Teaching mode of oral English in the age of artificial intelligence.

TitleTeaching mode of oral English in the age of artificial intelligence.
Publication TypeJournal Article
Year of Publication2022
AuthorsLi Y
JournalFront Psychol
Volume13
Pagination953482
Date Published2022
ISSN1664-1078
Abstract

With the deepening of cultural integration, people's demand for English learning is also increasing rapidly. However, traditional teaching methods have certain limitations, and teaching conditions are limited by the slow development of information technology, oral English courses have been shelved and stopped for a long time. With the rapid development of technology, the era of artificial intelligence has arrived. Learning assistance systems based on artificial intelligence have emerged in an endless stream, which has also innovatively solved the problem of oral language learning. Natural language processing is a computing mode of deep learning by artificial intelligence, which can carry out deep learning and training according to the current goal and finally get the desired result. But relying only on the auxiliary learning system cannot fundamentally solve the problem of oral language learning. Therefore, we aim to update the current spoken English learning methods using natural language processing technology, and propose a natural language processing-based oral English teaching model. In this mode, natural language processing can match different teaching methods according to the spoken language characteristics of different students, and give constructive suggestions. Moreover, the spoken English teaching mode based on natural language processing can be continuously upgraded and adjusted to adapt to the changing and developing era in time. Experiments show that the oral English teaching mode based on natural language processing can improve students' comprehensive ability of oral English. And it increased its comprehension by 19.7% year-on-year, and at the same time it also improved the enthusiasm for learning oral language by 33.3%.

DOI10.3389/fpsyg.2022.953482
Alternate JournalFront Psychol
PubMed ID35936279
PubMed Central IDPMC9355419