Findings are interpreted as evidence that course-aware style feedback is promising as a pre-submission revision aid, but that future versions should combine deterministic rule checks with LLM-generated explanations, rule citations, and stronger verification support.
Abstract
This innovative practice full paper presents CodeStylist, a web application that provides course-standard-aware code style feedback for early undergraduate programming courses. CodeStylist addresses a common instructional gap: students are expected to follow local conventions for naming, formatting, comments, organization, and readability, but feedback on these expectations is often delayed or inconsistent. Unlike generic linters or general-purpose LLM prompts, CodeStylist supports course-specific standards, multi-file submissions, and file- and line-localized explanations intended to guide revision rather than grade correctness. We report a formative expert review with 18 instructional staff from one early undergraduate programming course. Participants explored the prototype using self-selected code artifacts and completed a survey about response quality, anticipated student use, and redesign priorities. Ratings indicated modest perceived utility but limited trust: perceived correctness averaged 60.7%, response helpfulness averaged 3.50/5, response usefulness averaged 3.33/5, and anticipated student learning averaged 2.61/5. Despite these concerns, 17/18 respondents expected students to use the tool primarily for style checking, often at least weekly. Open-ended feedback showed that respondents valued CodeStylist for making implicit course standards more visible, but were concerned about unreliable output, overreliance, and latency or cost. We interpret these findings as evidence that course-aware style feedback is promising as a pre-submission revision aid, but that future versions should combine deterministic rule checks with LLM-generated explanations, rule citations, and stronger verification support.
This experience report describes how peer code reviews were implemented in an upper-division software engineering course that focuses on making modifications to large, existing code bases (i.e., brownfield development).
Anshul Shah, Thomas Rexin, Andrew Smithwick et al.· Annual Conference on Innovat...· 0 citations
A web-based misconception repository for Java/Processing is presented, with each entry connecting misconceptions to origins, typical errors, and concrete teaching opportunities, and two teaching-relevance criteria are introduced: Learner Discoverability and Learning Barrier.
Corinna List, Moritz Mueller, Michael Kipp· Proceedings of the 2026 Unit...· 0 citations
The rapid integration of large language models (LLMs) into undergraduate education presents an urgent challenge for engineering instructors. Despite widespread student adoption, there remains a critical lack of domain-specific empirical evidence to guide pedagogical policies and classroom interventions. This manuscript presents a descriptive study design and preliminary findings from an undergraduate engineering mechanics course conducted in Spring 2026. We detail a reproducible survey instrument used to capture student AI usage patterns, attitudes, and verification practices, which are subsequently linked to academic performance metrics. Additionally, we document a deployable sequence of nine structured, instructor-led AI demonstrations designed to model strategic LLM delegation and evaluation. While our preliminary data highlight shifting student behaviors and complex relationships between AI reliance and course outcomes, the primary contribution of this work is the provision of an open-access methodological framework. By making our complete study design, survey tools, and demonstration materials publicly available, we urge other engineering educators to collect and share similar empirical data. Navigating this unprecedented technological shift will require a collaborative, evidence-based approach to fully understand its long-term impacts on student learning.
S. Geng, Helen Lallos-Harrell, Jiya Ashar et al.· 0 citations
This paper introduces CodeOwl, an AI-driven tool that automates the generation of tiered Parsons problems automatically, and evaluated CodeOwl with a mixed-method framework comprising complexity analysis, expert ratings, and user studies.
Luca Cisternino, Florian Obermuller, Gordon Fraser· 0 citations
Delivering high-quality formative feedback in large introductory programming courses remains challenging despite the widespread use of automated grading systems. Although large language models (LLMs) can generate natural-language explanations grounded in student code and test outcomes, their diagnostic reliability and pedagogical suitability require systematic evaluation. This study presents a comparative analysis of six LLMs for generating formative feedback on introductory Java programs containing predefined defects under controlled conditions. Model outputs were evaluated across key dimensions of feedback quality and performance. Results reveal substantial cross-model variation, particularly in multi-defect scenarios. More precise fault localization was associated with higher diagnostic accuracy and completeness, but also with more frequent instructional-constraint violations. No single model consistently outperformed others across all criteria, and response latency did not reliably correspond to diagnostic quality. These findings underscore the context-dependent nature of LLM-generated formative programming feedback.
Melina Najimi, Saba Yazdani, Marzieh Ahmadzadeh· Proceedings of the Canadian...· 0 citations
Comparisons show that measured signal depends on production context and that process patterns can complement weak final-code signal in educational repositories, and that such models are appropriate only as instructor-mediated decision support, not as independent proof of authorship.
