Exploration of AIGC-Assisted Course Development for Ship Structure and Freight Integration in the Context of Industry-Education Integration

Jiafa Liu; Xuming Xu; Guoguang Lu; Qinghua You

doi:10.70088/w9xx2757

Authors

Jiafa Liu School of Maritime, Hainan Vocational University of Science and Technology, Haikou, China Author
Xuming Xu School of Maritime, Hainan Vocational University of Science and Technology, Haikou, China Author
Guoguang Lu School of Maritime, Hainan Vocational University of Science and Technology, Haikou, China Author
Qinghua You School of Maritime, Hainan Vocational University of Science and Technology, Haikou, China Author

DOI:

https://doi.org/10.70088/w9xx2757

Keywords:

industry-education integration, generative ai, maritime education, curriculum development, teaching reform

Abstract

In contemporary maritime-related academic programs, the long-standing pedagogical separation of ship structure and freight management courses has frequently resulted in insufficient comprehensive application skills among graduating students. To address this critical educational gap, this project systematically integrates the two traditionally distinct courses. It reorganizes the core teaching content around the typical, real-world operational tasks of modern shipping companies, thereby fostering a more cohesive learning environment. Furthermore, the curriculum innovatively incorporates Artificial Intelligence Generated Content (AIGC) tools to assist educators in developing complex 3D structural models, dynamic freight simulation scripts, and highly adaptive intelligent question banks. To evaluate the efficacy of this integrated approach, a pilot teaching study was conducted utilizing a rigorous quasi-experimental design. The study comprised a total of 160 students, evenly divided with 80 participants in the experimental group and 80 in the control group. The empirical results revealed that the experimental group achieved an average practical assessment score of 78.4, which was notably 9.2 points higher than that of the control group (P < 0.001). Additionally, course satisfaction scores reached 4.21 out of 5 in the experimental cohort, representing a statistically significant increase of 0.47 points over the control group (P < 0.001). Ultimately, the integration of AIGC demonstrates significant advantages in enhancing resource development efficiency and student engagement. However, the study also highlights that AI-generated educational content still necessitates meticulous teacher review and professional approval to ensure academic accuracy and industry compliance.

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