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
arXiv:2608. 12351v1 Announce Type: cross Abstract: Generative artificial intelligence (GenAI) has challenged the validity of unsupervised online assessment, especially in technical subjects where plausible answers can be produced with little effort.
By Riasat Islam (School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom), Thomas Roelleke (School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom)
arXiv:2411.02455v3 Announce Type: replace
Abstract: The rapid adoption of generative AI has created new opportunities for teaching, learning, and quality assurance. Existing applications, however, re...
By Bo Yuan, Jiazi Hu, Haimei Zhao
arXiv:2606. 24839v1 Announce Type: new Abstract: Agentic data analysis systems produce rich outputs, including code, numerical results, and verbal diagnostics.
By Tian Zheng, Kai-Tai Hsu
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
arXiv:2608. 16016v1 Announce Type: cross Abstract: Generative Artificial Intelligence (GenAI) can produce high-quality essays, code, and design artefacts, challenging the validity of conventional assessments that rely on single-point submissions and product-only grading.
By Rajan Kadel, Bellal Hossain, Samar Shailendra, Bushra Naeem
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.
By Felix Muzny, Carolyn Jones, Carter Ithier, Hasnain Sikora, Hrutika Harshadbhai Patel, Carla E. Brodley
arXiv:2606. 07544v1 Announce Type: cross Abstract: Middle school is a key window for building core academic skills and the learning routines students carry into later grades, yet many students still fall behind because help is often limited and comes too late, after they have already been stuck for a while.
By Misan Paul Etchie, Taiwo Olutosin
arXiv:2607. 08255v1 Announce Type: new Abstract: Large language models increasingly serve as teachers generating training data for smaller students.
By Miseong Shawn Kim
arXiv:2609.01591v1 Announce Type: new
Abstract: AI tutors are most useful when they adapt to each student's strengths, weaknesses, and preferred guidance, but evidence about which guidance works for...
By Ke Yang, Chenglong Wang, Michel Galley, Chandan Singh, Jeevana Priya Inala, ChengXiang Zhai, Jianfeng Gao
arXiv:2606. 12422v1 Announce Type: cross Abstract: The integration of large language models (LLMs) into educational assessment represents a transformative shift in classroom grading practices.
By Zewei Tian, Alex Liu, Lief Esbenshade, Michael Xiao, Zachary Zhang, Yulia L\'apicus, Thomas Han, Kevin He, Min Sun
arXiv:2609.21841v1 Announce Type: new
Abstract: Frontier language models now produce professional deliverables that expert graders judge to match human work on a substantial share of economically val...
By Abbas Raza Ali, Muhammad Ajmal Siddiqui, Moona Zahid