From Cases to Confidence: Developing Diagnostic Reasoning Skills Through Collaborative Learning in Graduate Nursing Education.

Journal: Nursing education perspectives
Published Date:

Abstract

Teaching diagnostic reasoning to graduate nursing students is both essential and challenging, particularly in asynchronous environments where absence of real-time interaction requires innovative strategies to engage students and support the development of clinical application skills. The innovation detailed in this article demonstrates a collaborative learning approach using artificial intelligence (AI)-generated cases to simulate clinical scenarios and cultivate diagnostic reasoning in first-year advanced practice provider students. Pre- and post-assessments showed increased confidence; qualitative reflections emphasized the value of peer collaboration. This innovative model demonstrates how collaborative learning, supported by AI, can transform clinical education in asynchronous nursing programs.

Authors

  • Michelle L Jackson
    About the Author Michelle L. Jackson, PhD, RN, is associate professor and director, Nurse Practitioner Program, Point Loma Nazarene University School of Nursing, San Diego, California. The author received a $500 Pedagogical Enrichment Grant for Inclusive Practice to support time spent researching, reflecting, and developing inclusive classroom strategies. ChatGPT was used to edit this manuscript. All content was reviewed, revised, and approved by the author in accordance with ethical publication standards. For more information, contact Dr. Jackson at mjackso2@pointloma.edu.

Keywords

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