ORIGINAL RESEARCH article
Front. Educ.
Sec. Digital Education
Volume 10 - 2025 | doi: 10.3389/feduc.2025.1591393
Deep Learning Based AI-Driven Teaching Models in Chinese High School English Class: A Case Study of Reading Lessons
Provisionally accepted
Yuxin Liu
Chengche Qiao*- Beijing Jiaotong University, Beijing, China
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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.
Keywords: artificial intelligence, deep learning, high school English class, reading session, Teaching model
Received: 11 Mar 2025; Accepted: 13 May 2025.
Copyright: © 2025 Liu and Qiao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Chengche Qiao, Beijing Jiaotong University, Beijing, China
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