AI Enhanced Reading Comprehension through Personalized Learning Environments

Abstract

This study investigates the effects of an AI-powered adaptive learning system on university students' reading comprehension and grammatical performance in an English as a Foreign Language (EFL) setting. A total of 100 students took part in an eight-week intervention that used a platform to dynamically alter text difficulty and deliver tailored feedback based on their success. To assess learning results and offer information about learners' experiences, quantitative pre- and post-tests were used, as well as qualitative interviews. The results showed considerable improvements in both comprehension and grammar accuracy, as well as increased motivation, engagement, and self-regulated learning habits. Students with lower beginning proficiency showed the most significant gains. The system's real-time personalization and scaffolding show AI's ability to improve differentiated training, increase metacognitive growth, and maintain student engagement over time. Based on these findings, the paper advises adding AI-adaptive platforms to language curricula, introducing AI literacy to teacher training programs, and conducting longitudinal and cross-disciplinary studies in future research. Overall, the findings highlight AI's transformative potential in promoting inclusive, data-driven, and adaptive language teaching.