From Integration to Impact: Constructing a Predictive Evaluation Model for AI’s Penetration Effect in Vocational Construction Education
Artificial intelligence is reshaping vocational education, but few instruments exist for measuring how deeply AI has actually penetrated a curriculum. This paper combines CiteSpace bibliometric analysis of the CNKI, Web of Science, and Scopus literature with a questionnaire survey and a case study of Sichuan College of Architectural Technology to construct the AI-Vocational Education Index (AI-VEI), a twelve-indicator model that converts AI penetration into a single score. Indicator selection is anchored in bibliometric hotspots and policy priorities rather than in expert opinion alone; weights are derived from a multi-criterion layered fusion of keyword count, burst strength, and betweenness centrality, and are then combined with Analytic Hierarchy Process weights (consistency ratio 0.095) to balance data objectivity with expert judgment. Applied to the case college, the model returns a score of 62 out of 100, corresponding to grade C on the four-band scale adopted here. The survey identifies four persistent gaps: 16.7% of respondents have never used an AI educational tool; AI remains absent from systematic curriculum design; acceptance and skills training remain weak, with 80% of respondents fearing erosion of independent thinking; and generative-AI tools are almost entirely missing from school-enterprise practice bases. The study contributes a transparent, reproducible instrument for evidence-based decisions about AI in vocational education, and argues that policy targets should be set against measured penetration rather than against nominal adoption.
[1] Central Committee of the Communist Party of China, 2013, Decision of the Central Committee of the CPC on Several Major Issues Concerning Comprehensively Deepening Reform.
[2] General Office of the CPC Central Committee and General Office of the State Council, 2022, Opinions on Deepening the Reform of the Modern Vocational Education System.
[3] Huang ZS, 2024, Empowering New Quality Productivity with Large AI Models: Internal Mechanisms, Realistic Obstacles and Practical Approaches. Reform and Strategy, 40(2): 1–12. https://doi.org/10.16331/j.cnki.issn1002-736x.2024.02.001
[4] Liang YL, Zhang MM, 2024, Theoretical Demands and Practical Paths for AI Large Models to Promote Educational Development. Journal of Nanjing University of Posts and Telecommunications (Social Sciences Edition), 26(2): 65–71. https://doi.org/10.14132/j.cnki.nysk.20240321.001
[5] Zhu J, 2022, Employment Elasticity Forecast Analysis of China’s Construction Industry. Chongqing Architecture, 21(11): 39–42.
[6] MyCOS Research Institute, 2025, Chinese 4-year College Graduates Employment Annual Report (2025). Social Sciences Academic Press, Beijing.
[7] MyCOS Research Institute, 2025, Chinese 3-year Vocational College Graduates Employment Annual Report (2025). Social Sciences Academic Press, Beijing.
[8] Barus OP, Hidayanto AN, Handri EY, et al., 2025, Shaping Generative AI Governance in Higher Education: Insights from Student Perception. International Journal of Educational Research Open, 2025(8): 100452. https://doi.org/10.1016/j.ijedro.2025.100452
[9] Bone M, Ehlinger EG, Stephany F, 2025, Skills or Degree? The Rise of Skill-Based Hiring for AI and Green Jobs. Technological Forecasting and Social Change, 2025(214): 124042.
[10] Duong T, Chen H, 2025, An AI Chatbot for EFL Writing: Students’ Usage Tendencies, Writing Performance, and Perceptions. Journal of Educational Computing Research, 63(2): 406–430.
[11] Darmawansah D, 2025, ChatGPT-supported Collaborative Argumentation: Integrating Collaboration Script and Argument Mapping to Enhance EFL Students’ Argumentation Skills. Education and Information Technologies, 30(3): 3803–3827.
[12] Remmers de Vries S, Gonzales-Wong CD, 2025, Professional Quality of Life of Educators during COVID-19: A Mixed-Methods Inquiry. International Journal of Educational Research Open, 2025(9): 100467. https://doi.org/10.1016/j.ijedro.2025.100467
[13] Wieser M, 2023, 2023, Investigating the Role of ChatGPT in Supporting Text-based Programming Education for Students and Teachers. In Informatics in Schools, ISSEP 2023; Lecture Notes in Computer Science; Springer: Cham, Switzerland; 40–53.
[14] Fernandez-Munin MC, Garcia-Doval FM, 2025, Starting Point in the Introduction of AI in VET: Analysis and Proposals in Spain. In Intelligent Data Engineering and Automated Learning — IDEAL 2024, Part II; Lecture Notes in Computer Science, 15347; Springer: Cham, Switzerland; 508–516.
[15] Emersic Z, 2025, Educational Computer Vision Materials for Classification and Tracking of Objects. In Intelligent Data Engineering and Automated Learning — IDEAL 2024, Part II; Lecture Notes in Computer Science, 15347; Springer: Cham, Switzerland; 502–507.
[16] Zhou JS, Chen ZJ, 2023, Leading the High-Quality Development of Vocational Education with New Achievements in Professional Construction. China Vocational and Technical Education, 2023(26): 17–22 + 35.
[17] Zhu DQ, Peng HL, 2025, Strategy and Practice of High-Quality Development of Higher Vocational Education in a Strong Country. China Electro-Education, 2025(1): 93–100.
[18] Cao Y, Meng QG, 2023, Promoting the Integration of Industry and Education in Vocational Education and Building a High-quality “Dual-Qualified” Vocational Teacher Team. China Vocational and Technical Education, 2023(5): 19–24.
[19] Zeng TS, Zhuang XZ, Zong C, 2023, Deepening the Reform of the Modern Vocational Education System to Fully Serve and Support China’s Modernization. Chinese Vocational and Technical Education, 2023(13): 5–12.
[20] Liu X, Shi WP, 2016, Governance of Vocational Education Group Schooling: Logic, Theory and Path. China Higher Education Research, 2016(2): 101–105. https://doi.org/10.16298/j.cnki.1004-3667.2016.02.19
[21] Lu YZ, Zhao HY, 2023, Development Patterns, Practical Difficulties and Institutional Reflections on the Construction of Industry-Education Integration Cities: Analysis Based on 21 Policy Texts. China Vocational and Technical Education, 2023(3): 13–22.
[22] Pan HS, Yu XK, Li YD, 2024, A Study on the Operation Mechanism of Municipal Industry-Education Consortiums from the Perspective of Collaborative Governance: A Case Study. Vocational Education Forum, 40(9): 18–27.
[23] Ma XY, Shi WP, 2024, Learning Characteristics of Vocational College Students: A Potential Profile Analysis Based on Learning Activities. China Higher Education Research, 2024(6): 101–108. https://doi.org/10.16298/j.cnki.1004-3667.2024.06.15
[24] Wang QW, Xu GQ, 2024, Construction of an Integrated Model of “Teaching-learning-assessment” in Vocational Education. Education and Vocational, 2024(11): 31–39. https://doi.org/10.13615/j.cnki.1004-3985.2024.11.004
[25] Chen C, 2006, CiteSpace II: Detecting and Visualizing Emerging Trends and Transient Patterns in Scientific Literature. Journal of the American Society for Information Science and Technology, 57(3): 359–377.
[26] Aderibigbe SA, Omar M, Elmehdi H, et al., 2025, Artificial Intelligence-driven Prediction of Optimal Technology-aided Alternative Operations in Post-emergency Contexts: A Case Study from an Emirati University. International Journal of Educational Research Open, 2025(9): 100473. https://doi.org/10.1016/j.ijedro.2025.100473
