计算机科学
生成语法
转化式学习
背景(考古学)
人工智能
多媒体
心理学
古生物学
教育学
生物
作者
Wanjin Dong,Daohua Pan,Soonbae Kim
标识
DOI:10.1016/j.jocs.2024.102397
摘要
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.
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