AI-Based Adaptive Learning Systems and Their Effect on English Reading Comprehension among College Learners

Artificial Intelligence-Based Adaptive Learning Systems and Their Effect on English Reading Comprehension among College Learners Dr. Balasaheb Ishwar Wakde MIT School of Computing, MIT ADT University, Loni-Kalbor, Pune Abstract Artificial Intelligence (AI)-based adaptive learning systems are transforming language education by offering personalized and data-driven instructional experiences. This study examines the effect of AI-enabled adaptive learning platforms on English reading comprehension among college learners. These systems utilize machine learning algorithms to analyze learner performance, identify individual strengths and weaknesses, and deliver customized reading materials and feedback in real time. The research adopts a quantitative approach using structured assessments to evaluate improvements in comprehension skills, including vocabulary acquisition, inference, and critical analysis. Findings indicate that learners exposed to adaptive learning environments demonstrate higher engagement, improved reading accuracy, and better retention compared to traditional methods. The study also highlights the role of immediate feedback and content personalization in enhancing learning outcomes. AI-based adaptive systems significantly contribute to improving English reading comprehension, offering scalable and effective solutions for modern higher education environments.