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Deep learning based AI-driven teaching models in Chinese high school English class

A case study of reading lessons

Dados Bibliográficos

ID22162052
AutoresYuxin Liu (0000-0001-9753-2617), Yu‐xin Liu (0000-0002-5815-8244, Beijing Jiaotong University), Chengche Qiao (0000-0002-0531-9585, Beijing Jiaotong University, autor correspondente)
Ano2025
Volume10
Data de publicação2025-05-30
Open AccessSim
TipoARTICLE
PeriódicoFrontiers in Education (JOURNAL)
Identificadores do periódicoISSN: 2504-284X • E-ISSN: 2504-284X
EditoraFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/feduc.2025.1591393
OpenAlexW4410905621
IdiomaEN
Citações recebidas1
Referências citadas26

The traditional English teaching model placed more emphasis on the rote memorization of knowledge, with less focus on encouraging deeper cognitive engagement, which may lead to more fragmented understanding among students. Nowadays, as intelligent technology evolves and educational objectives shift, traditional teaching models face new challenges, suggesting the need for innovative adaptation in high school education. Based on the theory of deep learning, this paper explores an AI-driven instruction model for high school English reading from five dimensions, that is, theoretical foundation, implementation conditions, teaching objectives, operational procedures, and teaching assessment, in an attempt to support deep thinking and core competencies of students, thus providing a possible direction for the sustainable development of high school English education. The practice of AI-driven education based on high school English indicates that the proposed instruction model not only optimizes traditional teaching models but also enhances the deep learning capabilities and higher-order thinking skills of students

Deep learning · Linguistics · Mathematics education · AI and Big Data Applications · Computer Science · Educational Technology and Pedagogy · Online Learning and Analytics · Psychology · Artificial Intelligence

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Citações por ano1
Intervalo de citações2026 - 2026 (1)
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