Teaching Strategies for English Audio-Visual Oral Instruction Based on Deep Neural Networks

With the rapid evolution of educational technology, leveraging advanced methodologies for English instruction has become increasingly critical to addressing the growing demand for efficient and engaging language learning. Current approaches in English language teaching often fall short in personalization, adaptability, and learner engagement, primarily due to their static structure and limited integration of cognitive and technological advancements. To bridge this gap, we propose a novel framework grounded in deep neural networks to enhance English Audio Visual Oral (AVO) instruction, aligning with the thematic scope of computational advancements in education. This study introduces the Adaptive Cognitive Learning Model (ACLM), a pedagogical innovation designed to dynamically adjust teaching strategies to individual learner profiles by integrating real-time performance feedback, modular content delivery, and multimedia-assisted learning. The ACLM employs a systematic feedback loop and adaptive mechanisms to personalize learning pathways, addressing domain-specific challenges such as vocabulary acquisition, grammar comprehension, and conversational fluency. Experimental evaluations demonstrate that our method significantly improves learner outcomes in engagement, comprehension, and retention compared to traditional approaches. These findings underscore the potential of combining cognitive alignment with dynamic neural networks to establish scalable, personalized, and effective instructional strategies in English AVO education, contributing to advancements in computational language pedagogy.