TY  - JOUR
AU  - Zhu, Quanmin
AU  - Wang, Haihong
PY  - 2025
DA  - 2025/09/23
TI  - Primary Thought on Artificial Intelligence (AI) Enhanced Control Engineering Education
JO  - ICCK Transactions on Sensing, Communication, and Control
T2  - ICCK Transactions on Sensing, Communication, and Control
JF  - ICCK Transactions on Sensing, Communication, and Control
VL  - 2
IS  - 3
SP  - 215
EP  - 225
DO  - 10.62762/TSCC.2025.254228
UR  - https://www.icck.org/article/abs/TSCC.2025.254228
KW  - artificial intelligence in education
KW  - control engineering education
KW  - sensing-integrated learning
KW  - MATLAB/Simulink
KW  - reinforcement learning
KW  - AI-assisted assessment
KW  - ethical challenges
AB  - Artificial intelligence (AI) is reshaping engineering education by offering adaptive, interactive, and data-driven learning environments. This paper examines the primary roles of AI in enhancing control engineering education (CEE), with emphasis on sensing-oriented applications such as sensor-based system modelling, AI-assisted signal processing, and data-driven state estimation-domains that underpin modern sensing, communication, and control systems. The paper discusses how AI tools, particularly MATLAB and Simulink integrated with machine learning and reinforcement learning capabilities, can serve as effective pedagogical instruments for teaching complex topics including nonlinear control, adaptive control, and sensor fusion. Key benefits-such as personalised learning, real-time simulation, and automated feedback-are identified alongside significant challenges, including assessment integrity, over-reliance on AI, algorithmic bias, and ethical concerns. The central argument is that AI should function as a tool to augment the teaching-learning process rather than a shortcut to circumvent genuine intellectual engagement, and that its integration into CEE requires careful pedagogical design, ethical awareness, and balanced curriculum planning.
SN  - 3068-9287
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Zhu2025Primary,
  author = {Quanmin Zhu and Haihong Wang},
  title = {Primary Thought on Artificial Intelligence (AI) Enhanced Control Engineering Education},
  journal = {ICCK Transactions on Sensing, Communication, and Control},
  year = {2025},
  volume = {2},
  number = {3},
  pages = {215-225},
  doi = {10.62762/TSCC.2025.254228},
  url = {https://www.icck.org/article/abs/TSCC.2025.254228},
  abstract = {Artificial intelligence (AI) is reshaping engineering education by offering adaptive, interactive, and data-driven learning environments. This paper examines the primary roles of AI in enhancing control engineering education (CEE), with emphasis on sensing-oriented applications such as sensor-based system modelling, AI-assisted signal processing, and data-driven state estimation-domains that underpin modern sensing, communication, and control systems. The paper discusses how AI tools, particularly MATLAB and Simulink integrated with machine learning and reinforcement learning capabilities, can serve as effective pedagogical instruments for teaching complex topics including nonlinear control, adaptive control, and sensor fusion. Key benefits-such as personalised learning, real-time simulation, and automated feedback-are identified alongside significant challenges, including assessment integrity, over-reliance on AI, algorithmic bias, and ethical concerns. The central argument is that AI should function as a tool to augment the teaching-learning process rather than a shortcut to circumvent genuine intellectual engagement, and that its integration into CEE requires careful pedagogical design, ethical awareness, and balanced curriculum planning.},
  keywords = {artificial intelligence in education, control engineering education, sensing-integrated learning, MATLAB/Simulink, reinforcement learning, AI-assisted assessment, ethical challenges},
  issn = {3068-9287},
  publisher = {Institute of Central Computation and Knowledge}
}