{"title":"“教育中的人工智能:个性化学习的机遇与挑战”研究","authors":"E. G. Lydia, P. Vidhyavathi, P. Malathi","doi":"10.36893/iej.2023.v52i05.750-759","DOIUrl":null,"url":null,"abstract":"The use of Artificial Intelligence (AI) in education has the potential to revolutionize the way we teach and learn, making education more personalized, engaging, and effective. AI can be used to develop personalized learning systems that adapt to the needs and abilities of individual learners. These systems can use data such as learner performance and behavior to create customized learning experiences that improve engagement and learning outcomes. AI can also be used to develop intelligent tutoring systems that provide feedback and guidance to learners. These systems can use data analytics to track learner progress and provide targeted feedback and support to help learners achieve their learning objectives. In addition, AI-powered language learning tools, automated grading and assessment, and immersive virtual and augmented reality learning environments are some of the other potential applications of AI in education. However, the adoption of AI in education also brings some challenges, including issues related to privacy, data security, bias, and ethics. It is important to ensure that AI is developed and used in an ethical and responsible manner to avoid any negative consequences. Moreover, the implementation of AI in education requires collaboration and coordination among educators, administrators, developers, and policymakers. This paper reviews the opportunities and challenges of using AI in the field of education, and highlights some of the recent research in this area. The paper concludes with recommendations for future research and practice in the use of AI for personalized learning in education.","PeriodicalId":37850,"journal":{"name":"Journal of Industrial Engineering International","volume":"65 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A STUDY ON \\\"AI IN EDUCATION: OPPORTUNITIES AND CHALLENGES FOR PERSONALIZED LEARNING\",\"authors\":\"E. G. Lydia, P. Vidhyavathi, P. Malathi\",\"doi\":\"10.36893/iej.2023.v52i05.750-759\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The use of Artificial Intelligence (AI) in education has the potential to revolutionize the way we teach and learn, making education more personalized, engaging, and effective. AI can be used to develop personalized learning systems that adapt to the needs and abilities of individual learners. These systems can use data such as learner performance and behavior to create customized learning experiences that improve engagement and learning outcomes. AI can also be used to develop intelligent tutoring systems that provide feedback and guidance to learners. These systems can use data analytics to track learner progress and provide targeted feedback and support to help learners achieve their learning objectives. In addition, AI-powered language learning tools, automated grading and assessment, and immersive virtual and augmented reality learning environments are some of the other potential applications of AI in education. However, the adoption of AI in education also brings some challenges, including issues related to privacy, data security, bias, and ethics. It is important to ensure that AI is developed and used in an ethical and responsible manner to avoid any negative consequences. Moreover, the implementation of AI in education requires collaboration and coordination among educators, administrators, developers, and policymakers. This paper reviews the opportunities and challenges of using AI in the field of education, and highlights some of the recent research in this area. 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A STUDY ON "AI IN EDUCATION: OPPORTUNITIES AND CHALLENGES FOR PERSONALIZED LEARNING
The use of Artificial Intelligence (AI) in education has the potential to revolutionize the way we teach and learn, making education more personalized, engaging, and effective. AI can be used to develop personalized learning systems that adapt to the needs and abilities of individual learners. These systems can use data such as learner performance and behavior to create customized learning experiences that improve engagement and learning outcomes. AI can also be used to develop intelligent tutoring systems that provide feedback and guidance to learners. These systems can use data analytics to track learner progress and provide targeted feedback and support to help learners achieve their learning objectives. In addition, AI-powered language learning tools, automated grading and assessment, and immersive virtual and augmented reality learning environments are some of the other potential applications of AI in education. However, the adoption of AI in education also brings some challenges, including issues related to privacy, data security, bias, and ethics. It is important to ensure that AI is developed and used in an ethical and responsible manner to avoid any negative consequences. Moreover, the implementation of AI in education requires collaboration and coordination among educators, administrators, developers, and policymakers. This paper reviews the opportunities and challenges of using AI in the field of education, and highlights some of the recent research in this area. The paper concludes with recommendations for future research and practice in the use of AI for personalized learning in education.
期刊介绍:
Journal of Industrial Engineering International is an international journal dedicated to the latest advancement of industrial engineering. The goal of this journal is to provide a platform for engineers and academicians all over the world to promote, share, and discuss various new issues and developments in different areas of industrial engineering. All manuscripts must be prepared in English and are subject to a rigorous and fair peer-review process. Accepted articles will immediately appear online. The journal publishes original research articles, review articles, technical notes, case studies and letters to the Editor, including but not limited to the following fields: Operations Research and Decision-Making Models, Production Planning and Inventory Control, Supply Chain Management, Quality Engineering, Applications of Fuzzy Theory in Industrial Engineering, Applications of Stochastic Models in Industrial Engineering, Applications of Metaheuristic Methods in Industrial Engineering.