In the era of artificial intelligence (AI), sustainable education requires not only the integration of advanced technologies but also the cultivation of key competencies such as critical thinking. This study investigates English major students’ engagement with generative AI tools in translation learning, focusing on their usage patterns, critical thinking behaviors, and ethical awareness. A mixed-methods design combining questionnaire data and semi-structured interviews was employed to examine how students interact with AI in translation contexts. The results show that while students frequently use AI for translation, writing, and language learning, their engagement remains primarily functional and efficiency-driven. Although they display openness and flexibility toward AI, their reasoning confidence, self-regulation, and ethical reflection are underdeveloped, indicating surface-level interaction and overreliance on technological authority. These findings highlight the need to cultivate higher-order cognitive and ethical capacities in translation training. Central to this discussion is the conceptualization of the ideal learner in AI-assisted translation, a reflective, autonomous, and ethically aware individual who engages with AI as a partner in cognitive and moral development. Building on this, the study proposes pedagogical strategies that integrate AI literacy, process-oriented assessment, and teacher training to foster critical thinking and responsible AI use in sustainable translation education.
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.