An artificial intelligence use framework for nursing education: Bridging policy and pedagogical implementation

Area of Science:

• Nursing Education
• Artificial Intelligence Integration
• Pedagogical Frameworks

Background:

• Nursing faculty require practical guidance for integrating artificial intelligence (AI) into academic tasks, beyond institutional policies.
• Existing AI policies often overlook crucial aspects like learning outcomes, task purpose, and nursing-specific responsibilities (e.g., patient privacy, clinical judgment).

Purpose of the Study:

• To introduce the AI Use Framework for Nursing Education, a pedagogical tool for responsible AI integration at the task level.
• To operationalize AI use in nursing education by providing task-specific guidance.

Main Methods:

• A 10-member taskforce adapted an existing scale through literature review and faculty consultation.
• The framework was refined using four guiding principles: transparency, learning outcome alignment, nursing context, and developmental scaffolding.
• Faculty input was gathered across pre-licensure to doctoral nursing programs.

Main Results:

• The framework categorizes AI use into six levels: No AI, AI-Assisted Editing, AI-Assisted Planning, AI-Assisted Content Creation (limited/extensive), AI System Evaluation, and AI Application Design.
• Key features include safeguards for protected health information (PHI)/personally identifiable information (PII), flexible documentation based on learning outcomes, and Bloom's-aligned separation of AI evaluation and creation.

Conclusions:

• The AI Use Framework bridges institutional policy and pedagogical practice with healthcare-specific adaptations.
• Successful implementation necessitates AI literacy education, institutional support, and faculty development.
• This framework enables nursing education to lead intentional AI integration and prepare students for AI-enabled healthcare environments.