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Research on the Continuous Improvement Mechanism of Digital Image Processing Course Quality Empowered by Artificial Intelligence

Author(s)

Xiuxiu Gu*, Mingliang Zhu, Qiong Wu, Kunpeng Ge, Jing Yuan

Affiliation(s)

School of Information Engineering, Suqian University, Suqian, Jiangsu, China *Corresponding Author

Abstract

Aiming at the prevalent pain points in the teaching of the Digital Image Processing course for communication engineering majors, such as a fragmented knowledge system, differentiated student foundations, and disconnected practical application, this study implements an artificial intelligence (AI)-driven educational model at Suqian University. The three-stage framework— "Foundation Building, Scenario Delving, and Intelligent Leap" —combines AI-powered modular knowledge mapping, dynamic tiered instruction, project-based learning (PBL) integration, AI-adaptive platforms, and industry-academia collaboration. These innovations effectively resolve traditional teaching bottlenecks. Practical implementation demonstrates significant improvements in student engagement, practical skills, and innovative thinking. Notable outcomes include increased competition awards, heightened research participation, and improved course satisfaction. The reform model has been successfully replicated across the "AI + Communication" curriculum cluster, establishing a replicable paradigm for engineering education innovation.

Keywords

Digital Image Processing; Teaching Reform; Artificial Intelligence; Hierarchical Teaching

References

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