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

NCT07838792 — Not applicable, Completed, 127 participants, Chang Gung University of Science and Technology

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Effects of Integrating a Reasoning Grid and ChatGPT on Clinical Reasoning in Nursing Students

Registry	NCT07838792
Status	Completed
Phase	Not applicable
Study type	Interventional; randomized, parallel, masking: double, other
Enrollment	127 (Actual)
Sponsor	Chang Gung University of Science and Technology (Other)
Conditions	Clinical Reasoning in Nursing Education
Interventions	Tanner-Structured Socratic AI Scaffolding, General-Purpose Generative AI (Other)
Dates	start 2025-09-15, primary completion 2026-02-05, completion 2026-02-05, first posted 2026-09-24, last updated 2026-10-22
Locations	1 site in Taiwan

-Summary

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.

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+Arms and interventions

Arm	Type	Interventions	Description
Tanner-Structured Socratic AI Scaffolding	Experimental	Other: 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.
General-Purpose Generative AI	Active comparator	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.

Tanner-Structured Socratic AI Scaffolding (Other): Participants used a research-team-developed AI chatbot structured around Tanner's Clinical Judgment Model. The chatbot guided participants sequentially through Noticing, Interpreting, Responding, and Reflecting using Socratic questions, graduated hints, metacognitive prompts, and constructive feedback rather than providing direct answers. The scaffolding was informed by learner-identified barriers to clinical reasoning.
General-Purpose Generative AI (Other): Participants used freely accessible general-purpose generative AI tools as they normally would for learning. They formulated their own task-focused queries and received no Tanner-structured sequence or standardized Socratic prompts.

+Outcome measures

Primary outcomes

Clinical Reasoning Performance [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 (0-3 points), ranked them in order of urgency (0-3 points), and proposed three appropriate nursing interventions for each problem (0-9 points). Total scores ranged from 0 to 15, with higher scores indicating better clinical reasoning performance.

Secondary outcomes

Case-Based Knowledge [Baseline and immediately after the intervention]: Case-based knowledge was assessed using a five-item, scenario-based, single-best-answer knowledge test covering infant fever management across five domains: assessment, problem identification, goal setting, intervention, and evaluation. Each correct answer was awarded 1 point, and incorrect answers received 0 points. Total scores ranged from 0 to 5, with higher scores indicating greater case-based knowledge.
Clinical Reasoning Confidence [Baseline and immediately after the intervention]: Clinical reasoning confidence was assessed using a single-item, self-reported 5-point rating scale measuring participants' confidence in clinical reasoning and decision-making related to a pediatric fever case. Scores ranged from 1 (very low confidence) to 5 (very high confidence), with higher scores indicating greater clinical reasoning confidence.

+Eligibility

Sex: All, accepts healthy volunteers

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.

+Locations (1)

Facility	City	Country	Status
Chang Gung University of Science and Technology	Taoyuan	Taiwan