TY - GEN
T1 - AI-Empowered Comprehensive Practice of Embedded Intelligent Measurement and Control System Course Reform
AU - Zheng, Wenbin
AU - Qiao, Jiaqing
AU - Yin, Hongtao
AU - Fu, Ping
AU - Liu, Bing
AU - Feng, Lei
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - With the rapid development of Industry 4.0 and intelligent manufacturing, traditional embedded courses can no longer meet the industry's demand for 'AI + embedded' interdisciplinary talents. This paper focuses on the empowering role of AI tools in course teaching, demonstrating the positive impact of AI technology on improving teaching quality through the construction of a three-dimensional support system comprising intelligent Q and A, learning analytics, and adaptive learning. Existing courses mostly concentrate on general embedded development, whereas this course takes measurement and control systems as its core vehicle, emphasizing the cultivation of students' practical skills in key technologies of the measurement and control field. This course introduces edge computing node deployment and lightweight AI inference frameworks to achieve localized intelligent decisionmaking and control optimization, reducing reliance on cloud computing. This approach more intuitively and effectively addresses the needs of students in this discipline, providing a replicable practical solution for intelligent teaching reform.
AB - With the rapid development of Industry 4.0 and intelligent manufacturing, traditional embedded courses can no longer meet the industry's demand for 'AI + embedded' interdisciplinary talents. This paper focuses on the empowering role of AI tools in course teaching, demonstrating the positive impact of AI technology on improving teaching quality through the construction of a three-dimensional support system comprising intelligent Q and A, learning analytics, and adaptive learning. Existing courses mostly concentrate on general embedded development, whereas this course takes measurement and control systems as its core vehicle, emphasizing the cultivation of students' practical skills in key technologies of the measurement and control field. This course introduces edge computing node deployment and lightweight AI inference frameworks to achieve localized intelligent decisionmaking and control optimization, reducing reliance on cloud computing. This approach more intuitively and effectively addresses the needs of students in this discipline, providing a replicable practical solution for intelligent teaching reform.
KW - Artificial Intelligence
KW - Course Reform
KW - Electronic Information Engineering
KW - Practical Teaching Reform
UR - https://www.scopus.com/pages/publications/105034902319
U2 - 10.1109/ICSMD67131.2025.11365510
DO - 10.1109/ICSMD67131.2025.11365510
M3 - 会议稿件
AN - SCOPUS:105034902319
T3 - ICSMD 2025 - International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence
BT - ICSMD 2025 - International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2025
Y2 - 21 November 2025 through 23 November 2025
ER -