Enterprise AI Analysis
Artificial intelligence-driven nursing examination research: dynamic evolution and hotspot analysis
This study systematically combed the current status of the application of artificial intelligence technology in the field of nursing education assessment, and presented the research hotspots in the field visually through knowledge mapping. METHODS: A comprehensive search was conducted in the core database of Web of Science, and the number of publications, countries and keywords were visualized and analyzed using software such as Cite Space (6.2.R3) and VOS viewer (1.6.20). RESULTS: A total of 903 relevant publications were included in this paper, with a general upward trend in the growth of literature on AI applied to the field of nursing examinations, with the main output country being the United States. Among them, 'Item Response Theory' is the hottest research topic, and the recent and future research hotspots will focus on hot areas such as 'Clinical Decision Support System'. CONCLUSION: The application of artificial intelligence in the field of nursing examination is becoming more and more widespread, which helps to improve the efficiency and quality of nursing educators.
Executive Impact & Key Metrics
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Total Publications
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Growth Rate (2023-2024)
United States
Top Output Country
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
This section explores key findings related to Bibliometrics, highlighting its implications for enterprise AI.
Enterprise Process Flow
Web of Science Core Collection (n=1074)
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Duplicate records removed (n=4)
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Records screened (n=1070)
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Studies excluded (n=167)
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Studies included in analysis (n=903)
This section explores key findings related to AI Applications, highlighting its implications for enterprise AI.
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'Item Response Theory' Occurrences (2015-2024)
This section explores key findings related to Research Hotspots, highlighting its implications for enterprise AI.
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This section explores key findings related to Future Trends, highlighting its implications for enterprise AI.
Strengthening Collaborative Innovation
The research highlights that cross-regional collaboration is critical for sharing knowledge maps and overcoming data barriers and computing power limitations in developing countries. Establishing a multi-level collaborative network can narrow global and regional disparities in AI application, accelerating progress and ensuring equitable development in nursing examination advancements.
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Projected Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
Our phased approach ensures a seamless integration of AI, maximizing benefits while minimizing disruption.
Phase 1: Discovery & Strategy
Conduct a comprehensive audit of existing examination processes. Define AI integration goals, identify key assessment areas for AI intervention, and establish a clear roadmap with measurable KPIs.
Phase 2: Pilot Implementation
Begin with a small-scale pilot of AI tools for specific objective question scoring or virtual simulation modules. Gather initial feedback from nursing educators and students, and iterate on AI model performance.
Phase 3: Scaled Deployment
Expand AI application across a wider range of examination types, including automated behavioral recognition for clinical skills. Integrate AI solutions with existing learning management systems for seamless data flow.
Phase 4: Optimization & Ethical Governance
Continuously monitor AI system performance, accuracy, and fairness. Establish ethical guidelines, ensure data privacy, and refine algorithms based on ongoing feedback and evolving educational standards.
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