While artificial intelligence(AI)shows promise in education,its real-world effectiveness in specific settings like blended English as a Foreign Language(EFL)learning needs closer examination.This study investigated th...While artificial intelligence(AI)shows promise in education,its real-world effectiveness in specific settings like blended English as a Foreign Language(EFL)learning needs closer examination.This study investigated the impact of a blended teaching model incorporating AI tools on the Superstar Learning Platform for Chinese university EFL students.Using a mixed-methods approach,60 first-year students were randomized into an experimental group(using the AI-enhanced model)and a control group(traditional instruction)for 16 weeks.Data included test scores,learning behaviors(duration,task completion),satisfaction surveys,and interviews.Results showed the experimental group significantly outperformed the control group on post-tests and achieved larger learning gains.These students also demonstrated greater engagement through longer study times and higher task completion rates,and reported significantly higher satisfaction.Interviews confirmed these findings,with students attributing benefits to the model’s personalized guidance,structured content presentation(knowledge graphs),immediate responses,flexibility,and varied interaction methods.However,limitations were noted,including areas where the platform’s AI could be improved(e.g.,for assessing speaking/translation)and ongoing challenges with student self-discipline.The study concludes that this AI-enhanced blended model significantly improved student performance,engagement,and satisfaction in this EFL context.The findings offer practical insights for educators and platform developers,suggesting AI integration holds significant potential while highlighting areas for refinement.展开更多
Let there be light-to change the world we want to be!Over the past several decades,and ever since the birth of the first laser,mankind has witnessed the development of the science of light,as light-based technologies ...Let there be light-to change the world we want to be!Over the past several decades,and ever since the birth of the first laser,mankind has witnessed the development of the science of light,as light-based technologies have revolutionarily changed our lives.Needless to say,photonics has now penetrated into many aspects of science and technology,turning into an important and dynamically changing field of increasing interdisciplinary interest.In this inaugural issue of eLight,we highlight a few emerging trends in photonics that we think are likely to have major impact at least in the upcoming decade,spanning from integrated quantum photonics and quantum computing,through topological/non-Hermitian photonics and topological insulator lasers,to AI-empowered nanophotonics and photonic machine learning.This Perspective is by no means an attempt to summarize all the latest advances in photonics,yet we wish our subjective vision could fuel inspiration and foster excitement in scientific research especially for young researchers who love the science of light.展开更多
In recent years,Artificial Intelligence(AI)-based weather prediction models have emerged as powerful tools in meteorology,capable of learning complex dependencies from extensive weather datasets and generating rapid f...In recent years,Artificial Intelligence(AI)-based weather prediction models have emerged as powerful tools in meteorology,capable of learning complex dependencies from extensive weather datasets and generating rapid forecasts after training.These models achieve prediction accuracies comparable to state-of-the-art Numerical Weather Prediction(NWP)systems.However,these models remain not fully operational due to their dependence on computationally intensive Data Assimilation(DA)systems for generating accurate initial fields.Recent advances in AI techniques offer a potential pathway to develop more efficient and accurate DA systems,advancing the operational feasibility of end-to-end AI-based weather forecasting.Despite growing interest,research in AI-based DA remains fragmented.Therefore,a comprehensive review is necessary to clarify the current progress,identify challenges,and guide the future development of next-generation AI-based DA systems.This review categorizes AI-based DA research into two primary domains.The first domain is AI-empowered DA,where AI enhances individual components such as observation operators,tangent linear and adjoint models,and uncertainty quantification.It also includes latent DA,which helps reduce computational costs.The second domain is AI-based end-to-end DA models,which integrate observations and short-range weather predictions within unified AI frameworks to generate accurate initial fields.We further discuss key challenges and opportunities,including dataset standardization,model evaluation protocols,assimilation of extended observation types,enforcement of physical constraints,and addressing operational scalability.Finally,we emphasize the importance of interdisciplinary collaboration across AI and meteorology in developing practical and reliable AI solutions to enhance DA processes and support more accurate weather forecasting.This review offers practical insights to the research community to expedite the development and operationalization of AI-based DA and end-to-end weather forecasting systems.展开更多
基金supported by the 2024“Special Research Project on the Application of Artificial Intelligence in Empowering Teaching and Education”of Zhejiang Province Association of Higher Education(KT2024165).
文摘While artificial intelligence(AI)shows promise in education,its real-world effectiveness in specific settings like blended English as a Foreign Language(EFL)learning needs closer examination.This study investigated the impact of a blended teaching model incorporating AI tools on the Superstar Learning Platform for Chinese university EFL students.Using a mixed-methods approach,60 first-year students were randomized into an experimental group(using the AI-enhanced model)and a control group(traditional instruction)for 16 weeks.Data included test scores,learning behaviors(duration,task completion),satisfaction surveys,and interviews.Results showed the experimental group significantly outperformed the control group on post-tests and achieved larger learning gains.These students also demonstrated greater engagement through longer study times and higher task completion rates,and reported significantly higher satisfaction.Interviews confirmed these findings,with students attributing benefits to the model’s personalized guidance,structured content presentation(knowledge graphs),immediate responses,flexibility,and varied interaction methods.However,limitations were noted,including areas where the platform’s AI could be improved(e.g.,for assessing speaking/translation)and ongoing challenges with student self-discipline.The study concludes that this AI-enhanced blended model significantly improved student performance,engagement,and satisfaction in this EFL context.The findings offer practical insights for educators and platform developers,suggesting AI integration holds significant potential while highlighting areas for refinement.
基金support from the National Key R&D Program of China under Grant(No.2017YFA0303800).MS acknowledges support from the Israel Science Foundation.
文摘Let there be light-to change the world we want to be!Over the past several decades,and ever since the birth of the first laser,mankind has witnessed the development of the science of light,as light-based technologies have revolutionarily changed our lives.Needless to say,photonics has now penetrated into many aspects of science and technology,turning into an important and dynamically changing field of increasing interdisciplinary interest.In this inaugural issue of eLight,we highlight a few emerging trends in photonics that we think are likely to have major impact at least in the upcoming decade,spanning from integrated quantum photonics and quantum computing,through topological/non-Hermitian photonics and topological insulator lasers,to AI-empowered nanophotonics and photonic machine learning.This Perspective is by no means an attempt to summarize all the latest advances in photonics,yet we wish our subjective vision could fuel inspiration and foster excitement in scientific research especially for young researchers who love the science of light.
基金supported by the National Natural Science Foundation of China(Grant Nos.42205161,42405146,42430612&42275170)。
文摘In recent years,Artificial Intelligence(AI)-based weather prediction models have emerged as powerful tools in meteorology,capable of learning complex dependencies from extensive weather datasets and generating rapid forecasts after training.These models achieve prediction accuracies comparable to state-of-the-art Numerical Weather Prediction(NWP)systems.However,these models remain not fully operational due to their dependence on computationally intensive Data Assimilation(DA)systems for generating accurate initial fields.Recent advances in AI techniques offer a potential pathway to develop more efficient and accurate DA systems,advancing the operational feasibility of end-to-end AI-based weather forecasting.Despite growing interest,research in AI-based DA remains fragmented.Therefore,a comprehensive review is necessary to clarify the current progress,identify challenges,and guide the future development of next-generation AI-based DA systems.This review categorizes AI-based DA research into two primary domains.The first domain is AI-empowered DA,where AI enhances individual components such as observation operators,tangent linear and adjoint models,and uncertainty quantification.It also includes latent DA,which helps reduce computational costs.The second domain is AI-based end-to-end DA models,which integrate observations and short-range weather predictions within unified AI frameworks to generate accurate initial fields.We further discuss key challenges and opportunities,including dataset standardization,model evaluation protocols,assimilation of extended observation types,enforcement of physical constraints,and addressing operational scalability.Finally,we emphasize the importance of interdisciplinary collaboration across AI and meteorology in developing practical and reliable AI solutions to enhance DA processes and support more accurate weather forecasting.This review offers practical insights to the research community to expedite the development and operationalization of AI-based DA and end-to-end weather forecasting systems.
