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:2606. 12428v2 Announce Type: replace-cross Abstract: In this work, we locate and analyze existing undergraduate Artificial Intelligence (AI) programs in the United States in Spring 2026, creating a historic record at a time of great change in this area.
arXiv:2609.36073v1 Announce Type: cross Abstract: The rapid proliferation of large language models (LLMs) in the context of education has introduced significant challenges in enforcement of academic...
arXiv:2606. 12428v1 Announce Type: cross Abstract: We present a report on the status of undergraduate Artificial Intelligence (AI) programs in the United States in Spring 2026.
DataCanvas-EDU is an agentic framework that lets instructors guide the creation of synthetic datasets for business analytics courses. Instructors set teaching goals and desired patterns via conversation, and an AI agent writes generation code, verifies the data, and produces assignments, reference solutions, and rubrics. The process is organized into four phases—Plan, Create, Verify/Test Analysis, and Evaluate—to streamline case preparation and enable students to explore new patterns with AI.
The article discusses how machine learning exercises can be designed for automated assessment tools, framing them as deterministic input-output tasks. It emphasizes that this approach does not create a new grading system but enables existing platforms (e.g., VPL for Moodle, Codeforces, MOJ) to support AI education more effectively. The authors argue that integrating theory with practice through such exercises can foster dynamic, interactive AI courses.
arXiv:2607. 12296v1 Announce Type: cross Abstract: With the increased use of generative AI (GenAI) applications such as ChatGPT, higher education institutions (HEIs) have released a range of guidelines and policies to direct adoption within their institutions.
The study examines how generative AI tools like ChatGPT perform on typical first‑year undergraduate mathematics assessment questions. By generating, transcribing, and blind‑marking AI responses to eight assessments covering the entire curriculum, the authors find that AI attains a first‑class level of performance, with consistency across modules that exceeds that of students in invigilated exams. The results suggest a need to redesign mathematics assessments to address the impact of generative AI.
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:2608.21379v1 Announce Type: new Abstract: Student burnout is highly prevalent in higher education, with reported rates ranging from 12% to over 70% and consistently exceeding those of the worki...
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:2606. 12413v1 Announce Type: cross Abstract: Students at all levels of higher education face a significant barrier in the form of information overload, which often paralyzes the initial stages of the research process and suppresses motivation.