Curriculum Reform of "Robot Vision Perception and Detection" Driven by the Industry-Education Integration Community: Model Innovation and Empirical Research

Abstract

Industry-education integration communities are redefining how advanced robotics courses are designed, delivered and validated. This paper reports a two-year design-based study that reconceptualised the undergraduate module “Robot Vision Perception and Detection” from a supply-chain-oriented alliance of three universities, two robotics manufacturers and one logistics giant. Adopting a community-of-practice lens, we replaced the conventional lecture–lab sequence with a “challenge-driven co-creation loop” in which corporate engineers release real-time production-line vision defects as curricular tasks, faculty scaffold theoretical principles, and students iterate solutions on the factory floor using identical hardware and data streams. Mixed-methods evaluation with 142 students and 18 industry mentors shows significant gains: (1) learning performance increased by 0.82 standard deviations; (2) student creative self-efficacy and systems-thinking improved 34\% and 29\% respectively; (3) average defect detection recall of student models rose from 72\% to 93\%, with 8 prototypes transferred to the partner lines; (4) faculty–industry co-publications and patent disclosures tripled. Qualitative trace data reveal that boundary objects (annotated datasets, Dockerized algorithms and shared Kanban boards) legitimately brokered epistemic differences between academia and industry. The study contributes an empirically grounded framework—CIE-CDL (Community-Integrated Education via Challenge-Driven Loops)—that embeds authentic socio-technical complexity into robotics curricula while simultaneously generating measurable value for industrial partners. Implications for scalable, sustainable industry–education symbiosis are discussed.