Theory-Guided Socratic AI Scaffolding for Clinical Reasoning in Nursing Students

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.

Inclusion Criteria: * 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. Exclusion Criteria: * Students repeating the Pediatric Nursing course. * Students with prior pediatric specialty rotation experience.