Sep 2026· Journal of Intelligent Computing System· 0 citations
Abstract
Particularly in terms of supporting writing, artificial intelligence can now do much more. It alters children's reading and writing skills as well as teacher evaluation of their work. Apps that help with language, programs that offer quick feedback, and tools that generate text are all examples of AI that can provide helpful advice, rapid responses, and additional writing practice for students. These fresh technologies provide problems even if they are beneficial and help to save time. Problems with honesty in schoolwork, excessive reliance on computer applications, the equilibrium between appropriately utilizing technology and not, and the loss of critical thinking or creativity comprise these. These obstacles are forcing teachers to reevaluate their teaching methods and ensure AI supports student learning instead of merely dominating. Although AI in education has some limitations and challenges, its potential to be enormously beneficial in the classroom is also quite high. This piece discusses more on how these difficulties come up while teaching writing. The report demands explicit instructions, suitable teacher training, and substantial school support to effectively apply artificial intelligence in order to enhance 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.
Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.
Jinhe Bi, Yifan Wang, Danqi Yan et al.· arXiv.org· 73 citations· ⚡4
This paper proposes adaptive sampling with approximate expected futures (ASAp), a decoding algorithm that guarantees the output to be grammatical while provably producing outputs that match the conditional probability of the LLM's distribution conditioned on the given grammar constraint.
Kanghee Park, Jiayu Wang, Taylor Berg-Kirkpatrick et al.· Neural Information Processin...· 70 citations· ⚡5
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
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