Artificial Intelligence-Assisted Learning in Emergency Nursing Education: A Scoping Review - Nurwahidah (Al-Hijrah Indonesia, 2026)
Background: Artificial intelligence (AI) is increasingly used to support nursing education, particularly in preparing learners to manage emergency and critical care situations. However, evidence from randomized controlled trials (RCTs) remains fragmented. Objective: To map and characterize RCT evidence on AI-assisted learning in nursing education, with emphasis on emergency, resuscitation, and critical-care contexts. Methods: Scoping review following the PRISMA-ScR guideline and the Joanna Briggs Institute framework. Data Sources: PubMed/MEDLINE, Scopus-indexed journals, ProQuest, and reference-list searching were conducted through July 2026. The articles screened studies and extracted data on study characteristics, interventions, comparators, and educational outcomes. Results: Fifteen RCTs (2016–2026) involving over 1,300 nurses and nursing students from nine countries were included. Nine studies evaluated AI-assisted learning in general nursing education, while six focused on emergency, resuscitation, or critical care. AI-based interventions included generative AI, chatbots, intelligent tutoring systems, virtual and augmented reality, and AI-integrated simulation. Most studies reported improvements in knowledge, clinical skills, or learner satisfaction. However, several found no significant advantage over conventional teaching and reported increased cognitive load or anxiety. Conclusion: AI-assisted learning has the potential to enhance emergency and critical care nursing education, particularly in psychomotor skills and clinical decision-making. Nevertheless, current evidence is limited by heterogeneous interventions, small single-center trials, and short follow-up, highlighting the need for larger multicenter RCTs with standardized outcome measures.