The project is the adoption of principles of Artificial Intelligence (AI)/Machine Learning and Data Analytics into a traditional nursing research (hypothesis testing) course. Necessitated by the recent entrance of “Big Data” use in health care, there is an urgent need to inject an understanding of the AI/ML techniques necessary to extract, organize and derive meaning from this huge amount of evidence in clinical decision-making. In fact, our department recently approved the adoption of Precision Health (PH) (the clinical use of the plethora of highly-individualized data in health & medical decisions) principles into six undergraduate courses, NURS 4222 (Nursing Research & Evidence-Based Practice) being one of them. The conversion to semesters affords a great opportunity to truly transform the course by blending in this additional necessary content that the graduate nurse is expected to apply. Faculty who are preparing to teach new matters related to “precise care” should avail themselves of gray literature searching, professional networking and incubation to replace, or at least supplement, traditional sources and sourcing such as texts and scholarly literature. These sources were applied creatively in this sample module entitled “Big Data & Evidence-Based Practice”. It is anticipated that our students, by extension and through sharing, our faculty, will develop the knowledge, skill and attitude (KSAs) desirable to adapt to this scientific renaissance known as Precision Health
