{"id":9265,"date":"2026-07-21T20:55:26","date_gmt":"2026-07-21T12:55:26","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=9265"},"modified":"2026-07-21T21:09:53","modified_gmt":"2026-07-21T13:09:53","slug":"jase-202610-33-049","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202610-33-049","title":{"rendered":"AI-Powered English Teaching Model Integrating Multimodal Corpora and Deep Learning in the Context of Digitalization"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 33<\/a><\/p>\n<\/div>\n\n\n\n

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Wang Yingxue1<\/sup>, Yin Jianjian2<\/sup>, and Guo Bisen1<\/sup><\/i><\/a><\/p>\n\n\n\n

1<\/sup>Weifang University, Weifang, Shandong, 261061, China<\/p>\n\n\n\n

2<\/sup>Shandong Vocational College of Science and Technology, Weifang Shandong, 261053, China<\/p>\n<\/div>\n\n\n\n

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Received: April 16, 2026
Accepted: June 7, 2026
Publication Date: July 21, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Test Scores <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202610_33.049<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Traditional English teaching often lacks integration between intelligent analysis and multimodal teaching principles in digital learning environments. This study proposes an end to-end AI-powered English teaching model that combines multimodal corpora with deep learningtechnologies. A synchronized multimodal teaching corpus was developed to integrate audio, visual, textual, and behavioral data. The model uses a hierarchical deep learning architecture with dedicated encoders and a cross-modal attention fusion mechanism to generate unified representations. A multi-task collaborative learning framework performs four key diagnostic functions simultaneously: pronunciation assessment, grammatical error detection, learning engagement classification, and knowledge tracking. Based on these diagnostic outcomes, a reinforcement learning agent using the proximal
policy optimization algorithm provides personalized learning recommendations. Experimental results show that the proposed model achieves superior performance across multiple tasks and significantly improves learning outcomes, including a 26.2% reduction in learning time for the experimental group. The study offers an effective approach for developing adaptive, precise, and intelligent digital English teaching systems.<\/p>\n\n\n\n

Keywords: AI-Powered English Teaching; Multimodal Corpus; Cross-Modal Fusion; Personalized Recommendation<\/em><\/p>\n\n\n\n

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