The expansion of undergraduate CS programs brings different forms of student identity, sociotechnical perspectives, and intersectionality into the classroom. These background factors affect student understanding of the world, and, consequently, their work in computing ethic classes. Instructors of computing ethics courses therefore must facilitate topics that are not only pertinent to modern technologies but that are also interesting for students from a range of backgrounds. In this work, we introduce a low-overhead, natural language processing tool that can assist instructors in extracting student talking points from over 600 discussion forum posts in a large-scale undergraduate computing ethics course. When compared to large language model approaches, this n-gram-based scripting tool is more effective in selecting popular quotes and summarizing course discussion. This tool is simple in implementation and can be easily adapted by instructors to prepare for classroom discussion.

This work explores computing ethics education through a sociological lens, focusing on education's dual role in reflecting and challenging Silicon Valley's hegemonic force. We present a case study of a one-unit computing ethics course at a R1 public university. Discussion-based assignments can foster accessible ethical discussions and scaffold "water cooler" talk among students; these informal conversations provide a starting point for critical engagement with ethical dilemmas. However, as a standalone offering, the course can reinforce the perception of ethics as secondary to technical skills, further highlighting the need to reimagine an embedded computing ethics education that better prepares students to critically engage with and reshape sociotechnical systems.

This study examines the challenges and opportunities of teaching computing ethics within the context of a large, low-workload, standalone course. CS199 is a one-unit, pass/fail computing ethics course designed to provide students with exposure to a wide array of topics and promote critical peer-based engagement. We leverage submitted work via Question, Quote, Comment, and Replies (QQCRs) and podcasts to facilitate discussions outside the classroom. While QQCRs have shown promise in promoting engagement and exposing students to diverse perspectives, limitations remain in stimulating deeper critiques of the material. We reflect on the effectiveness of asynchronous discussion and its alignment with broader learning goals in computing ethics education.

This study investigates how large language models (LLMs) can identify the presence of learning outcomes within student submitted work in a computing ethics course. To do so, we craft a codebook to spot key learning outcomes, such as the usage of critical reasoning and awareness of various social issues. We leverage the GPT-4o and GPT-3.5-turbo LLMs to apply codes onto 8,500 pieces of student submitted work. We then use Cohen's kappa to assess interrater reliability and compare human reviewers' coding to outputs from those models, finding that GPT-4o performed just as well as the agreement between human reviewers. We then use the model outputs to identify specific course readings that students engaged particularly deeply with to better inform our computing ethics instruction.

Current strategies to improve graft outcome following kidney transplantation consider information at the human leukocyte antigen (HLA) loci. Cell surface antigens, in addition to HLA, may serve as the stimuli as well as the targets for the anti-allograft immune response and influence long-term graft outcomes. We therefore performed exome sequencing of DNA from kidney graft recipients and their living donors and estimated all possible cell surface antigens mismatches for a given donor/recipient pair by computing the number of amino acid mismatches in trans-membrane proteins. We designated this tally as the allogenomics mismatch score (AMS). We examined the association between the AMS and post-transplant estimated glomerular filtration rate (eGFR) using mixed models, considering transplants from three independent cohorts (a total of 53 donor-recipient pairs, 106 exomes, and 239 eGFR measurements). We found that the AMS has a significant effect on eGFR (mixed model, effect size across the entire range of the score: -19.4 [-37.7, -1.1], P = 0.0042, χ2 = 8.1919, d.f. = 1) that is independent of the HLA-A, B, DR matching, donor age, and time post-transplantation. The AMS effect is consistent across the three independent cohorts studied and similar to the strong effect size of donor age. Taken together, these results show that the AMS, a novel tool to quantify amino acid mismatches in trans-membrane proteins in individual donor/recipient pair, is a strong, robust predictor of long-term graft function in kidney transplant recipients.