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arxiv: 2604.15697 · v2 · submitted 2026-04-17 · 📡 eess.SY · cs.SY

Integrating AI and Simulation for Teaching Power System Dynamics: An Interactive Framework for Engineering Education

Osasumwen Cedric Ogiesoba-Eguakun , Phani Kumar Inkollu , Rupesh Sah , Zia Rashid , Douglas Jussaume , Suman Rath This is my paper

Pith reviewed 2026-05-10 08:25 UTC · model grok-4.3

classification 📡 eess.SY cs.SY
keywords engineeringstudentssystemframeworkinteractivelayerlearningpower
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The pith

An AI-simulation framework is outlined to make power system dynamics more interactive and understandable for engineering students.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Power system dynamics involve complex math about how electricity grids respond to changes like faults or load shifts. Students often struggle because these ideas stay abstract without practice. The paper describes a three-part setup: an AI layer that gives explanations and guidance, a simulation layer that shows how systems behave when parameters change, and a user interface for real-time interaction. Students adjust values, see results, and receive AI feedback in a loop. The authors also outline steps for educators to build similar tools, breaking down concepts and checking learning. This aims to turn passive lectures into active exploration so students connect theory to real grids.

Core claim

The framework has three connected parts—an AI layer, a simulation layer, and a user layer—that work together in a continuous loop where students explore system behavior, change parameters, and receive feedback based on the results.

Load-bearing premise

That combining AI explanations with simulations will meaningfully reduce the difficulty students face with abstract, math-heavy power system dynamics concepts, without evidence of actual student outcomes.

Figures

Figures reproduced from arXiv: 2604.15697 by Douglas Jussaume, Osasumwen Cedric Ogiesoba-Eguakun, Phani Kumar Inkollu, Rupesh Sah, Suman Rath, Zia Rashid.

Figure 1
Figure 1. Figure 1: Proposed AI-driven interactive learning framework [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
read the original abstract

Artificial Intelligence (AI), especially cloud platforms and large language models (LLMs), is changing how engineering is taught by making learning more interactive and flexible. However, in electrical engineering and energy systems, students often find power system dynamics difficult to understand because the concepts are abstract, math-heavy, and there are limited opportunities for hands-on practice. This paper presents an AI-based interactive learning framework that combines simulation with intelligent feedback to improve understanding and student engagement. The framework has three connected parts: an AI layer that provides explanations and guidance, a simulation layer that models system behavior, and a user layer that allows students to interact with the system in real time. These parts work together in a continuous loop where students explore how the system behaves, change parameters, and receive feedback based on the results. The paper also provides a step-by-step process to help educators design and apply AI-supported learning environments, including breaking down concepts, using simulations, and assessing performance. This method helps students learn through practice and better understand how ideas from class apply to real power systems. It also provides a practical way to improve electrical engineering education and helps students get ready to use AI tools carefully and responsibly in engineering.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper is a high-level framework proposal with no quantitative models, so the ledger contains no free parameters, axioms, or invented entities.

pith-pipeline@v0.9.0 · 5536 in / 979 out tokens · 43492 ms · 2026-05-10T08:25:28.243288+00:00 · methodology

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Reference graph

Works this paper leans on

6 extracted references · 6 canonical work pages

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