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VArsity: Can Large Language Models Keep Power Engineering Students in Phase?

BibTeX

@misc{talkington2025varsitylargelanguagemodels,
      title={{VArsity: Can Large Language Models Keep Power Engineering Students in Phase?}}, 
      author={Samuel Talkington and Daniel K. Molzahn},
      year={2025},
      eprint={2507.20995},
      archivePrefix={arXiv},
      primaryClass={cs.CY},
      url={https://arxiv.org/abs/2507.20995}, 
}

Abstract

This paper provides an educational case study regarding our experience in deploying ChatGPT Large Language Models (LLMs) in the Spring 2025 and Fall 2023 offerings of ECE 4320: Power System Analysis and Control at Georgia Tech. As part of course assessments, students were tasked with identifying, explaining, and correcting errors in the ChatGPT outputs corresponding to power factor correction problems. While most students successfully identified the errors in the outputs from the GPT-4 version of ChatGPT used in Fall 2023, students found the errors from the ChatGPT o1 version much more difficult to identify in Spring 2025. As shown in this case study, the role of LLMs in pedagogy, assessment, and learning in power engineering classrooms is an important topic deserving further investigation.

VArsity: Can Large Language Models Keep Power Engineering Students in Phase? (Jul 2025)
 Samuel Talkington and Daniel K. Molzahn
 57th North American Power Symposium (NAPS 2025) / arXiv / bibTeX