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Exploration of Teaching Reform in Applied Undergraduate Courses Driven by Generative AI: Taking the Course of “IoT Machine Vision” as an Example

Author(s)

Yingyi Duan1, Xiaochen Zhang1, Yiran Tao2,*

Affiliation(s)

1Guangdong University of Science and Technology, Dongguan, Guangdong, China 2The Education University of Hong Kong, HongKong, China *Corresponding Author

Abstract

Addressing the key challenges in the teaching of engineering courses in applied undergraduate universities, including high entry barriers, difficult engineering practice, and weak innovation ability, this paper takes the IoT Machine Vision course as the case study, proposes a new model of teaching reform based on the AIGC+CDIO concept. This model integrates Artificial Intelligence Generated Content (AIGC) tools into the Conceive-Design-Implement-Operate (CDIO) process. Through conducting an empirical study on the teaching project of Intelligent Detection of Safety Helmet Wearing at Construction Sites, which indicates that, this model effectively lowers the technical barrier to coding. Students can transfer their focus from low-level code debugging to algorithm logic design and engineering applications. This model substantially enhances students’ competency to solve complex engineering challenges and cultivate their digital literacy.

Keywords

Generative AI; Internet of Things Engineering; Machine Vision; CDIO; Teaching Reform; Safety Helmet Detection

References

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