Generative AI in Engineering and Computing Education: A Scoping Review of Empirical Studies and Educational

Since the release of diverse generative AI (GenAI) tools such as ChatGPT, Google Gemini,DALL-E, and GitHub Copilot, there has been much debate around the impacts and implications of thesetools on education. Currently, extant literature remarks on the affordances, challenges, and opportunitiesof GenAI, but few studies report and analyze empirical studies and educational practices coming up byGenAI usage in learning settings. Then, in this Scoping Review (ScR) based on 146 studies retrievedfrom the databases SCOPUS, Web of Science (WoS), and ERIC, we analyzed the implications ofintegrating GenAI in engineering and computing education from K-12 to tertiary levels. We adopted anapproach starting from the bibliometric features of the studies in terms of authors, cites, years, or clustertopics, and navigating to the identification of methodologies, strategies, AI literacy instruments andguidelines, learning outcomes, and students’ and teachers’ perceptions, among other features. Weadvocate that current educational practices in engineering and computing with GenAI can indicate to us aroadmap of its potentialities, uses, and risks from the standpoint of both teachers and students, and thiscould help us to create more reflexive methodologies that enhance the teaching-learning process based onthe evidence. Our purpose with the outcomes and conclusions of this scoping review is to supporteducators, faculty members, and other stakeholders in engineering and computing education to co-createeducational methodologies that articulate GenAI with curricula, AI literacy, and prompt engineeringencompassing students’ learning domains such as cognitive, affective, or behavioral. This sample paper ismapped against the IJREK standard student template format.