Jul 2026· International Journal of Nursing Information· Vol 5, pp. 18-33· 0 citations· 75 references
TL;DR
This study concludes that successful AI integration must shift from ad-hoc usage toward structured, policy-driven curricular frameworks, and provides a critical strategic benchmark for educational administrators and policy makers to standardize digital health competencies, mitigate regional educational disparities, and safely future-proof the next generation of the healthcare workforce.
Abstract
The rapid expansion of digital health demands a transformation in health workforce education, yet the mapping of Artificial Intelligence (AI) modalities and their structural integration in nursing curricula remains fragmented. To address this gap, this scoping review aimed to systematically map global AI applications in nursing education from 2020 to 2026, offering a distinct contribution by synthesizing pedagogical innovations and structural implementation barriers to guide future curriculum design. Guided by the PRISMA-ScR framework, a systematic screening was conducted across Scopus, PubMed, and CINAHL databases. Results mapped five core AI technologies, including intelligent tutoring systems, virtual patient simulations, adaptive platforms, natural language processing, and predictive analytics, which significantly enhance students' clinical reasoning, critical thinking, and professional competence without compromising patient safety. However, global adoption is geographically skewed and heavily hindered by deficient technological infrastructure, high financial costs, ethical data privacy issues, and a pronounced gap in faculty digital readiness. This study concludes that successful AI integration must shift from ad-hoc usage toward structured, policy-driven curricular frameworks. Ultimately, this review provides a critical strategic benchmark for educational administrators and policy makers to standardize digital health competencies, mitigate regional educational disparities, and safely future-proof the next generation of the healthcare workforce.
A conceptual model requiring prospective validation is proposed as a conceptual model requiring prospective validation, intended to move beyond ad hoc AI use toward structured, competency-based training that strengthens analytical reasoning, ethical judgment, and equity-oriented practice in AI-enabled public health sys...
H. Arab, Ghazal Assaad Mirdad, Maha Abdallah· Frontiers in Public Health· 0 citations
Evidence indicates a transition from isolated AI educational initiatives toward competency-based and longitudinal curriculum integration and effective AI education extends beyond technical literacy and increasingly incorporates ethical, clinical, and professional competencies necessary for responsible AI adoption in he...
M. Morales-Cevallos, Diego Fabián Vique López, Catherine Hortensia Martínez Avalos et al.· Frontiers in Medicine· 0 citations
This commentary proposes a context-sensitive implementation framework for responsible AI integration in medical student education in Somalia and comparable low-resource settings that prioritizes faculty oversight, academic integrity, patient confidentiality, multilingual verification, local clinical validation, low-ban...
Mohamed Mohamud Ali, Abdullahi Abdirahman Omar· Advances in Medical Educatio...· 0 citations
This study employed a scoping review methodology guided by Arksey and O’Malley’s framework to systematically map and synthesize the evolving body of literature addressing AI-supported learning environments in nursing education, contributing to a more coherent understanding of AI’s pedagogical, structural, and equity-re...
Beulah Joy K. Damasco· Multidisciplinary Reviews· 0 citations
Nursing academics appear to adopt AI selectively, prioritising preservation of core professional values while embracing applications perceived to enhance, rather than replace, educational practice, providing evidence for nursing education programs globally regarding faculty development, institutional policy frameworks,...
Natasha Hawkins, Anthea Fagan, Yumiko Coffey et al.· Journal of Advanced Nursing· 0 citations
Current evidence indicates that AI can support learning, clinical reasoning, documentation, patient monitoring, decision support, workflow efficiency, and personalized education; however, evidence from real-world nursing settings remains less mature than technological development.
Natasha Mohit Mahajan· International Journal of Nur...· 0 citations
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