The development of generative artificial intelligence (GenAI) is reshaping second language (L2) writing by introducing new forms of interaction between learners and intelligent technologies. This conceptual paper examines the potential opportunities and critical challenges of GenAI in L2 writing, drawing on a purposively selected body of recent scholarship. It suggests that GenAI can provide contingent support throughout the writing process, facilitate idea generation and revision, and expand opportunities for multilingual composing through L1-mediated and translanguaging practices. At the same time, excessive reliance on AI may lead to cognitive offloading and weaken learners’ engagement in higher-order writing processes. Concerns about inaccurate information and fabricated citations further highlight the need for independent verification and critical AI literacy. Moreover, unequal access to technological resources, AI literacy, and educational support may contribute to an emerging AI divide and reproduce existing educational and linguistic inequalities. The paper argues that the pedagogical value of GenAI depends on how it is integrated into learners’ writing processes and on whether it supports sustained engagement, learner agency, and reflective thinking and decision-making without displacing their intellectual and authorial responsibilities.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
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
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 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