THE ROLE OF ADAPTIVE TECHNOLOGIES IN TEACHING ENGLISH SPEAKING SKILLS TO YOUNG LEARNERS

This research addresses the development of an embedded Edge AI framework designed to support adaptive English language learning for young students, focusing on real-time pronunciation evaluation and interactive feedback. To address the critical requirements of immediate responsiveness and strict user data privacy, the proposed methodology implements lightweight automatic speech recognition (ASR) and pronunciation assessment algorithms directly on resource-constrained edge hardware. The system dynamically adjusts task complexity based on real-time phonetic analysis and user performance metrics, facilitating personalized vocabulary and conversational practice at an optimal pace. By processing voice data locally on the device, the architecture mitigates privacy risks associated with cloud-based educational technologies. The research direction involves optimizing deep learning

models, such as quantized convolutional neural networks and lightweight transformer architectures, for deployment on embedded microprocessors. Performance evaluation focuses on latency, classification accuracy of phonetic errors, and hardware resource utilization efficiency. This framework provides a structured approach for implementing secure, adaptive educational tools that assist educators by offering localized, automated speech feedback while maintaining data integrity.

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