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:2608. 07475v1 Announce Type: cross Abstract: Generative Artificial Intelligence (GenAI) presents a governance challenge for STEM assessment.
By Yizhu Gao, Zhongzhou Chen, Min Li, Xiaoming Zhai
arXiv:2606. 30655v1 Announce Type: cross Abstract: AI-native course assessments in senior computer science courses and related fields should grade students by \emph{AI-resilient skill}: the ability to achieve outcomes beyond a strong AI baseline.
By Anshumali Shrivastava
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:2607. 01255v1 Announce Type: cross Abstract: Universities have responded to generative artificial intelligence (GenAI) in noticeably different ways, both internationally and within Spain.
By Jessica D\'iaz, Sonia Linio, Fernando Pescador, Daniel Martin-Fabiani
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