Impact of AI-powered adaptive ECG simulation training on nurses' knowledge, clinical decision-making and self-efficacy: a mixed-methods randomized controlled trial
Abstract
Aim To evaluate the impact of an AI-Powered ECG Simulation training on nurses' knowledge, clinical decision‑making and self‑efficacy BACKGROUND: Artificial intelligence-based tools demonstrate success in improving cognitive engagement, clinical decision making and skill development. However, the current evidence remains limited in evaluating their effectiveness in improving nurses' knowledge, clinical decision-making and self-efficacy in ECG interpretation. Design Explanatory sequential mixed-methods randomized controlled trial. Methods 64 nurses were randomly assigned to an intervention group and a control group. The AI intervention incorporated interactive ECG scenarios with adaptive difficulty levels, real-time feedback and performance analytics. Outcomes were assessed at baseline and 2 weeks post-intervention using an ECG knowledge test, a clinical decision-making vignette-based test, a decision-making Likert scale and a self-efficacy scale. Allocation concealment and blinding were ensured. Focus group discussions were conducted with 24 nurses to explore their experience of using the AI-Powered ECG simulation training. Results The intervention group achieved higher scores in the knowledge test (p Conclusion AI-Powered ECG simulation training enhanced nurses' knowledge, clinical decision making and self-efficacy. More research is needed to explore the long-term impacts of this education modality on different nursing clinical skills.
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These outcomes are particularly evident when learners receive real-time feedback and repeated practice within psychologically safe learning environments.
“These outcomes are particularly evident when learners receive real-time feedback and repeated practice within psychologically safe learning environments [4,8-11].”
Recent nursing studies of adaptive ECG simulation and AI-enhanced diagnostic narratives reported that intervention participants demonstrated better decision-making, diagnostic accuracy, and self-efficacy than control participants.
“Similar findings have been reported in recent nursing studies of adaptive ECG simulation and AI-enhanced diagnostic narratives, in which intervention participants demonstrated better decision-making, diagnostic accuracy, and self-efficacy than control participants [8,11].”
Studies involving AI-supported virtual, narrative, and generative simulations have reported improvements in self-efficacy, communication knowledge, perceived competence, diagnostic reasoning, and clinical decision-making.
“Studies involving AI-supported virtual, narrative, and generative simulations have reported improvements in self-efficacy, communication knowledge, perceived competence, diagnostic reasoning, and clinical decision-making.”
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Almalki, M. (2026) · Cureus · doi:10.7759/cureus.117315
“Similar findings have been reported in recent nursing studies of adaptive ECG simulation and AI-enhanced diagnostic narratives, in which intervention participants demonstrated better decision-making, diagnostic accuracy, and self-efficacy than control participants [8,11].”
- Authors
- Salem, M. F. A., Sharaf, A. K., & Younis, A. A. A.
- Affiliations
- Alexandria University (EG)
- Corresponding author
- Sharaf, Ahmed Khamis
- Venue
- Nurse Education in Practice
- Publisher
- Elsevier BV
- Published
- 2026-08
- Volume
- 95
- Pages
- 104904
- ISSN
- 1471-5953 (ISSN-L)
- OpenAlex
- W7166699443
- Cited by
- 1
- References
- 6
- Citations by year
- 2026: 1
- FWCI
- 4.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nurse Education in Practice (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Physiology › Simulation-Based Education in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Keywords
- artificial intelligence, ECG interpretation, simulation-based training, adaptive learning, clinical decision-making, self-efficacy, nursing education, real-time feedback, nurses
- MeSH terms
- Clinical Decision-Making, Simulation Training, Adult, Artificial Intelligence, Clinical Competence, Education, Nursing, Continuing, Electrocardiography, Female, Humans, Health Knowledge, Attitudes, Practice, Male, Middle Aged, Focus Groups, Self Efficacy
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