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Generative AI Ethics Toolkit for Clinical Prioritization in Nursing Education.

Jennie C De Gagne1, David J Carrejo2, Hyeyoung Hwang3

  • 1School of Nursing, Duke University, Durham, NC, USA.

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Summary

A new toolkit addresses ethical challenges of generative artificial intelligence (GenAI) in nursing education. It helps students and faculty critically evaluate GenAI for bias and ensure equitable patient care.

Keywords:
artificial intelligencebias and equitycritical prioritizationcyberethicsethical pedagogyethics

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Area of Science:

  • Nursing Education
  • Artificial Intelligence Ethics
  • Health Informatics

Background:

  • Generative artificial intelligence (GenAI) adoption in nursing education raises ethical concerns, especially in clinical prioritization.
  • Existing AI literacy resources lack nursing-specific guidance on GenAI risks like bias, inequity, and misinformation.
  • Limited tools exist to support ethical reasoning in GenAI-assisted nursing decision-making.

Purpose of the Study:

  • To adapt an interdisciplinary AI ethics toolkit into a nursing-specific mini-toolkit for educational use.
  • To integrate ethical frameworks (Rest's, cyberethics, Chan's ecological model) for comprehensive ethical reasoning.
  • To develop actionable pedagogical tools for addressing GenAI ethical challenges in nursing.

Main Methods:

  • Adapted an open-access AI ethics learning toolkit using three complementary ethical frameworks.
  • Integrated Rest's Four-Component Model (learner level), cyberethics principles (professional level), and Chan's ecological model (institutional level).
  • Developed case vignettes, reflection prompts, and faculty assessment rubrics focused on GenAI prioritization scenarios.

Main Results:

  • Created a nursing-focused mini-toolkit to scaffold ethical reasoning across individual, professional, and systemic domains.
  • The toolkit promotes critical evaluation of GenAI, fostering dialogue and professional judgment.
  • It provides structured opportunities for recognizing bias and ensuring accountability in GenAI use.

Conclusions:

  • The mini-toolkit translates abstract ethical principles into actionable pedagogical tools for nursing education.
  • It empowers faculty and students to be critical evaluators of GenAI, upholding nursing's commitment to equity and patient care.
  • This methodology offers a model for adapting interdisciplinary frameworks for nursing-specific AI ethics applications.