# Bridging Artificial Intelligence and Power Systems Education Using a Hands-On Executable Framework

arXiv: https://arxiv.org/abs/2608.02599 · HTML: https://mlsanity.com/paper/2608.02599/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 3 Aug 2026.

## Builds on (most cited first, 7 of 7)

- [Physics-Informed Neural Networks for Power Systems](https://mlsanity.com/paper/1911.03737/) (2019)
- [Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges](https://mlsanity.com/paper/2102.01168/) (2021)
- [Creation and Evaluation of a Pre-tertiary Artificial Intelligence (AI) Curriculum](https://mlsanity.com/paper/2101.07570/) (2021)
- [Framework for Adoption of Generative Artificial Intelligence (GenAI) in Education](https://mlsanity.com/paper/2408.01443/) (2024)
- [Fusion of Model-free Reinforcement Learning with Microgrid Control: Review and Vision](https://mlsanity.com/paper/2206.11398/) (2022)
- [Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution](https://mlsanity.com/paper/2607.13034/) (2026)
- [An Open Source Power System Simulator in Python for Efficient Prototyping of WAMPAC Applications](https://mlsanity.com/paper/2101.02937/) (2021)

## Abstract (arXiv)

Artificial intelligence (AI) is increasingly central to power and energy systems, supporting modeling, forecasting, optimization, and control. Yet most existing works emphasize specialized applications and offer little reusable material for newcomers or interdisciplinary learners, who increasingly rely on large language models rather than building their own. This gap points to a need for engineering-grounded AI (EGAI), in which AI workflows follow established engineering and power-system domain rules rather than acting as task-agnostic black boxes. Motivated by a community survey of researchers and practitioners, which shows 92% report at least one barrier before running an AI model and 94% want a power-specific hands-on course. This paper presents a framework consisting of open, executable module library that lowers the entry barrier for AI in power systems. The modules follow a progressive difficulty ladder that maps core AI concepts onto representative power-system tasks: (i) foundational deep neural network (DNN) templates for function approximation and load-curve fitting; (ii) a domain-coupled convolutional neural network (CNN) power-flow surrogate for a 5-bus system; and (iii) frontier modules on DNN-assisted optimization, deep reinforcement learning (DRL) for battery storage control, and physics-informed neural networks (PINNs) for the swing equation. All modules are released as Jupyter notebooks that run locally or on Google Colab and are delivered through an IEEE online course and IEEE Power & Energy Society (PES) webinar series. The webinar drew more than 590 live attendees, which is among the ten most-attended IEEE PES webinars, and over 344 repository visits within two weeks, reinforcing the survey-based motivation.