This paper investigates the implementation and effectiveness of AI-assisted case teaching within university logistics education, addressing critical gaps between technological potential and pedagogical practice. Through a comprehensive survey of logistics majors at Shenzhen Polytechnic University, combined with K-means clustering analysis, this research identifies three primary challenges: the predominance of entertainment-focused case selection undermining educational objectives, significant limitations in the timeliness and contextual depth of AI-generated materials, and insufficient inspirational impact on student learning. The clustering analysis further reveals three distinct student profiles, enthusiastic adopters, cautious participants, and resistant traditionalists, highlighting varied receptivity to AI-assisted methodologies. Root cause analysis demonstrates that these issues stem from inherent tensions in case teaching between authenticity and argumentation, divergent teacher competencies in curating and guiding AI-generated content, and widespread lack of student subjective initiative. In response, this paper proposes a structured framework for enhancing teaching effectiveness, emphasizing reality-grounded case design, integration of timely socio-industrial concerns, inclusive participatory mechanisms, and improved inspirational scaffolding. The findings underscore that successful AI integration requires moving beyond technological novelty to foster pedagogical synergy, where AI tools augment rather than replace critical educator roles. This study contributes to the evolving discourse on AI in education by providing empirical evidence and strategic insights for optimizing case-based learning in specialized vocational disciplines.

Zhougeng Lin, Xinqi Huang. A Cluster-Based Approach to Optimize AI-Generated Case Study in University Logistics Courses. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 5: 57-66. https://doi.org/10.25236/AJCIS.2026.090507.

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