arXiv:2606. 08400v1 Announce Type: cross Abstract: Graduate-level research reading report assessment creates a substantial labor burden for educators.
arXiv:2606. 03090v1 Announce Type: cross Abstract: The emergence of large language models (LLMs) has significantly accelerated recent research on LLM-based automatic grading (AG) systems.
The paper "Limits of LLM Text Detectors in Education" argues that existing LLM‑generated text detectors assume a binary human/LLM distinction, which fails to capture realistic student‑AI collaboration. It introduces a contribution‑aware evaluation framework with eight student contribution levels and presents GEDE, a benchmark of over 900 human‑written and 12,500 generated essays across 886 tasks. Using GEDE, the authors evaluate four detection methods and find that most detectors perform poorly on intermediate contribution levels, especially LLM‑assisted revisions, raising concerns about false accusations.
arXiv:2607. 13094v1 Announce Type: cross Abstract: The rise of Artificial Intelligence (AI) enables automatic analysis of large amounts of data.
The rise of Artificial Intelligence (AI) enables automatic analysis of large amounts of data. Previously time-consuming and labor-intensive tasks can be completed much more efficiently with the use of AI.
arXiv:2607. 10674v1 Announce Type: cross Abstract: As AI code tools become integrated into programming environments, students increasingly describe intended behavior in natural language and rely on these tools to generate code, shifting emphasis from code writing to specification.
arXiv:2606. 12864v1 Announce Type: cross Abstract: Despite strong performance in competitive programming, the role of Large Language Models (LLMs) in supporting human learning in the same setting remains largely unexplored.
arXiv:2605. 16336v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have made fluent essay writing, code drafting, and quiz answering instantly available to students at every level, from secondary school through graduate study.
The paper introduces CodeInsight, a large-scale dataset of over 3 million code submissions from 3,286 undergraduate students in two introductory C++ courses, capturing test‑case outcomes, timestamps, and source code. It presents a benchmark that evaluates various modeling approaches—including a Recurrent State Space Model and an LLM‑based predictor—on their ability to predict iterative problem‑solving dynamics such as performance changes and error persistence. The study finds that the RSSM outperforms other models on most courses, while the LLM generates full submissions but with lower predictive accuracy, suggesting it functions more as a generative solver than a behavior predictor.
arXiv:2608. 16318v1 Announce Type: cross Abstract: Recent advances in Generative Artificial Intelligence (GenAI) have substantially improved the ability of large language models (LLMs) to generate and explain source code.
arXiv:2607. 24757v1 Announce Type: cross Abstract: This paper reports on the rapid development and classroom deployment of a Thonny log visualizer built using AI-assisted ``vibe coding'' to make students' programming processes easily visible to teachers.
The paper presents ESSE, a self‑explanation tutor that uses a large language model to give immediate feedback on students’ line‑by‑line explanations of introductory programming worked examples. It evaluates the LLM’s judgments against a domain expert and a crowd of non‑experts, finding that the model is reliable enough to serve as the tutor’s assessment engine. In an introductory Java course, the tutor’s feedback encourages students to persist, improves the completeness and conceptual depth of their explanations, and shows evidence of learning.