AI Collaborative Framework for Gamified English Language Learning with Real-Time Encouraging Feedback

Abstract

Recent advances in transformer-based Natural Language Processing (NLP), speech intelligence, and adaptive machine learning have enabled the development of intelligent, real-time educational systems. This paper presents a scalable multimodal artificial intelligence (AI) framework for adaptive language learning that integrates text and speech processing within a unified architecture. Existing digital language learning platforms remain limited by delayed feedback, lack of multimodal interaction, and insufficient personalization. To overcome these challenges, the proposed framework combines transformer-based models (BERT and T5) for contextual text understanding and generation, DeepSpeech for automatic speech recognition, and predictive models, including XGBoost and Long Short-Term Memory (LSTM), for dynamic learner profiling and adaptive feedback generation. The system employs a unified multimodal pipeline to process textual and speech inputs, enabling low-latency, context-aware feedback on grammar, pronunciation, fluency, and vocabulary. A gamified adaptive learning mechanism dynamically adjusts task difficulty and reward progression based on real-time learner performance. Experimental evaluation on 120 tertiary-level learners over 16 weeks demonstrates significant improvements, including 38% in writing accuracy, 42% in speaking fluency, and 35% in engagement. Performance is validated using BLEU score, Word Error Rate (WER), F1-score, and latency metrics, confirming robustness and scalability. The proposed framework advances intelligent tutoring systems through integrated multimodal processing, real-time adaptability, and scalable AI-driven personalization.