arXiv:2609.24348v1 Announce Type: cross
Abstract: AI coding agents increasingly support software development beyond code completion, including planning, implementation, testing, and repository-level...
By Andreas Martin, Sandro Schwander
AI coding agents increasingly support software development beyond code completion, including planning, implementation, testing, and repository-level task execution. Their practical use, however, often...
arXiv:2606. 31589v1 Announce Type: cross Abstract: Organisations designing, developing, and deploying machine learning systems (MLS) need to be able to check that these systems are trustworthy, and communicate this clearly to their stakeholders, be they different categories of users, engineers, or wider society.
By Amel Bennaceur, Gopi Krishnan Rajbahadur, Prince Mercy, Bashar Nuseibeh, Faeq Alrimawi
arXiv:2608.21391v1 Announce Type: cross
Abstract: In this research-to-practice paper we present a survey that can be used to assess students' AI knowledge. As the use of artificial intelligence (AI),...
By Aditya Johri, Cory Brozina, Akriti Bagale
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.
By Felix Muzny, Carolyn Jones, Carter Ithier, Hasnain Sikora, Hrutika Harshadbhai Patel, Carla E. Brodley
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.
By Artur Jordao