Sep 2026· Comprehensive Journal of Science· 0 citations· 37 references
Abstract
The proliferation of artificial intelligence (AI) has impacted various fields, including education. In the context of foreign language (FL) learning, AI tools are increasingly being adopted by both instructors and students to facilitate the teaching and learning processes. This qualitative study explores the utilization of AI tools by English as a Foreign Language (EFL) postgraduate students at the Libyan Academy to enhance their academic writing skills. Data were collected through semi-structured interviews with 10 MA and PhD students in Applied Linguistics and analyzed using thematic analysis. The analysis revealed several key themes: the types of AI tools engaged, perceived benefits, encountered challenges, ethical considerations, and skill development. Findings indicate that students employ a diverse range of AI tools, with levels of dependency varying from limited consultation for specific tasks to heavy reliance for completing various writing assignments. While participants acknowledged significant benefits, particularly in enhancing their writing proficiency, they also reported drawbacks, such as limited access to premium versions of AI applications. The study concludes with recommendations for policymakers, instructors, and students, emphasizing the need for clear institutional guidelines on AI use, initiatives to raise student awareness of its ethical application, and efforts to mitigate challenges that could otherwise passively hinder the development of academic writing skills.
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 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
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
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