Theoretical Framework, Technical Paths and Practical Verification of AI-empowered Dubbing Education

Abstract

As a vital domain of vocal art and linguistic expression, dubbing education faces persistent challenges in traditional instruction, including a shortage of qualified teachers, subjective evaluation, and insufficient personalization. From the perspective of AI+education, this study systematically reviews the latest national policies from 2025 to 2026 and constructs a three-tier technical framework consisting of the infrastructure layer, algorithm engine layer, and application scenario layer. Five core technical pathways are elaborated in detail: intelligent speech assessment, affective computing, personalized recommendation, virtual teacher synthesis, and data-driven governance. On this basis, a “human-machine collaborative” dual-teacher classroom model and an intelligent dubbing training platform are designed. The effectiveness of AI-assisted instruction is empirically verified through a quasi-experimental study. Results show that AI-enabled dubbing education reduces feedback latency from days to seconds, increases emotional expression matching by over 26%, and lowers teachers’ grading workload by 62%. This paper provides both theoretical reference and practical guidance for the digital transformation of vocal art education.