Author(s)

Zhuohuang Zhang ¹, Li Li ¹

Affiliation(s)

¹ School of Artificial Intelligence, Shenzhen Polytechnic University, Shenzhen, China

Corresponding Author

Zhuohuang Zhang

ABSTRACT

The rapid advancement of Artificial Intelligence (AI) has driven significant changes in vocational computer curricula in China. Unlike previous studies focused on theoretical models, this research uses a data-driven approach to evaluate the structural evolution of these reforms within the Chinese vocational education context. Utilizing the Word2Vec model and semantic network analysis, we examined 370 core academic papers published between 2018 and 2025. The empirical results reveal three critical structural characteristics. First, we identify a distinct structural stratification in curriculum integration, where application-layer courses demonstrate high semantic coupling with AI, whereas infrastructure-layer courses remain relatively isolated. Second, we observe a pragmatic turn in the temporal evolution of reform focus; post-2022, the discourse decisively shifted from macroscopic policy design to microscopic pedagogical practice. Third, we detect a pronounced asymmetry in stakeholder attention, characterized by a heavy concentration on technological tools and student outcomes, contrasted with the marginalization of teacher digital literacy. These findings highlight the need to strengthen foundational courses and enhance faculty capabilities for a more balanced educational structure.

KEYWORDS

Artificial Intelligence, Vocational Education, Curriculum Reform, Word2Vec, Semantic Network

CITE THIS PAPER

Zhuohuang Zhang, Li Li. Evolutionary Trends and Structural Characteristics of Chinese Vocational Computer Curricula in the AI Era: A Semantic Network Analysis. Advances in Vocational and Technical Education (2026) Vol. 8: 30-35. DOI: http://dx.doi.org/10.23977/avte.2026.080104.

REFERENCES

[1] Miao, F., & Holmes, W. (2021). Artificial intelligence and education. Guidance for policy-makers. 
[2] Agbo, F. J., Olivia, C., Oguibe, G., Sanusi, I. T., & Sani, G. (2025). Computing education using generative artificial intelligence tools: A systematic literature review. Computers and Education Open, 100266. 
[3] Zhang, H. (2025). The Research on the Construction Path of Artificial Intelligence Curriculum System for Higher Vocational Undergraduate Education in the New Era. International Educational Research, 8(2), pp. 99-107. 
[4] Prayuda, M. S., Ginting, F. Y. A., & Tamba, L. (2026). Tracing Two Decades of Artificial Intelligence in Education: A Bibliometric Analysis of Trends, Themes, and Future Directions (2000–2025). European Journal of Educational Research, 15(1), 285-304. 
[5] Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781. 
[6] Ferreira‐Mello, R., André, M., Pinheiro, A., Costa, E., & Romero, C. (2019). Text mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(6), e1332.

Sponsors, Associates, and Links

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• Advances in Educational Technology and Psychology
  
  
• Curriculum and Teaching Methodology
  
  
• Transactions on Comparative Education
  
  
• Applied & Educational Psychology
  
  
• Adult and Higher Education