Oct 2026· Intersection A Journal at the Intersection of Assessment and Learning
Artificial Intelligence in Education
Abstract
Artificial intelligence (AI) is shaping the future of higher education, tasking educators with transforming teaching, learning, and assessment practices to consider how AI can enhance the educational environment. With its increasing availability, it is important for administrators, faculty, and students to explore the practical nature of AI use prior to entry into the workforce. Faculty must also reevaluate how they approach assessing student learning, tasking them to rethink each assessment’s purpose, how it will inform future practice, and how it will prepare learners to use AI. Furthermore, faculty must also be prepared to consider the collaborative nature of AI, transforming their mindset to consider AI as an active co-designer and partner, one that does not seek to diminish the educator’s human judgment. This manuscript will articulate the importance of co-design, where AI and educators collaboratively shape assessment tools, and serve to champion feedback partnerships that enhance reflective practice for both students and faculty.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9
