Title : The AI-literate nurse: A framework for integrating essential AI competencies into the curriculum
Abstract:
The lack of intentional Artificial Intelligence (AI) instruction within undergraduate curricula exacerbates the “theory-practice” gap, making future nurses inadequately prepared for a healthcare sector experiencing dramatic technological change. Research argues against incorporating stand-alone “AI literacy” instruction, supporting instead the integration of AI competencies throughout a student’s plan of study. This session introduces a curated core of essential AI competencies designed to reduce faculty frustration by allowing instructors to strategically focus their own AI-related learning in an academic environment characterized by information overload, competing time commitments, and limited professional development opportunities. Further, working in conjunction with backward design, these principles streamline curriculum development while ensuring students gain the skillsets needed to succeed in modern healthcare. Twelve curated, student-facing fundamentals are scaffolded across four modules: foundations of Artificial Intelligence; clinical integration and informatics; ethics, equity, and patient advocacy; communication, professionalism, and lifelong learning. The proposed learning objectives promote essential concepts of AI literacy such as comparing generative versus predictive AI, advance clinical integration by asking students to categorize AI products according to their uses in clinical settings, and reinforce the ethical implications of AI by evaluating algorithmic bias and strategies for the uniform and fair implementation of AI healthcare solutions.
