Exploring the Performance of Generative AI Tools in Electrical Engineering Education
Conference
Zhong, Z, Wijenayake, C, Edussooriya, CUS. (2023). Exploring the Performance of Generative AI Tools in Electrical Engineering Education
. 10.1109/TALE56641.2023.10398370
Zhong, Z, Wijenayake, C, Edussooriya, CUS. (2023). Exploring the Performance of Generative AI Tools in Electrical Engineering Education
. 10.1109/TALE56641.2023.10398370
This paper explores the use cases and performance of generative artificial intelligence (AI) tools in the context of Electrical Engineering education. A summary of commonly used generative AI tools in Engineering education is provided along with a review of their use cases from recent literature. The performances of the generative AI tools GPT-3.5, GPT-4 and Google Bard are examined using custom-made question sets in two representative courses: Digital Logic Design and Digital Signal Processing. The paper highlights that GPT4 achieves over 50% accuracy in both courses and generative AI tools are more effective at solving standalone concept questions than analytical or numerical questions.
