Artificial Intelligence-Ready Doctor of Nursing Practice Education: A Competency-Based Approach

Authors

• Veronica Ronnie Quattrini, Doctor of Nursing Practice, University of Maryland School of Nursing, Baltimore, MD, USA [email protected].
• Lisa Taylor, Graduate Programs in Nursing Practice, Texas Wesleyan University, Fort Worth, TX, USA.
• Donna Lynch-Smith, College of Nursing, University of Tennessee Health Science Center, Memphis, TN, USA.
• Tarnisha Hemphill, Midwifery Program, School of Nursing, The George Washington University, Washington, DC, USA.
• Tiffany Gibson, Advanced Nursing Practice Program, School of Nursing, University of North Carolina at Greensboro, Greensboro, NC, USA.
• Amy Ford, College of Nursing, University of Nebraska Medical Center, Omaha, NE, USA.
• Emily Fox, School of Nursing, University of Texas at Tyler, Tyler, TX, USA.
• Amanda Roesch, Doctor of Nursing Practice Program, University of Maryland, Baltimore, MD, USA.
• Tene Turner, Advanced Nursing Practice Program, University of North Carolina at Greensboro, Greensboro, NC, USA.
• Wanda Hilliard, Texas Tech University Health Science Center, Lubbock, TX, USA.
• Angel Anthamatten, School of Nursing, Vanderbilt University, Nashville, TN, USA.

Document Type

Journal Article

Publication Date

4-29-2026

Journal

Journal of doctoral nursing practice

Volume

19

Issue

1

DOI

10.1891/JDNP-2025-0051

Keywords

Doctor of Nursing Practice (DNP); advanced practice registered nurses (APRNs); artificial intelligence (AI); nursing education

Abstract

Artificial intelligence (AI) is rapidly transforming nursing education and clinical practice. As AI becomes increasingly embedded in health care delivery, integrating AI competencies into Doctor of Nursing Practice (DNP) education is essential to prepare advanced practice registered nurses (APRNs) to utilize these tools effectively and ethically. This manuscript examines the integration of AI into DNP education, addressing policy implications, best practices, and strategies to prepare APRNs for leadership in AI-enhanced environments. A review of institutional innovations and faculty strategies demonstrates the application of AI in nursing education through adaptive learning platforms, virtual simulations, predictive analytics, and AI-driven clinical decision support systems. Case exemplars highlight implementation approaches and educational outcomes. AI-enhanced tools have demonstrated several benefits, such as improved student engagement, individualized learning, and enhanced clinical reasoning. Case-based reflections revealed enhanced decision-making, mentorship, and student competency tracking. Limitations and potential risks of AI are also identified. Key guiding principles include evaluating existing competencies within the context of AI capabilities, defining emerging AI needs, supporting faculty development through AI training, and advancing policies for responsible and ethical AI use. The nursing profession is well recognized for its innovative approach to adopting new technologies. Embedding AI into DNP education requires intentional curricular reform, strong leadership support, and ethical oversight to ensure sustainable adoption. Nursing faculty must champion the strategic and responsible use of AI to prepare APRNs for evidence-based, technology-driven practice. DNP-prepared nurses, with their expertise in quality improvement and ethical practice, are uniquely positioned to shape the development and implementation of AI tools.

APA Citation

Quattrini, Veronica Ronnie; Taylor, Lisa; Lynch-Smith, Donna; Hemphill, Tarnisha; Gibson, Tiffany; Ford, Amy; Fox, Emily; Roesch, Amanda; Turner, Tene; Hilliard, Wanda; and Anthamatten, Angel, "Artificial Intelligence-Ready Doctor of Nursing Practice Education: A Competency-Based Approach" (2026). GW Authored Works. Paper 9007.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/9007

Department

Nursing Faculty Publications