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Artificial Intelligence Meets Accreditation to Modernize Nursing Education
Jennifer Chicca1, Teresa Shellenbarger, David Chicca
1Author Affiliations: Deputy Director (Dr J. Chicca) and Executive Director (Dr Shellenbarger), National League for Nursing Commission for Nursing Education Accreditation, Washington, DC; Part-time Faculty (Dr J. Chicca), School of Nursing, University of North Carolina Wilmington, Wilmington, NC; and Director (Mr D. Chicca), Office of Engineering Safety, Directorate of Standards and Guidance, Occupational Health and Safety Administration, Washington, DC.
Background:
Large language models, also known as artificial intelligence (AI) models, have become more sophisticated and pervasive in recent years. AI models have many uses and can help nursing faculty in their complex roles.
Problem:
Current literature addresses AI model uses, guidelines, benefits, and challenges for nursing education. However, most literature focuses on the teaching role, with few authors providing guidance for using AI models during quality improvement (QI) activities. These faculty responsibilities are critical yet difficult and could be aided by AI models. However, faculty need guidance to use AI models effectively for these initiatives.
Approach:
Using available literature and author expertise, this article provides guidance for faculty when completing QI and accreditation-related activities, including AI model cautions, ways to maximize output, and approaches for use.
Conclusion:
AI models have the potential to help faculty modernize nursing education as they enhance program monitoring, quality, and student outcomes.
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