Learning desk

MultiAgent EDU StackGather good sources. Teach what matters.
T3Mermaid to ASCII art (mermaid-ascii)T3Kimi K3, and what we can still learn from the pelican benchmarkT3Firefox in WebAssemblyT3Spot birds not golfT3[AINews] Kimi K3 2.8T-A50B: the largest open model ever released; Opus 4.8-class at Sonnet 5 pricingT1From physical surfaces to human-centric heat stress: LST and UTCI heat mapping reveals nonlinear effects of urban morphologyT1DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge GraphsT1GenTL: A General Transfer Learning Model for Building Thermal DynamicsT1A short review on the maximum clique problem algorithms with classical, AI, and quantum methodsT1Man, Machine, and Masterpiece: Artistic Ownership in the AI EraT1HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective OptimizationT1How Well Does AI-Generated Feedback Work? Intrinsic and Extrinsic Evaluation across more than 20,000 EFL Essay DraftsT3Mermaid to ASCII art (mermaid-ascii)T3Kimi K3, and what we can still learn from the pelican benchmarkT3Firefox in WebAssemblyT3Spot birds not golfT3[AINews] Kimi K3 2.8T-A50B: the largest open model ever released; Opus 4.8-class at Sonnet 5 pricingT1From physical surfaces to human-centric heat stress: LST and UTCI heat mapping reveals nonlinear effects of urban morphologyT1DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge GraphsT1GenTL: A General Transfer Learning Model for Building Thermal DynamicsT1A short review on the maximum clique problem algorithms with classical, AI, and quantum methodsT1Man, Machine, and Masterpiece: Artistic Ownership in the AI EraT1HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective OptimizationT1How Well Does AI-Generated Feedback Work? Intrinsic and Extrinsic Evaluation across more than 20,000 EFL Essay Drafts
← Dispatches

The integration of generative artificial intelligence in nursing education from 2020 to 2025: A bibliometric analysis

Primary research

#372

T1digested
Topic
AI in Medical Education
First seen
2026-07-16 23:33:01
Last seen
2026-07-16 23:33:01

Source raw items (1)

  • Semantic Scholar2026-07-16 23:31:52
    The integration of generative artificial intelligence in nursing education from 2020 to 2025: A bibliometric analysis

    Generative artificial intelligence (AI), particularly large language models such as Chat Generative Pre-Trained Transformer (ChatGPT), is rapidly transforming higher education, including nursing. This study mapped global research trends on the integration of generative AI in nursing education using a bibliometric approach. Articles indexed in Scopus between 2020 and 2025 were retrieved with keywords related to generative AI, ChatGPT, and nursing education. A total of 149 English-language journal articles were analyzed, and bibliometric visualization was conducted using VOSviewer version 1.6.20 to examine publication patterns, leading authors, journals, institutions, countries, and thematic clusters. Results showed a steady rise in publications, with significant growth in 2023–2024 following the widespread adoption of ChatGPT. The most prolific author is from Taipei Medical University, while Nurse Education in Practice was the top journal with 13 articles and 113 citations. Taipei Medical University and NUS Yong Loo Lin School of Medicine were the most productive institutions, and the United States led in overall output and international collaborations. Keyword analysis revealed four thematic clusters: technological foundations, pedagogical applications, competency and critical thinking, and nursing informatics. Generative AI in nursing education is an emerging field, and future research should address policy, long-term outcomes, and institutional adoption to ensure responsible integration.