This structured, task-level guidance positions nursing education to lead intentional AI integration while preparing students for AI-enabled practice environments.
Abstract
Background
Artificial intelligence (AI) is transforming higher education and healthcare, yet nursing faculty lack practical guidance for determining appropriate AI use in specific academic tasks. Institutional AI policies establish boundaries but rarely address learning outcomes, task purposes, or nursing-specific responsibilities such as patient privacy, clinical judgment development, and professional accountability.
Purpose
To present the AI Use Framework for Nursing Education, a six-category pedagogical framework that operationalizes responsible AI integration at the task level.
Methods
A 10-member taskforce adapted the Artificial Intelligence Assessment Scale through targeted review of existing frameworks, iterative refinement guided by four principles (transparency over detection, learning outcome alignment, nursing-specific contextualization, and developmental scaffolding), and consultation with faculty across pre-licensure through doctoral programs.
Results
The framework provides six categories: (a) No AI, (b) AI-Assisted Editing and Formatting, (c) AI-Assisted Planning and Ideation, (d) AI-Assisted Content Creation with limited and extensive subcategories, (e) AI System Evaluation and Research, and (f) AI Application Design and Development. Three distinguishing features include embedded protected health information/personally identifiable information safeguards, differentiated process evidence recommendations enabling faculty to tailor documentation requirements to learning outcomes and task complexity, and Bloom's-aligned separation of AI evaluation from AI creation activities.
Conclusion
The framework bridges institutional policy and pedagogical practice through healthcare-specific adaptations. Implementation requires comprehensive infrastructure including AI literacy education, institutional guidelines, and faculty support. This structured, task-level guidance positions nursing education to lead intentional AI integration while preparing students for AI-enabled practice environments.
A concise, nursing faculty-centered AI development checklist for responsible AI integration in teaching and assessment and simplifies steps for understanding new technologies, adds to research on competencies, and improves instructional efficiency is provided.
L. Lioce, Tonya Rutherford-Hemming, Tonya Breymier· The Journal of Nursery Educa...· 1 citation
This curriculum offers a generalizable, no-cost model for closing the AI evaluation skills gap in healthcare education, combining interactive correction with a mandatory, learner-defined capstone project.
Hiba Hamdar· Journal of Medical Education...· 0 citations
Artificial intelligence should ultimately enhance rather than replace nurses’ clinical judgement, ethical responsibility, communication, and compassionate care, and professional practice.
Nan Zhang· Advances in Vocational and T...· 0 citations
It is argued that AI-focused CME should be designed as a longitudinal organizational learning system rather than a discrete instructional event, and implications are offered for technology integration, faculty development, and organizational learning in clinical education.
T. Murphy, Rob E. Carpenter· International Journal on Int...· 1 citation
The findings highlight the need for longitudinal, program-wide integration of AI and digital health competencies, explicit alignment with recognised competency frameworks, and investment in educator capability to prepare graduates for safe and effective practice in increasingly AI-enabled healthcare environments.
Dianne Stratton-Maher, Thanveer Shaik, Yan Li et al.· Nurse Education Today· 1 citation
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