Uses of Generative AI in Engineering, Technology, and Computing Classrooms: Findings From the IEEE FIE Conference Proceedings

Generative AI (GAI) can be leveraged to good effect in engineering, technology, and computing (ETC) classrooms but with pre-conditions; among these include teacher preparedness. One dimension of teacher preparedness is knowing how GAI is used in classrooms like their own. This paper responds to that need. The paper surveys the literature to answer three questions: in formal instructional interventions, what were the tasks for which GAI was used; what methods did scholars use to evaluate the GAI instructional set; and what were some of the identified challenges and opportunities regarding the use of GAI in ETC classrooms? Taking account of quality and recency of scholarship, and the need to focus on ETC practices, a rapid review of IEEE FIE conference proceedings for 2023 and 2024 was undertaken. Of the 1,181 papers published in both conference proceedings, and after a two-stage screening process, 20 papers were selected for synthesis. Of the 20 papers examined in this review, we found seven patterns of research design. The most common was the learning and teaching intervention followed by gathering student feedback. While we expect that a bigger corpus will yield additional research designs, these seven provide a start for defining a typology of research design investigating student use of GAI. The review revealed eight distinct categories of use ranging from software development, the most common, with over 26 distinct tasks, to data classification and data analysis, each with two distinct tasks. GAI was found to be used for many low-cognitive tasks like generating bibliographies to cognitivelydemanding uses like design and modeling. The studies reported recurring concerns about using GAI, like perpetuating bias, and hallucinations. One striking empirical finding is that previous ways of accomplishing ETC tasks, like using MATLAB and hand analysis, may be quicker and easier than using GAI. The scholarship advocates for broad and deep GAI literacy programs, addressing GAI abilities, limitations, ethics of use, and prompt design. Moreover, instructors are encouraged to be GAI literate themselves. This know-how is central to designing meaningful GAI-based tasks which seek to prepare students for an increasingly AI-infused world and workplace.