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Moreover, cloud\u2010based AI solutions pose risks to student data privacy and suffer from high latency. TinyML, a lightweight machine learning paradigm, is inherently compatible with edge intelligence due to its ability to run efficiently on resource\u2010constrained edge devices and perform local inference, thereby reducing latency and safeguarding data privacy. This paper presents an edge\u2010intelligently assisted English classroom quality evaluation scheme based on the TinyML model, which utilizes edge computing devices to analyze classroom interaction data in real time, improving the objectivity and timeliness of teaching quality assessment. The scheme employs lightweight deep learning models for various analyses and conducts localized data processing to avoid cloud\u2010related issues. Experimental results demonstrate its superiority over traditional cloud AI schemes in accuracy, real\u2010time performance, and resource utilization, offering a viable approach for intelligent education evaluation.<\/jats:p>","DOI":"10.1002\/itl2.70072","type":"journal-article","created":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T09:09:40Z","timestamp":1751879380000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["<scp>TinyML<\/scp> Based Edge Intelligent English Classroom Quality Assessment Scheme"],"prefix":"10.1002","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4785-8215","authenticated-orcid":false,"given":"Shuang","family":"Jiang","sequence":"first","affiliation":[{"name":"Jilin Animation Institute  Jilin China"}]}],"member":"311","published-online":{"date-parts":[[2025,7,7]]},"reference":[{"issue":"3647","key":"e_1_2_8_2_1","first-page":"208","article-title":"An Investigation and Analysis of Classroom Evaluation in College Oral English Teaching","volume":"142","author":"Long T. 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