Artificial intelligence (AI) agents are spreading quickly and are changing what many jobs consist of, and education has been slower to respond than most sectors. Agent products are also getting easier to use, so the technical set-up that once fell to the user is now built into the product. This paper asks what vocational education should teach once that shift has happened. It draws on Chinese national and provincial policy texts and on all 106.00 scenarios in the first batch of the Ningbo “AI + Manufacturing” scenario list. Two findings follow. What makes an agent act on its own is not the model but the program that runs it, so teaching one platform has a short useful life. And 31.10 per cent of the listed scenarios sit in business functions rather than engineering ones, where the scarce ability is to state a task clearly. It is also to judge whether the result is usable. Both rest on business knowledge. Three measures follow: restructure learning objectives across knowledge, skill, and literacy; embed AI literacy in existing professional courses instead of adding a separate one; and, once a curriculum updating mechanism is in place, change assessment first, training tasks second, and industry links last.
References
[1] Ferrag, M. A., Lakas, A., Tihanyi, N., Debbah, M. (2026) LLM and AI agents for autonomous systems: a survey of applications, datasets, and security challenges. IEEE Open Journal of Intelligent Transportation Systems, 7, 615-657.
[2] Lai, Y., Luo, J., He, X. (2026) Innovative industrial cluster policies and enterprise digital transformation: Does FinTech matter. Applied Economics, 1-21.
[3] Du, S., Ricafort, R. (2026) Ethical AI practices in E-commerce companies: evidence from Henan toward, China toward a policy guide. International Journal of Sustainability and Advanced Integrated Research, 2(2), 960-966.
[4] Li, Y., Cheng, H., Qin, Q. (2025) Evaluations and improvement methods of deep learning ability in blended learning. International Journal of e-Collaboration (IJeC), 21(1), 1-17.
[5] Zhang, Y. (2025) AI-driven transformation of vocational education: opportunities, challenges, and future paths. International Journal of Knowledge Management (IJKM), 21(1), 1-20.
[6] Alva, L. R., Pandey, B. (2026) Agentic AI systems in the age of generative models: architectures, cloud scalability, and real-world applications. Artificial Intelligence Review, 59(3), 88.
[7] Herrera, M., Pérez-Hernández, M., Kumar Parlikad, A., Izquierdo, J. (2020) Multi-agent systems and complex networks: review and applications in systems engineering. Processes, 8(3), 312.
[8] Bollaert, L. (2025) Artificial intelligence: Objective or tool in the 21st-century higher education strategy and leadership? Education Sciences, 15(6), 774.
[9] Hamdoun, W. M. A. (2023) Constructive alignment approach: enhancing learning and teaching. British Journal of Multidisciplinary and Advanced Studies, 4(2), 162-170.
[10] Wu, Y., Huang, Y. (2026) Faculty AI literacy in higher education systems: a systematic review and future-adaptive framework. Interactive Learning Environments, 1-31.
[11] Liu, S., Cheng, H. (2024) Manufacturing process optimization in the process industry. International Journal of Information Technology and Web Engineering (IJITWE), 19(1), 1-20.
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Han, F., Xin, T. (2026) An Exploration of Restructuring Learning Objectives in China’s Higher Vocational Business Education in the Era of Multi-scenario Agents. Global Education Bulletin, 3(4), 31-38. https://doi.org/10.71052/grb2025/PFZG2158