Generative Artificial Intelligence in Undergraduate Engineering: A Systematic Literature Review

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.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?" and this will be accomplished through a systematic literature review (SLR).The SLR included the following phases (I) Explore different academic databases including 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 beyond the research topic's scope.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), were used.EC 5: Articles not pertaining to undergraduate engineering EC 6: Articles not pertaining

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