Overview

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This course helps nurses read AI outputs critically, weighing what shaped them, where they're likely to fall short, and what they can and can't tell you about the patient in front of you. Through the four-bucket framework and an AI-powered discussion activity, learners will practice tracing an AI output back to the training data and process that produced it, interpreting outputs with the right mix of confidence and skepticism, and recognizing bias as a predictable pattern that shows up when training data doesn't fully reflect the patients a tool serves. In support of improving patient care, Duke University Health System Clinical Education and Professional Development is accredited by the American Nurses Credentialing Center (ANCC), the Accreditation Council for Pharmacy Education (ACPE), and the Accreditation Council for Continuing Medical Education (ACCME), to provide continuing education for the health care team. The designation was based upon the quality of the educational activity and its compliance with the standards and policies of the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC). Duke University Health System Department of Clinical Education and Professional Development designates this activity for up to 9 credit hours for nurses. Nurses should claim only credit commensurate with the extent of their participation in this activity. Upon completion of all three courses (Seeing AI in Nursing Practice, Evaluating AI in Nursing Practice, and Applying AI in Nursing Practice) in the AI in Nursing Practice: Foundations for Quality and Safety Specialization, you will be eligible to apply for continuing education credits. Please note that continuing education credit rosters will be collected quarterly.

Syllabus

How AI Learns from Data
- Before you can evaluate an AI output, it helps to understand how the model behind it was developed. This module introduces how AI systems learn from data, including the roles of training, labels, and different learning approaches. You’ll consider how these choices shape what a model can recognize, what it may miss, and how its outputs should be interpreted.
From Data to Output
- This module follows the path from clinical documentation to the output that appears on your screen. Along the way, you’ll develop vocabulary to help you interpret AI outputs thoughtfully by understanding what they may tell you, what they may leave out, and when to approach them with additional questions.
Where AI Falls Short
- Understanding how AI systems work is one part of evaluating their outputs. This module explores the conditions that can make AI tools less reliable and introduces bias as a predictable pattern that may affect how models perform across different patient populations.
Putting It All Together
- This module closes the course and hands the learning back to you. You'll apply the evaluative vocabulary from this course to a real tool from your own workflow before completing the final assessment.