Factors Influencing Analysis for Level of Engineering English Education Based on Artificial Intelligence Technology

Abstract

The essence of English for engineering is English for professional purposes (ESP). The assessment of the level of education in engineering English classrooms is one of the key issues currently being discussed in all schools. While the traditional English classroom teaching model has been criticized due to many problems, the changes in the new English curriculum and the changes in the assessment methods have also created new demands on the English teaching methods. The artificial intelligence technology brings a new direction for the optimization of English classroom, and it also provides new support for the realization of intelligent and collaborative English classroom teaching. In view of the current situation that the college English teaching evaluation mode is still dominated by summative evaluation, this paper summarizes the current problems of poor teaching effect, unbalanced ability cultivation, and mismatch between evaluation and teaching in engineering English. On this basis, it adopts the BPNN network combined with the interactive mechanism of English teaching to establish a multi-dimensional interactive English learning framework of teacher, student, corpus, and AI resource base. And the BPNN algorithm process was optimized using the gray wolf algorithm to improve the engineering English teaching model. Finally, an experiment on teaching engineering English was conducted within a university, and the experimental results showed that teaching objectives, teaching contents, teaching methods, and teaching effects had important effects on teaching effectiveness. In addition, the teaching framework constructed using the improved BPNN algorithm was better in terms of the learning effect at the same time, especially in writing, during the teaching process. Finally, the experimental results show that BPNN optimized by gray wolf algorithm can achieve better teaching effect than BPNN.