Author(s)

Zhu Zhongpan ^1,2, Liu Zhongle ¹, Lai Xin ¹, Zhang Zhendong ¹

Affiliation(s)

¹ College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai City, China
² State Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, Shanghai City, China

Corresponding Author

Zhang Zhendong

ABSTRACT

The rapid advancement of intelligent connected vehicle (ICV) industries and emerging engineering education reform has generated an urgent demand for interdisciplinary and practice-oriented engineering talents. Nevertheless, conventional single-discipline training modes in universities are constrained by rigid disciplinary boundaries, fragmented curriculum systems, and shallow university–enterprise cooperation, resulting in a prominent talent supply–demand mismatch that severely restricts industrial high-quality development. To address these critical dilemmas, this study proposes an innovative "one subject, two pillars" talent training framework empowered by large models and intelligent agents. This novel intelligent education paradigm effectively breaks traditional educational limitations by clarifying the system’s internal coupling mechanism and closed-loop operational logic. Corresponding reform strategies covering organizational collaboration, curriculum reconstruction, practical teaching optimization, faculty development and dynamic evaluation are systematically formulated. This research provides a rigorous theoretical basis and replicable practical paradigm for advancing ICV compound talent cultivation, and offers valuable insights for the digital and intelligent transformation of emerging engineering education.

KEYWORDS

Large Model Foundation; Intelligent Agent; Interdisciplinary Integration; Industry–Education Integration; Intelligent Connected Vehicle

CITE THIS PAPER

Zhu Zhongpan, Liu Zhongle, Lai Xin, Zhang Zhendong. Interdisciplinary and Industry–Education Integration for ICV Talent Training Reform Driven by Large Models and Intelligent Agents. Curriculum and Teaching Methodology (2026). Vol. 9, No. 4, 44-51. DOI: http://dx.doi.org/10.23977/curtm.2026.090407.

REFERENCES

[1] Zhang X, Li J, Zhou J, et al. (2025) Vehicle-to-everything communication in intelligent connected vehicles: A survey and taxonomy. Automotive Innovation, 2025, 8(1): 13-45.
[2] Zhang X Y, Lu Y G, Gao X, et al. (2025) Vehicle-road collaborative perception technology and development trend for intelligent connected vehicles. Acta Automatica Sinica, 51(2): 233-248.
[3] Jung Y. (2026) Cultivating T-shaped Engineers Through AI Literacy and Interdisciplinary Learning: Design and Implementation of a First-Year Engineering Module. In Proceedings of the ASEE Gulf Southwest (GSW) Regional Conference, College Station, TX, United States.
[4] Wei C., Luo W. (2026) Weakened Learning Subjectivity Under Generative AI in Higher Education: A Social Network Simulation Study. In 2026 International Conference on AI in Education Technology and Applications (AIETA), Lanzhou, China, pp. 38-42.
[5] Murtaza M., Cheng C.T., Fard M. et al. (2025) Transforming Driver Education: A Comparative Analysis of LLM-Augmented Training and Conventional Instruction for Autonomous Vehicle Technologies. International Journal of Artificial Intelligence in Education, 35, 736-773.
[6] Yang W.X., Zhao T.F. (2025) Modeling Cross-Disciplinary Communication in Smart Education Via Generative Agent. In 2025 12th International Conference on Machine Intelligence Theory and Applications (MiTA), Naples, Italy, pp. 1-8.
[7] Alghazo M., Ahmed V., Bahroun Z. (2025) Exploring the Applications of Artificial Intelligence in Mechanical Engineering Education. Frontiers in Education, 9, 1492308.
[8] Ding W. (2025) Novel AI-Based Teaching Model for Advancing Automotive Styling Design Education to Meet Industry Demands. International Journal of Mechanical Engineering Education, 03064190251350152.
[9] Johnson K., Brown P. et al. (2026) Designing an Interdisciplinary Artificial Intelligence Curriculum for Engineering: Evaluation and Insights From Experts. Computer Applications in Engineering Education.
[10] Chen L, Long H, Cui Z, et al. (2025) Exploration on practical education mode of vehicle engineering specialty. The Educational Review, USA, 9(3).

Sponsors, Associates, and Links

---

• Advances in Educational Technology and Psychology
  
  
• Transactions on Comparative Education
  
  
• Applied & Educational Psychology
  
  
• Advances in Vocational and Technical Education
  
  
• Adult and Higher Education