McGill, S., & McGill, R. (2024, April), WIP: Generative AI as an Enhanced Study Aid in Engineering Courses  Paper presented at ASEE Mid-Atlantic Section Spring Conference, George Washington University, District of Columbia. 10.18260/1-2--45744

\bibitem{asee_peer_45744}
  McGill, S., \& McGill, R. (2024, April), \emph{WIP: Generative AI as an Enhanced Study Aid in Engineering Courses}
  Paper presented at ASEE Mid-Atlantic Section Spring Conference, George Washington University, District of Columbia.
  10.18260/1-2--45744

Stephen McGill Jr and Rebecca McGill.  "WIP: Generative AI as an Enhanced Study Aid in Engineering Courses".  ASEE Mid-Atlantic Section Spring Conference, George Washington University, District of Columbia, 2024, April.  ASEE Conferences, 2024.  https://peer.asee.org/45744 Internet.  14 Jun, 2025

\bibitem{asee_peer_45744}
  Stephen McGill Jr and Rebecca McGill.
  "WIP: Generative AI as an Enhanced Study Aid in Engineering Courses".
  \emph{ASEE Mid-Atlantic Section Spring Conference, George Washington University, District of Columbia, 2024, April}.
  ASEE Conferences, 2024.
  https://peer.asee.org/45744 Internet.  14 Jun, 2025

@INPROCEEDINGS{asee_peer_45744
author = "Stephen McGill Jr and Rebecca McGill"
title = "WIP: Generative AI as an Enhanced Study Aid in Engineering Courses"
booktitle = "ASEE Mid-Atlantic Section Spring Conference"
year = "2024"
month = "April"
address = "George Washington University, District of Columbia"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/45744}
number = {10.18260/1-2--45744}
}

TY  - CPAPER
AB  - Engineering classrooms are marked by their balance between building foundational theory and giving examples to solidify understanding. Student performance improves with increased quantity and diversity of example problems; however, faculty members, especially those early in their careers, can find it burdensome to provide enough quality example problems to accommodate student interests and learning styles. Using Generative Artificial Intelligence (AI) can provide a novel approach to fill the gap between faculty resources and student demand.
In our work, we assess the exam performance of undergraduate mechanical engineers when taking a course covering vibration analysis, both with and without the availability of Generative AI tools. For this single course, we analyzed the performance of the students over two semesters – the first shortly prior to the public release of the ChatGPT AI chatbot; the second shortly after the release of the tool – while introducing ChatGPT as a direct learning tool in the second semester of the course. The professor showed relevant example conversations with the AI chatbot, to pursue equitable and effective usage of new AI tooling by the student population. Following the professor’s example conversations, students can ask the chatbot to generate more examples, and the chatbot will also attempt to give and explain solutions. Students can also ask the chatbot follow-up questions to deepen their understanding and can interact with the AI tool when they study outside of the classroom.
During class time, students receive exams that include straightforward computing of characteristic values relevant to vibration systems. Additionally, the exams contain word problems that require determining component values from design specifications. With exams designed for similar difficulty from semester to semester, we inspect performance differences in consecutive junior-level cohorts, specifically looking at performance on direct computation and on word problems with design decisions. This ongoing work seeks to capture the effects of embedding generative AI within the classroom. As the student body begins to adopt generative AI tooling, we see a unique opportunity to characterize the effects of AI integration.
AU  - Stephen McGill Jr
AU  - Rebecca McGill
CY  - George Washington University, District of Columbia
DA  - 2024/04/19
PB  - ASEE Conferences
TI  - WIP: Generative AI as an Enhanced Study Aid in Engineering Courses
UR  - https://peer.asee.org/45744
DO  - 10.18260/1-2--45744
ER  -