Kexin He,Xiaoya Feng

Anchored in the context of digital transformation in education and the deep integration of artificial intelligence (AI), this study explores the innovative application of deep learning technologies in primary school English teaching, aiming to construct a smart educational ecosystem characterized by “technology empowerment–instructional reconstruction–competency enhancement.” The research first analyzes the compatibility of AI technologies—including intelligent speech recognition, natural language processing, and image recognition—with primary English instruction, and explains their facilitative mechanisms in students’ language acquisition (such as pronunciation accuracy, grammar internalization, and contextual expression) through the lens of cognitive science. By building a closed-loop teaching model of “dynamic assessment–personalized delivery–real-time intervention,” the study empirically examines the positive effects of AI applications on classroom interaction efficiency (an increase of 37%) and students’ autonomous learning ability (an increase of 29%). The study also identifies practical challenges such as data privacy concerns, lack of emotional interaction, and urban-rural equipment disparity. In response, it proposes a three-dimensional coordinated strategy involving “technology optimization–teacher role transformation–policy support.” Future research will focus on multimodal learning analytics, generative AI-driven content creation, and promoting educational equity across regions, thereby offering both theoretical reference and practical pathways for the deep integration of AI and subject-based teaching.

Generative AI; Primary School English Teaching; Multimodal Integration; Intelligent Teaching; Educational Ecosystem Reconstruction

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