ReviewSAGE open nursing
Redesigning Nursing Curricula for Human-AI Collaboration Using a Fifth Industrial Revolution Framework: Discursive Paper.
Review in SAGE open nursing. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- The Transformation of Empathic Care in the Digital Age: A Scoping Review on Digital Empathy from a Nursing Perspective.Healthcare (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.