Advancing RRNAs’ Learning Through Generative Artificial Intelligence: Integrating Prompt Engineering Training and Acceptable Use Policy in Nurse Anesthesia Education (1 MAC Ed CE)

This session introduces a dynamic and learner-centered approach to integrating Generative Artificial Intelligence (GenAI) into nurse anesthesia education, specifically designed to support Resident Registered Nurse Anesthetists (RRNAs). Attendees will explore three core components of GenAI integration approach: prompt engineering training, contextualized policy guidance, and RRNA student perspectives.
First, participants will engage with practical tools and techniques for prompt engineering, including step-by-step strategies, prompt frameworks (e.g., Role-Goal-Context, CARE, RACE), and customizable patterns (e.g., Persona, Recipe, Interview). These methods equip RRNAs with skills to generate effective prompts for writing, research, and clinical decision-making.
Second, the session highlights the Acceptable Use Policy for GenAI developed by the VCU Department of Nurse Anesthesia. This policy includes definitions, the ACHIEVE principles, permitted and prohibited uses, and guidelines for data privacy, HIPAA/FERPA compliance, and citation. Learners will evaluate ethical case scenarios to understand how policy regulates GenAI use in clinical and academic settings.
Finally, the session incorporates qualitative data from 63 RRNAs whose reflections on GenAI use were analyzed using the FAME framework (Feasibility, Appropriateness, Meaningfulness, Economics). Learners will reflect on key themes—such as usability, academic value, time savings, and ethical concerns—and use these insights to inform and adapt GenAI policies and training in their own institutions.
This session offers a comprehensive model for responsible and innovative GenAI integration, balancing technical proficiency, policy awareness, and learner voice to advance nurse anesthesia education.