Generative Artificial Intelligence in Undergraduate Engineering: A Systematic Literature Review

Hudson James Harris; Javeed Kittur

Download Paper | Permalink

Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: July 12, 2024
Conference Session: Educational Research and Methods Division (ERM) Technical Session 19
Tagged Division: Educational Research and Methods Division (ERM)
Permanent URL: https://peer.asee.org/47492

Request a correction

Paper Authors

biography

Hudson James Harris University of Oklahoma

visit author page

Hudson Harris is a first-year biomedical engineering student at the University of Oklahoma. Fascinated by the potential implications of artificial intelligence (AI) in the coming years, Hudson authored this paper to capture a snapshot of current research on generative AI within undergraduate engineering. This work aims to serve as a foundational resource for ongoing academic discourse and future developments. Hudson's interest in the intersection of AI and biomedical engineering drives his academic pursuits, seeking to explore how these technologies can revolutionize both fields

visit author page

biography

Javeed Kittur University of Oklahoma orcid.org/0000-0001-6132-7304

visit author page

Dr. Kittur is an Assistant Professor in the Gallogly College of Engineering at The University of Oklahoma. He completed his Ph.D. in Engineering Education Systems and Design program from Arizona State University, 2022. He received a bachelor’s degree in Electrical and Electronics Engineering and a Master’s in Power Systems from India in 2011 and 2014, respectively. He has worked with Tata Consultancy Services as an Assistant Systems Engineer from 2011–2012 in India. He has worked as an Assistant Professor (2014–2018) in the department of Electrical and Electronics Engineering, KLE Technological University, India. He is a certified IUCEE International Engineering Educator. He was awarded the ’Ing.Paed.IGIP’ title at ICTIEE, 2018. He is serving as an Associate Editor of the Journal of Engineering Education Transformations (JEET).

He is interested in conducting engineering education research, and his interests include student retention in online and in-person engineering courses/programs, data mining and learning analytics in engineering education, broadening student participation in engineering, faculty preparedness in cognitive, affective, and psychomotor domains of learning, and faculty experiences in teaching online courses. He has published papers at several engineering education research conferences and journals. Particularly, his work is published in the International Conference on Transformations in Engineering Education (ICTIEE), American Society for Engineering Education (ASEE), Computer Applications in Engineering Education (CAEE), International Journal of Engineering Education (IJEE), Journal of Engineering Education Transformations (JEET), and IEEE Transactions on Education. He is also serving as a reviewer for a number of conferences and journals focused on engineering education research.

visit author page

Download Paper | Permalink

Abstract

The dawn of the Fourth Industrial Revolution has ushered in an era where the fusion of digital, physical, and biological worlds is increasingly evident. In this evolving landscape, Artificial Intelligence (AI) has emerged as a major force, reshaping traditional boundaries across various domains. While industry advancements in AI are rapid, the academic realm, responsible for nurturing the future workforce, seems to be progressing at a varied pace. Particularly in the foundational undergraduate years, the urgency to embed AI into the curriculum is pressing. However, a significant gap persists in the literature focusing on the marriage of generative AI and undergraduate engineering. The absence of comprehensive research in this sphere poses dual challenges. Firstly, it hampers the efforts of educators and curriculum designers to effectively infuse cutting-edge AI knowledge into their syllabi. Secondly, the risk looms of a potential disparity between academic teachings and real-world industry requirements, which can culminate in a detrimental skills gap. Addressing this void, our research aspires to act as a bridge. By methodically reviewing existing literature, we aim to offer a cohesive view of generative AI in undergraduate engineering. The overarching goal is to provide actionable insights to educators, policymakers, and curriculum architects, ensuring that future engineers are not only well-versed in their core disciplines but also adept in leveraging AI's expansive capabilities. This research study answers the following research question, “What is the current state, trends, and future of generative AI in Undergraduate Engineering?” will be accomplished through a systematic literature review (SLR). Additionally, to explore, investigate, and categorize the articles retrieved from the databases the focus will be on engineering disciplines, frameworks, research design, data collection, sampling methods and sample sizes. The SLR will include the following phases (I) Explore different academic databases such as Google Scholar, IEEE Explorer, Web of Science, Engineering Village, ERIC, Science Direct, and Wiley Online Library to retrieve articles using the search terms. The search terms include Generative AI or Artificial Intelligence + College + Engineering, AI or Artificial Intelligence + Engineering, Chat GPT + engineering + education, and Undergraduate artificial intelligence. (II) Screening the abstracts and full text of the articles to eliminate papers that are beyond the scope of the research topic. Exclusion criteria such as EC 1: Articles written before 2013, EC 2: Articles not written in English, EC3: Articles not pertaining to engineering, EC 4: Articles not pertaining to generative AI excluding Chat GPT (Deep learning, text generation, vast data input), EC:5 Work-in-progress articles will be excluded, will be used. (III) The articles that make it to the final phase will be reviewed in detail. (IV) This knowledge will be consolidated, synthesized, and examined to find the emergent themes, and a comprehensive review of the current state, trends, and future of generative AI in undergraduate engineering will be completed. The research team is in the process of data collection and will be completed soon. More details on the themes emerging from the synthesis of the articles will be presented in the full paper.

Citation
Format

Harris, H. J., & Kittur, J. (2024, June), Generative Artificial Intelligence in Undergraduate Engineering: A Systematic Literature Review Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47492

TY - CPAPER
AB - The dawn of the Fourth Industrial Revolution has ushered in an era where the fusion of digital, physical, and biological worlds is increasingly evident. In this evolving landscape, Artificial Intelligence (AI) has emerged as a major force, reshaping traditional boundaries across various domains. While industry advancements in AI are rapid, the academic realm, responsible for nurturing the future workforce, seems to be progressing at a varied pace. Particularly in the foundational undergraduate years, the urgency to embed AI into the curriculum is pressing. However, a significant gap persists in the literature focusing on the marriage of generative AI and undergraduate engineering.
The absence of comprehensive research in this sphere poses dual challenges. Firstly, it hampers the efforts of educators and curriculum designers to effectively infuse cutting-edge AI knowledge into their syllabi. Secondly, the risk looms of a potential disparity between academic teachings and real-world industry requirements, which can culminate in a detrimental skills gap. Addressing this void, our research aspires to act as a bridge. By methodically reviewing existing literature, we aim to offer a cohesive view of generative AI in undergraduate engineering. The overarching goal is to provide actionable insights to educators, policymakers, and curriculum architects, ensuring that future engineers are not only well-versed in their core disciplines but also adept in leveraging AI&#39;s expansive capabilities. This research study answers the following research question, “What is the current state, trends, and future of generative AI in Undergraduate Engineering?” will be accomplished through a systematic literature review (SLR). Additionally, to explore, investigate, and categorize the articles retrieved from the databases the focus will be on engineering disciplines, frameworks, research design, data collection, sampling methods and sample sizes.
The SLR will include the following phases (I) Explore different academic databases such as Google Scholar, IEEE Explorer, Web of Science, Engineering Village, ERIC, Science Direct, and Wiley Online Library to retrieve articles using the search terms. The search terms include Generative AI or Artificial Intelligence + College + Engineering, AI or Artificial Intelligence + Engineering, Chat GPT + engineering + education, and Undergraduate artificial intelligence. (II) Screening the abstracts and full text of the articles to eliminate papers that are beyond the scope of the research topic. Exclusion criteria such as EC 1: Articles written before 2013, EC 2: Articles not written in English, EC3: Articles not pertaining to engineering, EC 4: Articles not pertaining to generative AI excluding Chat GPT (Deep learning, text generation, vast data input), EC:5 Work-in-progress articles will be excluded, will be used. (III) The articles that make it to the final phase will be reviewed in detail. (IV) This knowledge will be consolidated, synthesized, and examined to find the emergent themes, and a comprehensive review of the current state, trends, and future of generative AI in undergraduate engineering will be completed. The research team is in the process of data collection and will be completed soon. More details on the themes emerging from the synthesis of the articles will be presented in the full paper.
AU - Hudson James Harris
AU - Javeed Kittur
CY - Portland, Oregon
DA - 2024/06/23
PB - ASEE Conferences
TI - Generative Artificial Intelligence in Undergraduate Engineering: A Systematic Literature Review
UR - https://peer.asee.org/47492
ER -