A Practical Path of Multi-AI Agents Collaboration Based on Low-Code Coze Platform to Empower Students’ English Learning Efficiency

Abstract

This study proposes a multi-agent collaboration system based on the low-code Coze platform to enhance students’ English third classroom practices. Addressing the limitations of traditional extracurricular learning—including fragmented scenarios, insufficient personalized guidance, and a lack of virtual-real coordination—we leverage Coze’s visual workflow engine to integrate training in listening, speaking, reading, and writing. By incorporating technologies such as speech recognition, multimodal generation, and dynamic knowledge graphs, the system provides adaptive learning support. Case studies across four language skill scenarios demonstrate its effectiveness in improving training coherence, feedback immediacy, and cross-scenario adaptability. This potential platform boosts teacher-student engagement, offering a replicable technical solution for educational digital transformation.