User Intent Understanding and Service Classification in English Tutoring Systems via Large Language Models Over Wireless Communication Networks

Abstract

This paper proposes a novel hybrid framework that combines lightweight edge-side intent sketching with cloud-based large language model (LLM) reasoning, called Wireless LLM-Enhanced Intent-Service Parsing Framework (WISE). Specifically, WISE integrates four components: Local Intent Sketching Module (LISM), Semantic Feature Compression and Transmission (SFCT) unit, Prompt-Aware LLM Service Classification Engine (LSCE), and Semantic Alignment and Service Prediction module (SASP). This architecture enables efficient semantic understanding with minimal transmission overhead. Experimental results on a curated English tutoring intent-service dataset demonstrate that WISE achieves superior accuracy (88.9% intent classification accuracy and 86.5% F1 score), while reducing communication costs by over 80% compared to cloud-only LLM solutions. Additional ablation studies and training analyses confirm the effectiveness and stability of the proposed design. WISE offers a scalable and real-time solution for intelligent language tutoring in wireless edge environments.