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Research on Curriculum Reform of Machine Learning and Artificial Intelligence under the background of Emerging Engineering Education

Quanfu Wang, Qi Ni, Huanhui Liang, Xuanjiao Lyu, Wenjia Guo

Abstract


In the context of Emerging Engineering Education, ‘Machine Learning and artificial Intelligence’, is one of pivot components in studies in higher education reforms. However, the theoretical knowledge of Machine Learning and artificial Intelligence cybersecurity is obscure and complex, rendering it challenging for students to comprehend. Even, practical case studies are scarce, and students demonstrate a lack of interest and motivation to the course. To address these issues, this paper proposes a teaching method, which incorporates an image recognition engine bypass experiment, to augment practical case studies and enhance teaching efficacy. The course feedback indicates the method practically deepening students’ understanding of course content and significantly improving faculty quality in educational reform efforts.

Keywords


Emerging Engineering Education; Studies In Curriculum Reform; Machine Learning; Artificial Intelligence; Teaching Quality

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References


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DOI: http://dx.doi.org/10.18686/ahe.v9i5.14264

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