{"title":"Exploring the integration of IoT and Generative AI in English language education: Smart tools for personalized learning experiences","authors":"Wanjin Dong , Daohua Pan , Soonbae Kim","doi":"10.1016/j.jocs.2024.102397","DOIUrl":null,"url":null,"abstract":"<div><p>English language education is undergoing a transformative shift, propelled by advancements in technology. This research explores the integration of the Internet of Things (IoT) and Generative Artificial Intelligence (Generative AI) in the context of English language education, with a focus on developing a personalized oral assessment method. The proposed method leverages real-time data collection from IoT devices and Generative AI's language generation capabilities to create a dynamic and adaptive learning environment. The study addresses historical challenges in traditional teaching methodologies, emphasizing the need for AI approaches. The research objectives encompass a comprehensive exploration of the historical context, challenges, and existing technological interventions in English language education. A novel, technology-driven oral assessment method is designed, implemented, and rigorously evaluated using datasets such as Librispeech and L2Arctic. The ablation study investigates the impact of training dataset proportions and model learning rates on the method's performance. Results from the study highlight the importance of maintaining a balance in dataset proportions, selecting an optimal learning rate, and considering model depth in achieving optimal performance.</p></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"82 ","pages":"Article 102397"},"PeriodicalIF":3.1000,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Science","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S187775032400190X","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
English language education is undergoing a transformative shift, propelled by advancements in technology. This research explores the integration of the Internet of Things (IoT) and Generative Artificial Intelligence (Generative AI) in the context of English language education, with a focus on developing a personalized oral assessment method. The proposed method leverages real-time data collection from IoT devices and Generative AI's language generation capabilities to create a dynamic and adaptive learning environment. The study addresses historical challenges in traditional teaching methodologies, emphasizing the need for AI approaches. The research objectives encompass a comprehensive exploration of the historical context, challenges, and existing technological interventions in English language education. A novel, technology-driven oral assessment method is designed, implemented, and rigorously evaluated using datasets such as Librispeech and L2Arctic. The ablation study investigates the impact of training dataset proportions and model learning rates on the method's performance. Results from the study highlight the importance of maintaining a balance in dataset proportions, selecting an optimal learning rate, and considering model depth in achieving optimal performance.
期刊介绍:
Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory.
The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation.
This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods.
Computational science typically unifies three distinct elements:
• Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous);
• Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems;
• Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).