AI-Enhanced Reading Comprehension: Improving Critical Thinking Skills in English Language Learners

Artificial Intelligence (AI) is revolutionizing English Language Learning (ELL) by addressing challenges in traditional methods, which often lack personalized feedback, context-aware assessments, and support for critical thinking skills like inference, analysis, and evaluation. These limitations restrict learners' ability to achieve deeper comprehension and higher-order cognitive skills. This study introduces an AI-enhanced framework using BERT (Bidirectional Encoder Representations from Transformers), a cutting-edge deep learning model in natural language processing, to overcome these barriers and improve ELL students' reading comprehension and critical thinking abilities. The proposed framework preprocesses datasets with advanced tokenization, normalization, and contextual text analysis to provide high-quality inputs. Leveraging BERT's bidirectional architecture, the system captures nuanced word relationships and contextual meanings, offering personalized assessments and adaptive feedback tailored to individual learning gaps. The interactive learning environment fosters critical cognitive skills and enables students to constructively analyze and evaluate texts. Experimental results demonstrate the model's effectiveness, achieving a 98.1 % accuracy and a low loss rate of 0.2 %, outperforming traditional methods and models like GRU and CBRNN. These findings highlight the potential of BERT to enhance ELL by promoting comprehension and critical thinking. The study presents a scalable and innovative solution, advancing AI's role in modern education and language acquisition.