Aug 2026· Nurse Education Today· Vol 167, pp.
107318
· 0 citations· 31 references
Medicine
TL;DR
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.
Abstract
Background
The rapid digital transformation of healthcare is reshaping the capabilities required of the nursing workforce. Technologies such as artificial intelligence (AI), clinical decision-support systems, and digital health technologies are increasingly embedded within healthcare delivery. Consequently, professional and policy frameworks increasingly emphasise the need for nurses to develop AI and digital health competencies. However, the extent to which these capabilities are integrated within undergraduate nursing education remains unclear.
Aim
To map and synthesise the evidence on how artificial intelligence and digital health competencies are integrated within undergraduate nursing education to prepare graduates for practice in increasingly digital healthcare environments.
Methods
A scoping review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) was conducted across six databases. Studies examining the integration of AI or digital health competencies within undergraduate nursing education were included. Data were charted and synthesised using thematic analysis.
Results
Seventeen studies met the inclusion criteria. Integration of AI and digital health within undergraduate nursing education remains fragmented and largely exploratory, most commonly occurring through isolated courses, pilot initiatives, or educator-led activities rather than coordinated, program-wide curriculum design. Educational approaches emphasise scaffolded learning through case-based learning, simulation, and reflective activities to support the development of AI and digital health competencies. These competencies are predominantly conceptualised as professional, ethical, and evaluative capabilities, emphasising critical thinking, accountability, and patient-centred care rather than technical mastery. While students and educators generally report positive attitudes towards AI technologies, variation in readiness, confidence, and digital literacy persists. Systemic barriers, including limited faculty preparedness, curriculum constraints, governance ambiguity, and resource disparities, continue to limit the sustainable integration of AI and digital health competencies within nursing curricula.
Conclusion
These 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.
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.
Dr. S. Lakshmi· International Journal of Nur...· 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, and curriculum design strategies integrating technological advancement whilst maintaining person-centred values.
Natasha Hawkins, Anthea Fagan, Yumiko Coffey et al.· Journal of Advanced Nursing· 0 citations
The evidence showed that, virtual reality simulations with varying immersive abilities were the most popular innovative educational technology used in undergraduate mental health nursing education and both immersive and non‐immersive VRS were equally effective in improving student learning experiences compared to traditional learning modalities.
Sini Jacob, Sebastian Samuel, Greety Antony et al.· International Journal of Men...· 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 healthcare.
M. Morales-Cevallos, Diego Fabián Vique López, Catherine Hortensia Martínez Avalos et al.· Frontiers in Medicine· 0 citations
OBJECTIVE
This scoping review will map the scope and nature of the available evidence on the use of artificial intelligence-based clinical decision support systems (AI-CDSSs) for undergraduate nursing students.
INTRODUCTION
AI is increasingly integrated into nursing education to support students' clinical reasoning and decision-making. Yet the literature on AI-CDSSs remains fragmented and heterogeneous, with variability in technologies, educational applications, and reported outcomes, as well as concerns regarding bias and ethical implications. To date, no comprehensive synthesis has mapped the evidence on AI-CDSS use for undergraduate nursing students.
ELIGIBILITY CRITERIA
This review will map empirical research involving undergraduate nursing students using AI-based tools to support clinical reasoning, diagnostic reasoning, prioritization, or the nursing process. It will consider sources published in English, Spanish, French, Italian, or Chinese, provided that the title and abstract are in English and the record is considered relevant. Searches will be limited to sources published from 2017 onward, aligning with the rapid evolution and only recent widespread availability of modern generative AI tools.
METHODS
This review will follow the JBI methodology for scoping reviews. Searches will be conducted in PubMed, Scopus, CINAHL (EBSCOhost), the Cochrane Library, ERIC (EBSCOhost), and Web of Science Core Collection, while gray literature will be retrieved from ProQuest Dissertations and Theses Global (ProQuest), OpenAlex, Google Scholar, and professional body and regulatory sources. Screening will be performed independently by 2 reviewers, and data will be synthesized descriptively and thematically. Reporting will be documented according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews.
REVIEW REGISTRATION
OSF https://osf.io/eh9bt/.
Francesco Scerbo, G. Caggianelli, Simone Ciucciarelli et al.· JBI Evidence Synthesis· 0 citations
In the context of digital disruption, nursing education requires approaches that integrate digital intelligence (DI) with professional competencies while addressing limitations of traditional simulation-based learning (SBL), which is often resource-intensive and constrained in large classes. This study developed and evaluated “Nurse Sims,” a progressive web application that integrates with an Internet of Things (IoT)-enabled humanoid manikin (ESP32) to support real-time clinical training and automated feedback for nursing students. A four-phase research and development design was employed. Phase 1 assessed learning needs among 462 nursing students in Thailand using questionnaires and PNIModified analysis. Phase 2 involved the development of the SBL model and technological system. Phase 3 evaluated the intervention with 31 nursing students and six instructors using repeated-measures analysis of variance across three assessment points (Situations 1, 3, and 5). Phase 4 involved expert validation of the innovation. Results show a high need for SBL (PNImodified = 0.796). Significant improvements were observed in professional competencies (ηp² = .911) and DI (ηp² = .933) (p < .001). Students reported high acceptance of the system (M = 4.52, SD = 0.49), while experts rated the innovation as highly appropriate (M = 4.76, SD = 0.20). Qualitative feedback indicated improved digital self-efficacy and engagement with technology-enhanced learning. The findings suggest that IoT-integrated simulation systems can enhance nursing competencies, and DI and may serve as a scalable model for nursing education in resource-constrained settings.
Prakob Koraneekij, Sresuda Wongwiseskul· Electronic Journal of e-Lear...· 0 citations
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