Board 48: Perceptions of ChatGPT on Engineering Education: A 2022-2023 Exploratory Literature Review
- Conference
- Location
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Portland, Oregon
- Publication Date
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June 23, 2024
- Start Date
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June 23, 2024
- End Date
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July 12, 2024
- Conference Session
- Tagged Division
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Computers in Education Division (COED)
- Permanent URL
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https://peer.asee.org/47044
Paper Authors
Trini Balart Texas A&M University
Trinidad Balart is a PhD student at Texas A&M University. She completed her Bachelors of Science in Computer Science engineering from Pontifical Catholic University of Chile. She is currently pursuing her PhD in Multidisciplinary Engineering with a focus in engineering education and the impact of AI on education. Her main research interests include Improving engineering students' learning, innovative ways of teaching and learning, and how artificial intelligence can be used in education in a creative and ethical way.
Kristi J. Shryock Texas A&M University
Dr. Kristi J. Shryock is the Frank and Jean Raymond Foundation Inc. Endowed Associate Professor in Multidisciplinary Engineering and Affiliated Faculty in Aerospace Engineering in the College of Engineering at Texas A&M University. She also serves as Director of the Craig and Galen Brown Engineering Honors Program. She received her BS, MS, and PhD from the College of Engineering at Texas A&M. Kristi works to improve the undergraduate engineering experience through evaluating preparation in areas, such as mathematics and physics, evaluating engineering identity and its impact on retention, incorporating non-traditional teaching methods into the classroom, and engaging her students with interactive methods.
Abstract
This exploratory literature review delves into the perceptions and implications of ChatGPT within engineering education, based on publications from December 2022 to October 2023. It aims to uncover how this generative AI, known for its natural language processing prowess, influences pedagogical practices and learning outcomes in the engineering field. The research question addressed in this literature review is "What are the perceptions related to implications ChatGPT may have on the future of engineering education?”. This question investigates the anticipated impacts of ChatGPT on the future of engineering education, extending to its potential applications, limitations, and broader educational implications. The review adopts a exploratory approach, utilizing a qualitative methodology to sift through relevant literature, guided by keywords related to ChatGPT and engineering education. Key findings suggest that ChatGPT can significantly bolster constructivist learning by supporting problem-solving, hands-on learning, and experiential activities. The review also sheds light on how ChatGPT has been integrated into engineering education, assessing its effectiveness through various pedagogical frameworks and evaluation methodologies, including formative and summative assessments. The study also contemplates the ethical considerations and challenges associated with integrating ChatGPT into educational settings, particularly concerning academic integrity and the ethical use of AI. By juxtaposing different perspectives and methodologies, the review encapsulates a holistic view of ChatGPT's pedagogical potential and its limitations. This exploratory literature review not only expands our understanding of the pedagogical implications of ChatGPT in engineering education, but also lays the groundwork for future research. It underscores the importance of leveraging the technological advances and benefits of AI in education, advocating a nuanced approach to integrating ChatGPT into the educational landscape. The results of this review hope to provide valuable insights into assessment methods, help educators evaluate the effectiveness of ChatGPT in engineering education, and pave the way for future research in this rapidly evolving field.
Balart, T., & Shryock, K. J. (2024, June), Board 48: Perceptions of ChatGPT on Engineering Education: A 2022-2023 Exploratory Literature Review Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47044
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