A new framework called Engineering-Grounded AI (EGAI) has been developed to make artificial intelligence more accessible in power and energy systems education. This framework addresses the need for reusable materials and hands-on learning, as indicated by a survey where 92% of respondents faced barriers running AI models and 94% desired a power-specific course. The EGAI framework includes open, executable modules that progressively introduce AI concepts through power system applications, such as load-curve fitting, power-flow approximation, and optimization for battery storage control. These modules are available as Jupyter notebooks and have been integrated into an IEEE online course and webinar series, which saw significant attendance and repository engagement. AI
IMPACT This framework aims to lower barriers for AI adoption in power systems education, potentially accelerating interdisciplinary learning and research.
RANK_REASON The item describes a research paper presenting a new framework and executable modules for AI in power systems education. [lever_c_demoted from research: ic=1 ai=1.0]
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- 5-bus system
- artificial intelligence
- convolutional neural network
- deep neural network
- deep reinforcement learning
- Engineering-Grounded AI
- Google Colab
- Hugging Face
- IEEE Power & Energy Society
- Institute of Electrical and Electronics Engineers
- large-language models
- physics-informed neural networks
- power engineering
- Project Jupyter
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