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

Area of Science:

• Nursing Education
• Medical Simulation
• Artificial Intelligence in Healthcare

Background:

• Teaching diagnostic reasoning to graduate nursing students is challenging, especially in asynchronous learning environments.
• Lack of real-time interaction necessitates innovative strategies for engagement and skill development.

Purpose of the Study:

• To demonstrate a collaborative learning approach using AI-generated cases to improve diagnostic reasoning in advanced practice provider students.
• To evaluate the impact of AI-supported collaborative learning on student confidence and clinical application skills.

Main Methods:

• Utilized artificial intelligence (AI) to generate simulated clinical scenarios for first-year advanced practice provider students.
• Implemented a collaborative learning model within an asynchronous educational setting.
• Conducted pre- and post-assessments to measure changes in student confidence and diagnostic reasoning skills.

Main Results:

• Pre- and post-assessments indicated a significant increase in student confidence.
• Qualitative reflections highlighted the perceived value of peer collaboration in the learning process.
• The AI-driven collaborative model proved effective in enhancing diagnostic reasoning skills.

Conclusions:

• Collaborative learning, augmented by AI-generated cases, offers a transformative approach to clinical education in asynchronous nursing programs.
• This innovative model effectively addresses the challenges of teaching diagnostic reasoning in remote learning environments.
• The study underscores the potential of AI to enhance nursing education and prepare future advanced practice providers.