Generative AI Ethics Toolkit for Clinical Prioritization in Nursing Education

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.