Author(s)

Qian Li ¹, Yongliang Zhang ¹, Han Wang ¹

Affiliation(s)

¹ School of Mechanical Engineering, University of Shanghai for Science and Technology, Jungong Road 516, Shanghai, China

Corresponding Author

Qian Li

ABSTRACT

The course on mechanical equipment structural design faces structural challenges in transitioning from empirical imitation to intelligent judgment. The core contradictions of this course currently include: the tension between the highly context-dependent nature of structural design knowledge and traditional standardized teaching approaches, the misalignment between the rapid generation of design solutions by artificial intelligence(AI) and the gradual cultivation of engineering judgment capabilities, and the renewed manifestations in which structural design is difficult to teach and difficult to learn of the classic dilemma in the intelligent era. Grounded in the policy framework of the "AI + Education Action Plan" and incorporating cutting-edge AI application research in mechanical design education, this paper proposes a restructured pedagogical philosophy characterized by a process of judgment-first, process-driven, and human-machine collaboration. The study posits that the core value of structural design courses lies not in training students to draw correct diagrams, but rather in cultivating their engineering judgment when confronted with uncertain constraints. The value of AI empowerment lies in liberating students from low-level activities, such as referencing design catalogs and replicating existing drawings and allowing them to focus on comparing structural solutions, predicting failure paths, and developing decision-making logic under given constraints. Accordingly, this paper proposes a systematic restructuring plan across four dimensions: redefining course objectives, reorganizing the curriculum framework, innovating teaching modalities, and reforming evaluation mechanisms.

KEYWORDS

Artificial intelligence; Mechanical equipment structural design; Curriculum restructuring; Engineering judgment; Human-machine collaboration

CITE THIS PAPER

Qian Li, Yongliang Zhang, Han Wang. Assessment and Restructuring of an AI-Enabled Course on Mechanical Equipment Structural Design. Curriculum and Teaching Methodology (2026). Vol. 9, No. 5, 108-115. DOI: http://dx.doi.org/10.23977/curtm.2026.090514.

REFERENCES

[1] Waldherr, F., Henderson, A., Krugh, M.,  Mears, L. (2026) Design and Assessment of a Novel Laboratory-Based Manufacturing Course for Undergraduate Engineering Students. Manufacturing Letters, 49, 1597-1608.
[2] Ola, R., Issam, A.M., Mohamed, I. (2020) Student-Centered Assessment of the Capstone Design Project Course in Mechanical Engineering Program. International Journal of Engineering Education, 36, 998-100.
[3] Holland, D.P., Walsh, C.J., Bennett, G.J. (2019) A qualitative investigation of design knowledge reuse in project-based mechanical design courses. European Journal of Engineering Education, 44, 137-152.
[4] Reuven, K., Iris, T. (2017) Teaching Industrial Design in Mechanical Engineering: A Laboratory for Design and Engineering. International Journal of Engineering Education, 33, 672-679.
[5]  Zhou, H., Wang, L., Tong, J., Yu, G. (2025)  Construction of a Case-Based Teaching Model for Science-Education Integration in the Context of "New Engineering": A Case Study of Course "Mechanical Design" . Journal of Sociology and Ethnology, 7, 112-117.
[6] Nie, Y., Ma, Y. (2025) Pathways for the Course Optimization of Mechanical Design, Manufacturing and Automation Based on Industry-Education Integration. New Explorations in Education and Teaching, 3, 36-38.

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