Volume 4,Issue 3
Research on Industrial Adaptation and Practical Transformation of Emerging Engineering and Business Disciplines in Local Universities in the Era of Big Data and AI
The deep integration of big data and artificial intelligence (AI) is empowering industrial upgrading, posing new interdisciplinary, data-driven, and practice-oriented demands on talent cultivation in emerging engineering and business disciplines at local universities. However, these institutions currently face challenges such as outdated curricula, impractical teaching detached from real-world scenarios, insufficient engineering practice and data literacy among faculty, and superficial industry-education integration. To address regional industrial needs, it is essential to shift curricula from knowledge-based to competency-oriented with dynamic updates, transition practical teaching from simulation-based to real project-driven approaches, transform faculty into “dual-qualified” professionals with both teaching and industry expertise, and establish a collaborative ecosystem involving governments, universities, and enterprises. Through multidimensional reforms, the alignment between talent development and local industries can be enhanced, providing high-quality human resources to support regional digital transformation.
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[2] Gao D, 2026, “Industry Integration - Characteristic Shaping”: Logic, Models, and Pathways for the Applied Transformation of Local Universities. Journal of Fujian Jiangxia University, 16(1): 33–44.
[3] Ren S, Hu B, Tang Y, 2025, Research on Collaborative Innovation Mechanisms for Local Undergraduate Universities Driving Regional Industrial Upgrading. Science & Technology Economy Market, 2025(4): 44–46.
[4] Han Y, Zheng Q, Jiang Y, 2025, Breaking Through Dilemmas and Dynamic Optimization in the Construction of Micro-Majors in Digital Economy at Local Universities. Modern Business Trade Industry, 2025(20): 43–45.
[5] Zhou S, 2025, Research on Collaborative Innovation Pathways Between Private Universities and Cultural Industry Bases Driven by AIGC. Comedy World (Mid-Month Edition), 2025(8): 100–102.
[6] Mao H, 2025, Research on Innovative Pathways for Green Management Models in University Chemistry Laboratories Driven by Big Data and AI Technologies. Chemical Fiber & Textile Technology, 54(5): 255–257.