Enterprise AI Analysis
Research Hotspots and Frontier Trends of Vocational Education in China under the Influence of Artificial Intelligence: A Bibliometric Analysis Based on CiteSpace
Authors: Ruiyun Du, Ninghui Tan, Fangying Jiang, Qingchun Weng
This study uses China National Knowledge Infrastructure (CNKI) as the data source platform, focuses on high-quality papers published in Peking University core journals and CSSCI journals. It uses CiteSpace, a bibliometric software that looks at research, to find out about the most popular topics, how research has developed, and the latest trends in vocational and technical education in China in the era of artificial intelligence.
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Core Authors Identified
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Top Author Publications
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Modularity Index (Q)
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Silhouette Coefficient (S)
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This study utilized China National Knowledge Infrastructure (CNKI) as the primary data source, specifically focusing on high-quality papers from Peking University core journals and CSSCI journals. A rigorous process of retrieval, screening, and cleaning resulted in a final dataset of 307 valid papers. The bibliometric software CiteSpace (version 6.3.1) was employed for analysis, performing betweenness centrality and cluster analysis to ensure the significance and credibility of the findings (modularity Q-values of 0.8483 and silhouette S-values of 0.9808).
Analysis revealed that Professor Yanli Xu from Tianjin University was the most prolific author with 10 papers. A total of 14 core authors were identified, contributing 60 papers (19.54%) to the sample. However, the study noted a low network density (0.0037), indicating limited collaboration among researchers. In terms of institutions, Tianjin University led with 23 publications, followed by Southwest University (16) and Beijing Normal University (12). Vocational colleges and ministry-affiliated research institutions also play significant roles, contributing to policy formulation and guiding practice.
Keyword analysis using betweenness centrality highlighted 'Talent cultivation' (1.00 centrality, 135 frequency) and 'Artificial intelligence' (0.56 centrality, 101 frequency) as key themes. Other significant keywords included 'Higher vocational education', 'Information technology', and 'Industry-education integration'. Clustering analysis identified core research themes such as Vocational Education, Artificial Intelligence, Intelligent Era, Information Technology, School-Enterprise Cooperation, Industry-Education Integration, Digitization, Intelligent Education, Higher Vocational Colleges, Challenges, Applied Research, and Human-Job Matching, demonstrating the diverse facets of AI's influence on vocational education.
- Research methods are relatively limited in Chinese academia, with a dominance of qualitative approaches and a lack of large-scale data modeling.
- The field exhibits clear temporal characteristics, with themes evolving in synchronization with societal needs and major events.
- A lack of cohesion among core author groups and insufficient collaboration hinders the ability to solve complex interdisciplinary problems.
- The rapid advancements in generative AI technology have significantly increased related literature since 2022, warranting special attention.
Enterprise Process Flow
Retrieve and identify Chinese database (CNKI) n=330
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Screening (Type: Academic papers)
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The cleaning of irrelevant and duplicate literature
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Samples (Finally included n=307)
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Analysis (Statistical analysis software: CiteSpace (version 6.3.1))
| Keyword | Betweenness Centrality | Frequency |
|---|---|---|
| Talent cultivatio | 1.00 | 135 |
| Higher vocational education | 0.84 | 8 |
| Influence | 0.71 | 2 |
| Talent cultivation | 0.67 | 26 |
| Artificial intelligence | 0.56 | 101 |
| Integration of industry and education | 0.41 | 20 |
| Information technology | 0.21 | 5 |
| Value-added evaluation | 0.20 | 1 |
| Intelligent era | 0.19 | 12 |
| Digitalization | 0.19 | 6 |
| Professional development | 0.14 | 5 |
| Multiple subjects | 0.14 | 1 |
| Digital technology | 0.13 | 3 |
| Professional development | 0.11 | 4 |
| Cluster Name | Silhouette Score | Main Keywords in Cluster |
|---|---|---|
| Vocational Education | 0.997 | Vocational Education, Digital Education, Implementation Path |
| Artificial Intelligence | 1 | Artificial Intelligence, Skill Demand, Value |
| Intelligent Era | 0.992 | Intelligent Era, Higher Vocational Education, Vocational Colleges |
| Information Technology | 0.97 | Talent Cultivation, Information Technology, Big Data |
| School-Enterprise Cooperation | 0.958 | Digital Economy, Specialty Construction, School-Enterprise Cooperation |
| Industry-Education Integration | 0.917 | Industry-Education Integration, Ethical Risk, Development Path |
| Digitization | 0.861 | Digitization, Professional Development, Digital Technology |
| Intelligent Education | 0.993 | Intelligent Education, Development, Technological Culture |
| Higher Vocational Colleges | 1 | Higher Vocational Colleges, Collaborative Innovation, Practical Orientation |
| Challenges | 0.975 | Challenges, Employment, Job Displacement |
| Applied Research | 1 | Vocational Education Teachers, Training Platform, Practical System |
| Human-Job Matching | 0.993 | Working World, Industry 4.0, Skill Mismatch |
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Strategic Roadmap for AI Integration
Based on the analysis, here are key strategic initiatives to enhance vocational education in the AI era.
Align Research with Industry Needs
Create direct links between academic research priorities and evolving industry demands to ensure educational relevance and impact.
Foster Collaborative Education Models
Develop and implement multidirectional knowledge exchange platforms, such as 'industry-university-research integration,' to promote continuous quality improvement.
Enhance Research Methodology
Adopt mixed-methods research frameworks, combining qualitative and quantitative approaches for more scientific rigor and practical utility in education reform.
Internationalize Vocational Education Research
Actively compare and learn from international practices to broaden the scope and impact of vocational education research in China.
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