Research on the Application of Deep Learning and Embedded Systems in College English Education

This work aims to explore the use of an embedded system that incorporates deep learning for college English teaching to solve problems like low feedback, low interest, and less effectiveness of conventional approaches. In this study, a CNN-based embedded system that combines text and speech for adaptive learning is assessed over the course of a semester using test and control groups. It shows enhanced interaction and real-time feedback. The experiment showed that the CNN achieved the test accuracy of 94.3%, while the traditional model achieved only 80.2%. The test group demonstrated substantial improvements in academic performance across all categories: These skills include; listening, reading, writing, and speaking. The engagement scores rose by 22.3%, while the system efficiency grew by 15.6%, which is evidence of the system’s capacity to maintain students’ motivation and enhance the teaching and learning processes. Mentioned studies show that deep learning and embedded systems have the promise of transforming language learning. Therefore, we give a system that provides tailored, effective, and adaptive solutions for the learners. In their needs through the use of artificial intelligence in the educational setting.