We are responding to calls for students to be trained in using generative AI (GAI). But training must take account of what students know; their prevailing attitudes; and their ethics with respect to using GAI. And knowing what our students know about GAI gives instructors an opportunity to co-construct meaningful teaching and learning moments. A class of 103 students, enrolled in the course Communication in the Engineering Sciences, in our BSc program in Electrical and Computer Engineering, was probed on their use of generative AI. The study draws on two datasets—student responses to a case study on unethical use of GAI and a questionnaire gathering qualitative data. Questionnaire responses reveal that generally students understand the basics of how GAI works. But this understanding is flawed, for example some students erroneously believe that GAI draws in real time from the Internet. Most respondents have used GAI in their studies, particularly to clarify concepts and summarize. We did not find widespread use of GAI for higher cognitive tasks. But this we suspect is linked to program sequencing: as students advance in their program more sophisticated uses of GAI are likely. This supports the need for longitudinal studies which track use in relation to program advancement. Like peers elsewhere, this class expressed concern about GAI's ability to propagate misinformation and bias; GAI-facilitated plagiarism; data privacy; and overreliance leading to impairment of learning. As it relates to whether GAI qualifies as an author, students demonstrated a fairly nuanced understanding of this complex issue. One student felt that this was a gray area, citing that even outside of GAI generated content, scholars build on each other's work to such an extent that the originality of any work can be questioned. In case study responses, all 103 students agreed that using GAI without permission is dishonest. Respondents noted that students, instructors, and university administration each have a responsibility to ensure that GAI is not misused. And like peers elsewhere, they welcome institutional policy and guidance on using GAI.

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.

Free-to-use generative AI (GAI) threatens assessment integrity. The scholarship establishes that GAI can produce passing to highly sophisticated responses to a range of assessment items. And detecting AI-generated output is fraught. Human detection is spotty and there are no proven software solutions at the time of writing. Even where there are promising detection solutions, these are likely to become obsolete as GAI evolves. The challenge for instructors is reliably and consistently establishing authorship of student submissions. There are two main perspectives on academic cheating. Proactive approaches appeal to students' honor and precede submission. Whereas punitive strategies are employed after submission, intending to detect and punish dishonesty. This paper focuses on post-assessment regimes where academic integrity is checked after submission. This work-in-progress, research-to-practice paper collates post-assessment strategies from the literature for detecting unsanctioned use of GAI. Two sets of scholarship were synthesized to answer one question: What post-assessment strategies are there for detecting unsanctioned use of GAI? The first set of literature focusses on post-assessment strategies in general. The second set addresses post-assessment strategies given the proliferation of free-to-use GAI. The Education Resources Information Center (ERIC) database was searched for general post-assessment strategies, yielding five tools. This search of general, non-discipline specific scholarship returns instructors to practices that have been tried and tested before the advent of GAI. It is possible that they can form part of a larger strategy of reducing GAI misuse; but this requires further study. The second set of tools, extracted from scholarship published by IEEE Xplore, totals six. These are post-assessment tools specific to detecting GAI-generated text. The study reveals that as scholars discuss the nature of GAI misuse, there is need to problematize definitions of that misuse. There are several tools available to instructors for detecting dishonest content; no single tool is infallible. Instructors need to think not in terms of a one-off detection solution but a regimen of post-assessment checks to compensate for this fallibility of tools. And there is the need to test tools that work best given teaching and learning contexts.

This work-in-progress, research-to-practice paper examines how students have used generative AI (GAI) productively and with permission to complete their senior capstone course in an undergraduate electrical and computer engineering program. Data were extracted from two sources: faculty lists of permissible use and student feedback. Data show that GAI was used to clarify concepts; generate bibliographies; summarize literature; write code; classify phenomena; edit written work; and produce models. Similar student use has been reported in other engineering education contexts. One striking finding is that some students elected not to use GAI. They reasoned that the capstone project was too high stakes a task to risk submitting incorrect work as they were uncertain about their command of the subject matter to confidently vet the AI's outputs. This study is part of a larger discussion on how to productively deploy GAI in classrooms. It proposes a preliminary cache of uses specific to electrical and computer engineering and it may prove useful to engineering instructors who wish to create assessment tasks that leverage GAI.

The abstract is the quintessential technical writing genre, because abstract writing involves judiciously selecting content and conveying such content in lean, focused writing. This study tested the efficacy of the structured abstract content framework, proposed by Hartley, in the teaching of abstract writing in Electrical and Computer Engineering (ECE). Subject-matter experts and a technical editor independently assessed anonymised abstracts written by four (4) sophomore cohorts based on two (2) source texts. Findings demonstrate that instruction on the structured abstract content framework, as opposed to general abstract instruction, impacts neither content coverage nor writing clarity. However, the study has enabled the authors' to articulate the requirements for the ECE discourse community in a generic, text-blind assessment rubric for abstracts and has provided students with a formal schema for reading technical papers.