Artificial intelligence (AI) has increasingly been incorporated into English education, yet much of the existing discussion has focused on formal school and university settings. This study examines the use of AI in English as a Foreign Language (EFL) education beyond formal schooling from a lifelong learning perspective. A critical narrative review was conducted to explore how AI may be used across different learning contexts, including early childhood education, supplementary education for school-age learners, English learning for practical purposes among adults, and English learning among older adults. The review suggests that the educational value of AI cannot be determined by its technological functions alone. Learners differ in their goals, degree of choice, available support, and preferred forms of participation, and the same AI function may therefore provide different opportunities and constraints. The review further suggests that AI-assisted task performance should be distinguished from independent language development and from participation in activities that learners themselves value. Effective AI use also requires context-sensitive approaches to feedback, AI literacy and self-regulated learning, privacy protection and responsibilities for learner support, and changes in learning goals over time. Future research should examine AI use in actual out-of-school EFL settings and consider the broader instructional and support systems in which the technology is embedded. This perspective may contribute to a more learner-centered understanding of AI-supported lifelong EFL education.
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