Exploring Student Perceptions of Learning Experience in Fundamental Mechanics Courses Enhanced by ChatGPT

Milad Rezvani Rad; Julian Ly Davis

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: June 26, 2024
Conference Session: Understanding the Student Experience in Mechanics Courses
Tagged Division: Mechanics Division (MECHS)
Page Count: 15
DOI: 10.18260/1-2--47426
Permanent URL: https://peer.asee.org/47426
Download Count: 123

Paper Authors

biography

Milad Rezvani Rad University of Southern Indiana

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Dr. Milad Rad is an Assistant Professor in the Engineering Department at the University of Southern Indiana. He earned his Ph.D. in Mechanical Engineering from the University of Alberta in Canada. Besides his specialization in functional thermally sprayed coatings, he explores innovative AI-driven approaches to enhance student engagement in the classroom.

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Julian Ly Davis University of Southern Indiana orcid.org/0000-0003-4109-3904

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Jul Davis is an Associate Professor of Engineering at the University of Southern Indiana in Evansville, Indiana. He received his PhD in 2007 from Virginia Tech in Engineering Mechanics where he studied the vestibular organs in the inner ear using finite element models and vibration analyses. After graduating, he spent a semester teaching at a local community college and then two years at University of Massachusetts (Amherst) studying the biomechanics of biting in bats and monkeys, also using finite element modeling techniques. In 2010, he started his career teaching in all areas of mechanical engineering at the University of Southern Indiana. He loves teaching all of the basic mechanics courses, and of course his Vibrations and Finite Element Analysis courses.

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Abstract

Enhancing the engagement and boosting the learning experience of students in foundational engineering courses have always been among the top priorities of professors. The rapid recent technological advancements of artificial intelligence (AI) have brought about promising pedagogical opportunities for engineering students with more efficient learning tools. This manuscript aims to study the prospects and obstacles in using a state-of-the-art natural language processing (NLP) model for obtaining a deeper understanding of core engineering courses. In this regard, several engineering examples were explored for analyzing the accuracy of quantitative results obtained from ChatGPT. In-class surveys were also conducted to assess the enthusiasm of students and enhanced interactivity of implementing ChatGPT-powered educational platform in solving engineering problems. We discovered that students can noticeably benefit from the key beneficial features offered by artificial intelligence including, but not limited to, real-time assistance, personalized feedback, and dynamic content generation. Survey results highlight the positive impact of implementation of ChatGPT on engineering students' scholarly performance and their broader learning experience. Despite all the undeniable advantages AI offers, it is essential to exercise caution and thorough analysis when evaluating the final results as the final outcomes are not always correct. Not only can incorrect results be discouraging, but they can also mislead students and hinder their potential ability to engage in deep, critical thinking. Regardless of the accuracy of the results, it is beyond doubt that ChatGPT is a valuable tool for educators in the field of mechanical engineering who are enthusiastic in offering an innovative approach to foster deep understanding and interest in fundamental engineering concepts. It is strongly believed that the limitations of the current ChatGPT model can be addressed and rectified in future iterations of the model, making the future of AI-driven education more promising, and establishing the generative models as flawless and reliable resources for both students and educators in STEM fields.

Citation
Format

Rezvani Rad, M., & Davis, J. L. (2024, June), Exploring Student Perceptions of Learning Experience in Fundamental Mechanics Courses Enhanced by ChatGPT Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--47426

TY  - CPAPER
AB  - Enhancing the engagement and boosting the learning experience of students in foundational engineering courses have always been among the top priorities of professors. The rapid recent technological advancements of artificial intelligence (AI) have brought about promising pedagogical opportunities for engineering students with more efficient learning tools. This manuscript aims to study the prospects and obstacles in using a state-of-the-art natural language processing (NLP) model for obtaining a deeper understanding of core engineering courses. In this regard, several engineering examples were explored for analyzing the accuracy of quantitative results obtained from ChatGPT. In-class surveys were also conducted to assess the enthusiasm of students and enhanced interactivity of implementing ChatGPT-powered educational platform in solving engineering problems. We discovered that students can noticeably benefit from the key beneficial features offered by artificial intelligence including, but not limited to, real-time assistance, personalized feedback, and dynamic content generation. Survey results highlight the positive impact of implementation of ChatGPT on engineering students&#39; scholarly performance and their broader learning experience. Despite all the undeniable advantages AI offers, it is essential to exercise caution and thorough analysis when evaluating the final results as the final outcomes are not always correct. Not only can incorrect results be discouraging, but they can also mislead students and hinder their potential ability to engage in deep, critical thinking. Regardless of the accuracy of the results, it is beyond doubt that ChatGPT is a valuable tool for educators in the field of mechanical engineering who are enthusiastic in offering an innovative approach to foster deep understanding and interest in fundamental engineering concepts. It is strongly believed that the limitations of the current ChatGPT model can be addressed and rectified in future iterations of the model, making the future of AI-driven education more promising, and establishing the generative models as flawless and reliable resources for both students and educators in STEM fields.
AU  - Milad Rezvani Rad
AU  - Julian Ly Davis
CY  - Portland, Oregon
DA  - 2024/06/23
PB  - ASEE Conferences
TI  - Exploring Student Perceptions of Learning Experience in Fundamental Mechanics Courses Enhanced by ChatGPT
UR  - https://peer.asee.org/47426
DO  - 10.18260/1-2--47426
ER  -