Development of a Mobile AR-Based English Vocabulary Learning Platform

Abstract

The integration of mobile computing and sensing technologies has propelled technologyenhanced language learning into a new era characterized by contextualization and immersion. A key challenge is the effective fusion of augmented reality (AR) and artificial intelligence (AI) to create a learning platform that not only fosters deep vocabulary acquisition but also facilitates flexible knowledge transfer. This study introduces an advanced AR-based English vocabulary learning platform that leverages semantic tracking and multimodal information fusion. This platform transitions from passive labeling to active contextualized content creation, enhancing cross-modal personalized interactions through a novel service modeling and matching module. Additionally, this research proposes an event-matching-driven transfer mode recognition method. This method, which uses learning interactions modeled as temporal event sequences and integrates textual semantic features, allows for a detailed analysis of vocabulary knowledge transfer across three dimensions: consolidation, contextual generalization, and conceptual abstraction. Technologically, this approach marks a progression from simple environmental labeling to sophisticated contextual understanding and response, establishing a comprehensive loop of intelligent environmental perception and adaptive content creation. Methodologically, it develops a new framework for analyzing transfer effects, shifting from broad effect assessments to detailed analyses of cognitive mechanisms, and setting a new standard for the design and evaluation of intelligent language learning systems.