Artificial Intelligence Driven English Translation: Utilizing Machine Learning and IoT Data to Improve Context Accuracy

The traditional English teaching mode ignores the differences between individual students, resulting in some students' low motivation to learn, while personalized English learning based on the support of Internet of Things can effectively improve students' learning efficiency.In this paper, we construct the evaluation index system of student engagement from four dimensions: learning attitude, learning process, learning effectiveness and emotional experience, and quantify the degree of student engagement in IoT-supported personalized English learning through a fuzzy mathematical model.In order to further improve student engagement, a student engagement improvement mechanism is designed based on four evaluation results: excellent, good, moderate and poor.The study takes the freshman (1) class of English majors in School A, which uses IoT technology for personalized English learning, as the research object and uses a fuzzy mathematical model to quantify student engagement.The evaluation grade of student engagement in this class is "medium", and the evaluation score is 71.37.Accordingly, a student engagement improvement mechanism was introduced.After 8 weeks of rectification, the final grade of student engagement in English learning is "excellent", with an evaluation score of 88.49, which is a total improvement of 17.12 points, and the student engagement enhancement mechanism designed in this paper has a significant effect.