Artificial Intelligence Meets Accreditation to Modernize Nursing Education.

Area of Science:

• Nursing Education
• Artificial Intelligence
• Quality Improvement

Background:

• Large language models (AI) are increasingly sophisticated and pervasive.
• AI offers diverse applications to support nursing faculty roles.
• Existing literature primarily focuses on AI in teaching, not quality improvement.

Purpose of the Study:

• Provide guidance for nursing faculty using AI in quality improvement (QI) and accreditation.
• Address the gap in literature concerning AI for QI activities in nursing education.
• Offer practical advice for effective AI implementation in faculty responsibilities.

Main Methods:

• Literature review synthesizing current knowledge on AI in nursing education.
• Incorporation of author expertise in AI and nursing faculty roles.
• Development of practical guidance for AI application in QI and accreditation.

Main Results:

• AI models can significantly aid nursing faculty in complex roles, particularly in QI.
• Guidance includes cautions, methods to maximize AI output, and specific approaches for use.
• Faculty require structured support to leverage AI effectively for educational initiatives.

Conclusions:

• AI models hold substantial potential to modernize nursing education.
• AI can enhance program monitoring, improve educational quality, and positively impact student outcomes.
• Strategic implementation of AI is key to realizing its benefits in nursing education.