Beyond the Prompt: Leveraging Generative AI and Tanner's Model to Decode Clinical Logic in Undergraduate Nursing.

BACKGROUND: Transitioning from theory to clinical practice is a persistent hurdle in undergraduate nursing programs, largely because traditional teaching methods lack the frequent, iterative practice needed to close the practice-readiness gap. PURPOSE: This article describes an innovative pedagogical approach that integrates Generative Artificial Intelligence (GenAI) with Tanner's Clinical Judgment Model to develop nonlinear, algorithmic thinking patterns. METHODS: Using an "If This, Then That" logic framework, students use GenAI as a "thinking partner" to build clinical logic. The process follows Tanner's 4 phases: Noticing, Interpreting, Responding, and Reflecting. Students explore consequential pathways and refine their clinical hypotheses in a low-stakes environment. RESULTS: The AI-augmented iterative algorithm moves students away from rote memorization and toward spatial-logical mapping, promoting metacognition, and transitioning students from linear thinking to complex pattern recognition. CONCLUSIONS: When used as a scaffolded, iterative tool, GenAI enhances clinical synthesis and prepares students for the "chaos of the floor."