Classroom Practice of Computer Intelligent English Translation Teaching Based on Embedded System

Computer intelligent teaching has become an important approach for improving English translation education through artificial intelligence and embedded computing technologies. This study investigates the classroom practice of computer intelligent English translation teaching based on an embedded system and develops an adaptive teaching framework integrating embedded hardware, multimodal perception, and a Self-Organizing Feature Map (SOFM) neural network. The embedded platform acquires real-time visual and behavioral information to evaluate student engagement and provides immediate instructional feedback through local processing with low latency. Experimental evaluation involving 300 students from three universities demonstrates that the proposed approach significantly enhances translation performance, increasing the mean translation accuracy from 50.3% to 83.8% after one semester of instruction, with an average improvement of 33.5 percentage points. The embedded system maintains an average feedback latency below 0.55 s while sustaining real-time multimodal data processing, confirming its practical deployment capability. By integrating embedded intelligent sensing, multimodal signal acquisition, and efficient information transmission into classroom teaching, the proposed framework provides an engineering-oriented solution for adaptive educational systems and offers potential references for electromagnetic sensing-assisted edge devices, wireless information interaction, and intelligent signal processing in next-generation digital learning environments.