Use Case of Generative AI for Automating Reports on Nursing Students’ Clinical Rotations

Clinical rotations are vital for developing nursing skills, clinical judgment, and readiness for professional practice. Nursing students in a large southeastern U.S. health system completed surveys after their rotations (Dedicated Education Unit, Precepted, and Cohort models). An AI-driven workflow was created to analyze both qualitative and quantitative feedback, generating structured reports for healthcare and academic partners. The system reduced manual reporting workload while delivering timely, actionable insights on student experiences, strengths, and areas for improvement. Findings highlight how integrating generative AI can streamline administrative processes, support evidence-based decision-making, and strengthen academic–practice partnerships in nursing education.

Describe the role and significance of clinical rotations in nursing education and their impact on student readiness for professional practice.

Explain how generative AI can be applied to automate reporting workflows and synthesize qualitative and quantitative feedback from nursing student clinical rotations models.

Design strategies to integrate AI-generated reporting into academic–practice partnerships to support evidence-based improvements in nursing education.