AI-Native Classroom: Data-Driven Innovation in "Triad-Teacher" English Instruction
Abstract
This study presents AI-Native Classroom V5.0, grounded in Vygotsky's Zone of Proximal Development (ZPD) and Tao Xingzhi's "Little
Teacher" pedagogy. The system utilizes Firebase real-time database to construct a comprehensive pre-class, in-class, and post-class data
ecosystem. Key innovations include: (1) Multimodal ZPD diagnosis integrating Web Speech API with semantic similarity analysis for fourdimensional assessment (acoustic 40%, content 40%, fluency 20%); (2) "Triad-Teacher" model (Lead Teacher + AI Agent + Peer Leader) with
dual scaffolding mechanism; (3) Gamified "Turtle Soup" collaborative learning and PechaKucha 200 information integration strategies. The
system enables dynamic group formation, real-time SOS monitoring, and multi-dimensional evaluation (teacher, peer, self), demonstrating
effective precision teaching for 60-student cohorts while cultivating critical AI literacy. This study contributes to the theoretical framework of
AI-native pedagogy and provides practical implications for digital transformation in language education.
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DOI: http://dx.doi.org/10.70711/neet.v4i7.9778
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