Transformer-based adaptive learning system for English cross-cultural communication: a web-enabled approach

Cross-cultural communication competence is essential for English learners in our globalized world, yet existing computer-assisted language learning systems often overlook cultural factors and lack personalized adaptation mechanisms. We propose TCALS (Transformer-based Cross-cultural Adaptive Learning System), a novel web-enabled intelligent tutoring system that integrates cultural-aware natural language understanding with adaptive learning strategies. Our system introduces three key innovations: (1) a Cultural-Aware Transformer architecture that extends BERT with cross-cultural attention mechanisms to dynamically model learners' native language backgrounds and capture L1-specific transfer patterns, (2) a multi-task learning framework that jointly optimizes proficiency assessment, error detection, and difficulty estimation for robust representation learning, and (3) an adaptive learning engine grounded in Item Response Theory that personalizes content recommendations by balancing difficulty optimality, error relevance, and cultural appropriateness. Comprehensive experiments on the Reddit English Learning Corpus containing over 100,000 learner posts from 150+ language backgrounds demonstrate that TCALS achieves 89.6% accuracy and 0.876 Macro F1-Score, representing substantial improvements of 4.4% and 0.045 over the strongest baseline. Our deployed web application achieves sub-200ms response latency through model quantization, demonstrating practical feasibility for real-time educational applications. This work bridges natural language processing, educational psychology, and cross-cultural communication to advance personalized language learning.