The so-called “triple disconnect” – between curriculum and the technology frontier, between classroom scenarios and real industrial settings, and between graduate outcomes and market demand – is a stubborn obstacle facing Innovation and Entrepreneurship (I&E) education at China’s application-oriented universities. Drawing on a six-month quasi-experimental pilot at Baoji University of Arts and Sciences, we propose and evaluate a faculty capability development model, large language model – course-competition integration (LLM-CCI). This model combines four mechanisms: industry-rooted task curation in the Baoji “China Titanium Valley” cluster; a campus-deployed large language model (LLM) functioning as a “technology middle platform”; a four-stage course-competition-transformation pipeline; and dual mentorship pairing each pilot teacher with an enterprise engineer and an academic supervisor. Thirty teachers from a single computer-science college participated, with 16 in the experimental track and 14 in a matched control. Faculty I&E capability was measured before and after the pilot on five dimensions; student outcomes were tracked across three flagship competitions; enterprise project owners provided scenario-level satisfaction ratings. The experimental group recorded large within-group gains on Industry Literacy (Δ=1.27, d=2.35), Competition Coaching (Δ=1.14, d=1.96) and Entrepreneurship Guidance (Δ=0.98, d=1.51), and outperformed the control group on every dimension after baseline adjustment (partial η² ranged from 0.18 to 0.34). LLM-mediated assessment agreed closely with expert raters (Intraclass Correlation Coefficient [ICC]=0.82). The model offers a transferable, moderate-cost template for application-oriented universities serving single-cluster regional economies, and contributes empirical evidence on the use of generative artificial intelligence (AI) in faculty professional development.
References
[1] Jin, F., Roald, G. M. (2025) Training university educators to foster embedded entrepreneurship education: an evaluation. Cogent Education, 12(1), 2564255.
[2] Lee, D., Arnold, M., Srivastava, A., Plastow, K., Strelan, P., Ploeckl, F., Lekkas, D., Palmer, E. (2024) The impact of generative AI on higher education learning and teaching: a study of educators’ perspectives. Computers and Education: Artificial Intelligence, 6, 100221.
[3] Shahanga, G. J., Akyoo, E. P. (2025) Bridging skills gaps in higher education through industrial attachment to academic staff in Tanzania. African Quarterly Social Science Review, 2(4), 219-228.
[4] Zacharis, G., Papadakis, S. (2025) Can AI grade like a human? Validity, reliability, and fairness in university coursework assessment. Educational Process: International Journal, 19, e2025591.
[5] Chai, F., Ma, J., Wang, Y., Zhu, J., Han, T. (2024) Grading by AI makes me feel fairer? How different evaluators affect college students’ perception of fairness. Frontiers in Psychology, 15, 1221177.
[6] Crompton, H., Burke, D. (2023) Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22.
[7] Shahanga, G. J., Akyoo, E. P. (2025) Bridging skills gaps in higher education through industrial attachment to academic staff in Tanzania. African Quarterly Social Science Review, 2(4), 219-228.
[8] Hwang, G.-J., Xie, H., Wah, B. W., Gašević, D. (2020) Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100001.
[9] Zawacki-Richter, O., Marín, V. I., Bond, M., Gouverneur, F. (2019) Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(39), 1-27.
[10] Moorhouse, B. L., Wan, Y., Wu, C., Kohnke, L., Ho, T. Y., Kwong, T. (2024) Developing language teachers’ professional generative AI competence: an intervention study in an initial language teacher education course. System, 125, 103399.
[11] Estévez-Ayres, I., Callejo, P., Hombrados-Herrera, M. Á., Alario-Hoyos, C., Delgado Kloos, C. (2024) Evaluation of LLM tools for feedback generation in a course on concurrent programming. International Journal of Artificial Intelligence in Education, 34(3), 774-790.
[12] Letteri, I., Vittorini, P. (2025) Enhancing student feedback in data science education: Harnessing the power of AI-generated approaches. International Journal of Artificial Intelligence in Education, 35(5), 921-940.
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Zhu, L., Zhang, P. (2026) Bridging Industry and Academia through Course – Competition Integration: An LLM-Enabled Faculty Capability Development Model Anchored in a Regional Titanium Cluster. Global Education Bulletin, 3(2), 1-9. https://doi.org/10.71052/grb2025/LJOP1169