Beyond literacy to clinical competency: A framework for integrating generative AI into nursing education

Area of Science:

• Nursing Education
• Artificial Intelligence in Healthcare
• Clinical Informatics

Background:

• Health systems are integrating generative artificial intelligence (GenAI) into clinical workflows, posing risks like hallucinations and automation bias.
• Current nursing education often overlooks clinical preparedness for GenAI, focusing instead on academic integrity.
• Nurses are vital for patient safety in AI-augmented care but require enhanced GenAI competencies.

Purpose of the Study:

• Propose the Generative AI Nursing Competency (GANC) framework for nursing education.
• Provide a standards-aligned pathway for preparing nurses to use GenAI responsibly.
• Equip nurses with skills to critically appraise and manage AI-generated outputs in clinical settings.

Main Methods:

• Developed the GANC framework, outlining seven core competency domains.
• Specified observable indicators and assessment exemplars, including simulation and OSCE stations.
• Integrated guidance on managing GenAI risks and emphasizing manual-mode readiness.

Main Results:

• The GANC framework offers a structured approach to GenAI competency development for nurses.
• It addresses critical areas such as evidence verification, clinical reasoning, and ethical accountability.
• The framework supports the cultivation of nurses as accountable clinical validators.

Conclusions:

• Strengthening GenAI competencies in nursing education is crucial for safe AI integration.
• The GANC framework provides a practical roadmap for educators and health systems.
• Nurses are essential gatekeepers for patient safety in AI-augmented healthcare environments.