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. 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.