Crystal Salsbury

Nursing Department Bryant & Stratton College, USA

Abstract Title: Integrating AI-Generated Unfolding Case Studies into Undergraduate Nursing Education

Biography:

Research Interest: This presentation highlights the integration of AI-generated, instructor-facilitated unfolding case studies into a prelicensure undergraduate nursing curriculum. Utilizing accessible AI tools such as ChatGPT, Leonardo AI, and Runway ML, faculty designed interactive, immersive video-based simulations aligned with the International Nursing Association for Clinical Simulation and Learning (INACSL) Standards of Best Practice: Simulation®, the National Council of State Boards of Nursing (NCSBN) Clinical Judgment Model, and Bloom’s Taxonomy. These multimedia cases were embedded into classroom instruction and delivered with structured prebriefing, guided decision-making activities, and reflective debriefing to simulate real-world clinical reasoning. This approach provided a scalable, low-cost model to promote clinical judgment development in the absence of increased clinical site availability. Early outcomes suggest improved student engagement, prioritization, and clinical reasoning skills. This session will showcase case examples, outline the development and implementation process, and share qualitative feedback from students and faculty. The presentation will provide practical insights for academic nurse educators seeking to enhance classroom-based clinical learning through innovative, technology-integrated teaching strategies. This method offers a promising approach for nursing programs facing clinical placement challenges while striving to foster safe, competent, and judgment-ready new graduate nurses.