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."