Eighty-four interns were randomly assigned to the ChatGPT-PBL group (n=42) or traditional PBL group (n=42) at a 1:1 ratio. Based on traditional PBL, the experimental group integrated ChatGPT-4 to construct a "instructor-student dual-layer" supported PBL teaching framework, including dynamic generation of personalized clinical cases, provision of real-time operational feedback, and decision-making simulation training. The traditional PBL group received standardized traditional PBL teaching. The intervention lasted for 4 weeks. The primary outcome measures included theoretical assessment scores, Objective Structured Clinical Examination (OSCE) scores, Chinese Version of Critical Thinking Disposition Inventory (CTDI-CV) scores, Holistic Clinical Assessment Tool for Nursing Undergraduates (HCAT) scores, and teaching satisfaction. RESULTS Post-intervention, the theoretical score of the ChatGPT-PBL group was significantly higher than that of the traditional PBL group (82.76±5.02 vs 71.88±5.88, P