Widespread student adoption of large language models (LLMs) has prompted many CS instructors to assign greater weight to handwritten, proctored assessments. However, this approach struggles to scale as class sizes outpace course staff resources. To address this challenge, our study explores LLM-assisted grading to reduce required grading time. While prior work has emphasized tool accuracy, we evaluate both time and accuracy by comparing outcomes when course staff use an LLM-assisted grader versus Gradescope. We also incorporate a mixed-methods analysis of student and staff perceptions. In a CS1 course of 166 students supported by four teaching assistants (TAs), we observed that LLM-assisted grading reduced overall grading time by 40% compared to Gradescope, with time savings of 48% for exams and 25% for quizzes. Across all assessments, short answer questions showed a 46% time improvement, and free response questions showed a 37% time improvement. In terms of accuracy, accepted regrade requests increased negligibly from 0.1% to 0.5% across three exams and six quizzes. Students were generally neutral about LLM-assisted grading, but stressed the value of TA feedback and oversight. Meanwhile, TAs expressed positive sentiments towards the tool, tempered by concerns of skewed perceptions of students caused by the tool. Overall, these findings indicate that LLM-assisted grading can greatly reduce grading time, with only minor accuracy trade-offs that can be mitigated. As a result, LLM-assisted grading emerges as a promising approach for enhancing grading efficiency in CS courses, meriting further exploration for broader adoption.

Computing Education Research (CER) is increasingly considering digital accessibility, yet the current state of disability inclusion remains unexplored. To understand how and to what extent disabled people and their voices are involved in CER, we conducted a systematic literature review of 68 full papers about accessibility published between 2008 and 2025 in the SIGCSE Technical Symposium, the premiere CER venue. Our analysis reveals that less than half of papers about accessibility involve disabled participants, accessibility research papers are more likely to address visual disabilities or accessibility topics more generally, and use of disability-focused theories or frameworks is uncommon. Based on these findings, we advocate for increased involvement of disabled people in all aspects of computing education research, including in designing and evaluating accessible curricula and prototypes, examining disabled experiences in the classroom, and conducting research itself. We conclude with specific recommendations for methods and frameworks that computing education researchers and teachers can use to meaningfully incorporate disabled perspectives into their practices.

Edge deployment of task-oriented semantic parsers demands high accuracy under tight latency and memory budgets.We present Grammar Pruning, a lightweight zero-shot framework that begins with a user-defined schema of API calls and couples a rule-based entity extractor with an iterative grammar-constrained decoder: extracted items dynamically prune the context-free grammar, limiting generation to only those intents, slots, and values that remain plausible at each step.This aggressive searchspace reduction both reduces hallucinations and slashes decoding time.On the adapted FoodOrdering, APIMIXSNIPS, and APIMIXATIS benchmarks, Grammar Pruning with small language models achieves an average execution accuracy of over 90%-rivaling State-of-the-Art, cloud-based solutions-while sustaining at least 2x lower end-to-end latency than existing methods.By requiring nothing beyond the domain's full API schema values yet delivering precise, real-time natural-language understanding, Grammar Pruning positions itself as a practical building block for future edge-AI applications that cannot rely on large models or cloud offloading.

This work investigates the impact of Large Language Models (LLMs) and the COVID-19 pandemic on student behavior with autograder systems in three programming-heavy courses. We examine whether the release of LLMs like ChatGPT and GitHub Copilot, along with post-pandemic effects, has modified student interactions with autograders. Using data from student submissions over five years, totalling over 4,500 students across over 420,000 submissions, we analyze trends in submission behaviors before and after these events. Our methodology involves tracking submission patterns, focusing on timing, frequency, and score.

With the mainstream adoption of Large Language Models (LLMs) over the last year, members of both academia and the media have raised concerns around the potential impact on student learning and pedagogy. Many students and educators wonder about the pedagogical fit of this emerging technology. We aim to measure the adoption and perception of LLMs among the CS education community in an R1 University to distinguish reality from hype. To this end, we conduct a large survey study targeting three populations participating in computing courses at the university: intro-sequence students (ISS), experienced students (ES), and faculty. Our survey seeks to gather insight around the different populations' perceptions of LLMs in education, as well as how these perceptions may be changing as LLMs improve. Our results show several significant differences across the views of 760 respondents. Most students report LLMs' un-paralleled potential for quick information access, yet many harbor concerns about their reliability and impact on academic integrity. Additionally, while ES rapidly integrate LLMs into their learning, ISS and faculty remain cautious, highlighting a stark contrast in adoption rates. Faculty are unconvinced of LLMs' educational benefits and are concerned about potential challenges in evaluating students' learning outcomes. LLMs are reshaping pedagogical approaches and student engagement. However, with the notable reservations expressed by certain segments, particularly by faculty and ISS, there is an imperative for careful, informed, and ethical integration to ensure that these tools enhance rather than compromise the educational experience.

This full innovative practice paper describes a computational tool designed to optimally match students to industry-sponsored capstone projects in a software engineering capstone course for Computer Science undergraduates at an R1 University (R1U). In the context of these capstone courses, where students stand at the culmination of their academic journey, aligning students' personal learning goals and existing computing skills with team formations becomes critical. This paper presents the Student-Project Matching Tool (SPMT), created to help students find the best available industry-sponsored projects based on their desired learning outcomes, project requirements, and their interests in each project. To choose the learning outcomes they aim to achieve, students can select from a list of predefined software engineering categories and the skills needed to achieve proficiency in each category. The initial list of technical skills for each category was recorded from job postings on a variety of well-known job-search websites, and was further refined by the capstone program's industry partners. Allowing students to select the skills they will work on ensures that they have opportunities and exposure to the skill sets required for employment while still working on one of their most appealing projects. We have developed and piloted the SPMT, which utilizes student vectors to represent their interests and experiences across various software engineering skill sets. Similarly, this tool uses vectors to represent the skills required by each available project, aligning with the exact dimensions as those of the student vectors. The SPMT calculated Euclidean distances between the student interest and project requirement vectors. Next, the resulting Euclidean distances were multiplied with weights associated with students' level of interest in each industry-sponsored project. Subsequently, we framed the student-project matching process as a linear sum assignment problem, aiming to minimize the total sum of Euclidean distances between each student-project pair. The output of the SPMT process consistently matched students with teams that met their software engineering interests and project priorities. Our results reveal increased engagement and growth toward students' desired learning outcomes and computing skills. Specifically, after the first term of the capstone sequence, most students self-reported higher levels of proficiency growth in the skills within their desired software engineering category. This suggests that the SPMT effectively provides students with valuable learning experiences relevant to their career interests and representative of real-world settings.

With the mainstream adoption of Large Language Models (LLMs), members of both academia and the media have raised concerns around their impact on student learning and pedagogy. Many students and educators wonder about the pedagogical fit of this emerging technology. We aim to measure the adoption of and attitudes toward LLMs among the CS student population at an R1 University to determine how students are using these new tools. To this end, we conducted a large survey study targeting two populations participating in computing courses at the university: intro-sequence students (ISS) and experienced students (ES).