Edge-Cloud Collaborative Speech-Driven Adaptive Learning System with Emotion-Cognitive Integration for Personalized English Education

This study establishes an edge-cloud adaptive learning system that synergizes real-time speech processing with emotion-aware pedagogy. Deployable on 4GB RAM devices, the architecture reduces feedback latency by 57% (186ms median) while improving pronunciation accuracy by 28.7% (p<0.001) compared to conventional systems. Through a 12-week trial with 60 EFL learners, we demonstrate that affective-cognitive integration accounts for 42% of learning outcome variance (R2=0.42), validating the necessity of emotion-aware designs. The open-source implementation enables scalable deployment in resource-constrained educational settings.