Sep 2026· Advances in computational intelligence and robotics book series· pp. 387-408· 15 references
Abstract
This chapter presents a teaching and assessment approach that incorporates generative artificial intelligence (GenAI) into learning while maintaining a focus on students' developing understanding. Drawing on a portfolio-and-interview model implemented in Mathematical Statistics, the approach combines longitudinal portfolios of learning evidence with individual oral interviews. The portfolio records both material collected from external sources, including GenAI where appropriate, and evidence of students' engagement with that material, while the interview provides a complementary opportunity for students to explain and defend their learning. The model is informed by Fink's taxonomy of significant learning, research on desirable difficulties and generative learning, and the AI Assessment Scale. The approach emphasises transparent GenAI use and the cognitive work that remains the responsibility of the student. The chapter describes the creation and assessment of portfolio content, the role of interviews, and practical considerations relating to implementation.
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
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
This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...
This work revisits schema linking when using the latest generation of large language models (LLMs) and finds empirically that newer models are adept at utilizing relevant schema elements during generation even in the presence of large numbers of irrelevant ones.
Karime Maamari, Fadhil Abubaker, Daniel Jaroslawicz et al.· arXiv.org· 109 citations· ⚡19
A novel threat is unveiled in which attackers steer the RAG system's response by injecting malicious passages into its knowledge base, enabling the attacker to steer the response without altering the user input or modifying the RAG weights.
Jiaqi Xue, Meng Zheng, Yebowen Hu et al.· arXiv.org· 109 citations· ⚡8
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MIT News · Artificial Intelligence· news.mit.eduSep 30, 2026
With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.
