Integration of Deep Learning in English Reading Instruction in the Era of Digital Transformation

This study employed a quantitative approach with a quasi-experimental nonequivalent control group design to examine the effectiveness of deep learning technology integration in junior high school English reading instruction during the digital transformation era. Data were collected through pre-tests and post-tests involving 60 students divided into an experimental group using deep learning and a control group using conventional methods. Independent t-test and N-Gain analysis revealed a significant improvement in reading comprehension in the experimental group compared to the control group. These findings indicate that deep learning not only enhances learning outcomes but also fosters pedagogical transformation toward adaptive, data-driven learning, offering theoretical contributions to AI-based instructional models and practical implications for teachers in designing relevant learning strategies.