Personalized Path to Language Education Supported by Artificial Intelligence

This study aims to build a personalized language learning path optimization model based on artificial intelligence technology to address core issues in traditional language education, such as the difficulty of standardized teaching models in meeting the diverse needs of learners, uneven learning outcomes due to individual differences, and the lack of a dynamic feedback mechanism. By collecting multi-dimensional learning data (including voice interaction frequency, vocabulary acquisition trajectory, grammatical error distribution and cognitive behavioral characteristics), a hybrid clustering algorithm is used to accurately construct learner portraits, combined with the LSTM-GRU dual-channel neural network to dynamically analyze learning behavior patterns, and an adaptive recommendation engine based on reinforcement learning is designed. Finally, a personalized learning path dynamic generation system is constructed. Experimental data show that in a 12-week empirical study, the experimental group (N=320) improves to 90.9 points in the comprehensive language ability test compared with the control group (N=315), the learning task completion rate increases to 98.5%, the grammatical structure error rate decreases to 3.2%, and the continuous learning time of high-anxiety learners is extended to 9.7 hours. Research shows that this AI-supported model can effectively solve the homogeneity dilemma of traditional education, significantly improve language learning efficiency through data-driven dynamic path optimization, and provide a replicable technical framework and empirical basis for the field of intelligent education. © 2025 Copyright held by the owner/author(s).