This Element provides the first comprehensive synthesis of AI-IDLE research and practice, grounded in an integration of proactive language learning theory and cultural-historical activity theory, and illustrates how learners negotiate resources and navigate diverse sociotechnical environments to engage in AI-IDLE.
Abstract
Artificial intelligence-mediated informal digital learning of English (AI-IDLE) is a rapidly emerging subfield of computer-assisted language learning that focuses on autonomous, self-directed second language (L2) English development through AI tools beyond the classroom. Extending the established research agenda on informal digital learning of English (IDLE), AI-IDLE responds to a changing context in which AI technologies and digital platforms with embedded AI functionalities are reshaping the ecology of informal language learning. This Element provides the first comprehensive synthesis of AI-IDLE research and practice, grounded in an integration of proactive language learning theory and cultural-historical activity theory. It examines key antecedents and outcomes of AI-IDLE and, through original case studies, illustrates how learners negotiate resources and navigate diverse sociotechnical environments to engage in AI-IDLE. The Element concludes by outlining pedagogical strategies for supporting AI-IDLE and identifying future research directions for advancing this nascent field.
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...
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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...
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With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.