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

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.