arXiv:2607. 13094v1 Announce Type: cross Abstract: The rise of Artificial Intelligence (AI) enables automatic analysis of large amounts of data.
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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.