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Teaching Nursing Students Effective Artificial Intelligence Prompt Engineering: The CARE Framework.

Mary E Bester1, Kathryn Zeigler

  • 1Author Affiliations: School of Nursing, Savannah, Georgia.

Nurse Educator
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PubMed
Summary

This study introduces the CARE Prompt Engineering Framework to help students improve critical thinking and clinical decision-making with artificial intelligence (AI). Nurse educators can use this framework to teach effective AI prompting and ensure responsible AI use in healthcare education.

Keywords:
AI-assisted learningclinical judgment developmentprompt engineering in nursing educationreflective practicerole modeling

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Area of Science:

  • Health Professions Education
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support

Background:

  • Effective artificial intelligence (AI) prompting is crucial for students to leverage AI for enhanced critical thinking and clinical decision-making.
  • The integration of AI in education necessitates structured approaches to guide student interaction and learning.

Purpose of the Study:

  • To introduce and describe the CARE (Context, Action, Role, Expectation) Prompt Engineering Framework.
  • To provide nurse educators with a systematic method for teaching effective AI prompting to students.
  • To emphasize the importance of the human connection in AI-assisted learning and clinical practice.

Main Methods:

  • Development of the CARE Prompt Engineering Framework.
  • Emphasis on maintaining the "human connection" within AI interactions.
  • Discussion of each CARE element with remedial prompting strategies for improved AI output.
  • Process of verifying AI outputs, revising context, and submitting follow-up actions.

Main Results:

  • Students utilize their skills to query AI for information, requiring effective prompting techniques.
  • Instructors play a vital role in modeling responsible and effective AI-prompt engineering.
  • The CARE framework facilitates the refinement of AI outputs through verification, revision, and follow-up actions.

Conclusions:

  • The CARE framework offers a systematic approach for nurse educators to teach clinical decision-making skills.
  • It supports faculty in role-modeling effective AI prompt engineering.
  • Ensures students develop and utilize effective AI prompts for enhanced learning and practice.