Design and implementation of a vocational education English adaptive teaching system based on deep learning and optical technologies

The growing demand for oral English proficiency in vocational education necessitates innovative teaching methods tailored to individual learners' needs. This study proposes an adaptive English-speaking teaching system based on deep learning and optical technologies to address these challenges. By integrating a hybrid Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architecture, the system performs accurate speech recognition and grammar analysis, extracting critical acoustic and linguistic features. Optical Character Recognition (OCR) enhances text input efficiency, eye-tracking technology enables precise analysis of learners' attention and behavior, and Augmented Reality (AR) improves interactive learning experiences. Reinforcement learning is employed to dynamically adjust exercise difficulty and content based on learners' performance, offering a personalized and adaptive learning experience. The system was evaluated using a diverse dataset comprising speech samples and annotated textual corpora. Experimental results demonstrated significant improvements in speech recognition accuracy (92.8%) and grammar analysis accuracy, outperforming traditional methods. Additionally, the adaptive mechanism enhanced learning efficiency by 21.5%, emphasizing its effectiveness in addressing individual learning needs. This study highlights the integration of deep learning and optical technologies as a transformative approach for smart education, contributing to intelligent, scalable, and efficient solutions for vocational training and language learning.