Transforming Nursing Simulation Education: An Action Learning Framework Integrating Clinical Practice, Curriculum Design, Skills Competition, and Certification Standards via Intelligent Technologies

Jing Zhang(QingDao BinHai University)
Yudong Zhao()
Na Li()

Abstract

This study proposes an intelligent technology-enhanced framework for reforming nursing simulation education by integrating clinical practice, curriculum design, skills competition standards, and 1+X certification systems. Grounded in action learning theory, the four-phase cyclical instructional model—Task Analysis, Virtual Simulation, Physical Practice, and Reflective Iteration—utilizes virtual reality (VR), AI tutoring systems, and IoT-enabled smart laboratories to bridge the theory-practice gap. Key innovations include: 1) Occupational-curricular-competitive-certificate integration through dynamic competency mapping, aligning training with real-world clinical workflows (e.g., sepsis protocols from competitions improved intervention speed by 41%). 2) AI-driven adaptive scaffolding, where machine learning analyzes procedural data (e.g., eye-tracking, biomechanics) to personalize remediation, demonstrating 27% higher procedural accuracy in dysphagia management versus traditional methods. 3) Hybrid physical-virtual environments featuring sensor-fusion technology (e.g., piezoelectric arrays quantifying aspiration-prevention postures) and physiologically responsive VR scenarios (e.g., blood pressure fluctuations triggered by medication errors). Validation in geriatric nursing showed 78% of learners accurately predicted aspiration risks post-training, with clinical performance matching nurses with 5–8 months of experience. The framework offers a transferable paradigm for cultivating digitally literate nursing professionals while addressing systemic challenges in curriculum-clinical alignment and certification integration.  

Keywords

Smart nursing education, Quadripartite integration model, Action learning, Virtual simulation, Competency-based assessment, 1+X certification

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DOI: http://dx.doi.org/10.12345/jetm.v9i3.30911