Design of an Interactive Intelligent Mobile System for College English Instruction Oriented toward Teaching Effectiveness Enhancement

Abstract

Traditional mobile learning systems for college English commonly suffer from high interaction latency, coarse-grained feedback, poor contextual adaptability, and insufficient utilization of on-device computing resources, which significantly constrain the learning experience and instructional effectiveness. To address these challenges, this study integrates mobile heterogeneous computing, lightweight on-device models, augmented reality (AR) visual interaction, and context-awareness technologies to construct an interactive intelligent mobile system aimed at improving teaching effectiveness in college English. The research focuses on optimizing mobile-side technical implementation by designing a heterogeneous computing scheduling engine tailored for mobile terminals to enable efficient collaborative execution of multiple on-device tasks. A lightweight on-device speech assessment and prosody correction algorithm is proposed to reduce reliance on cloud computing and improve feedback accuracy. In addition, a low-power, highly robust AR-based interactive corrective feedback mechanism is developed to address pronunciation organ movement correction in mobile learning scenarios. Furthermore, a context-aware collaborative filtering recommendation strategy is introduced to seamlessly adapt instructional content to fragmented mobile learning contexts. Multi-dimensional experimental results demonstrate that the proposed system effectively reduces interaction latency, optimizes mobile power consumption and runtime stability, and significantly enhances the effectiveness of college English speaking instruction. This study provides a novel solution for deep integration of technology and pedagogy in mobile education and offers technical references for related research and engineering applications.