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Redesigning Nursing Curricula for Human-AI Collaboration Using a Fifth Industrial Revolution Framework: Discursive
Joseph Andrew Pepito1, Faustino Jerome Babate2, Judith D Ismael3
1Cebu Doctors University, Cebu, Philippines.
Introduction:
Artificial intelligence (AI) is increasingly embedded in healthcare, reshaping clinical decision-making, care delivery, and professional nursing roles. Although nursing curricula now include digital health and informatics, they remain largely focused on technical skills and insufficiently prepare nurses for ethical, relational, and collaborative engagement with intelligent systems. As AI becomes an active participant in care processes, nursing education requires a human-centered framework that supports meaningful human-AI collaboration. This article aims to develop a theoretically grounded Fifth Industrial Revolution (5IR) framework to guide nursing curriculum redesign for ethical and effective collaboration with AI.
Method:
A discursive conceptual approach was employed, integrating conceptual analysis with a structured but nonsystematic review of interdisciplinary peer-reviewed literature published between 2019 and 2025. The analysis was guided by 5IR theory, post digital theory, and sociotechnical systems thinking. Through iterative thematic synthesis, the literature was examined to identify core competencies, pedagogical strategies, and institutional conditions necessary for AI-integrated nursing education.
Results:
Seven interrelated competency domains were identified through conceptual synthesis: technological fluency and algorithmic literacy, ethical and legal acumen, digital empathy and relational intelligence, critical thinking in AI-supported decision-making, interdisciplinary collaboration and systems thinking, adaptive learning and postdigital literacy, and cultural competence in global AI contexts. These findings informed the development of the 5IR Human-AI Collaborative Nursing Education Model, which comprises five interdependent components: sociotechnical foundations, a human-AI collaborative competency core, transformative curriculum pedagogies, a multistakeholder codesign ecosystem, and adaptive evaluation with continuous feedback.
Conclusion:
The findings highlight a persistent gap between current nursing curricula and the ethical, relational, and sociotechnical demands of AI-enabled healthcare. The proposed model offers an adaptable, human-centered framework that positions nurses as ethical collaborators and codesigners of intelligent care systems, providing a foundation for future curriculum innovation, empirical research, and policy development.
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