Teaching Reading Comprehension in the Age of Artificial Intelligence: Integrating Classical Reading Theories with Intelligent Educational Tools

Reading comprehension remains a cornerstone of academic success in EFL/ESL contexts, yet instructional and assessment practices often continue to emphasize outcome-based testing rather than the cognitive and metacognitive processes underlying effective reading. This conceptual paper reconceptualizes reading comprehension instruction in the age of artificial intelligence by integrating classical reading theories—such as schema theory, vocabulary depth, and strategic reading models—with emerging AI-driven educational tools. Drawing on contemporary research, the study argues that AI can function as a pedagogically informed intermediary across pre-reading, while-reading, and post-reading stages by providing personalized scaffolding, adaptive questioning, and formative feedback. The paper further highlights the potential of AI-supported analytics to shift assessment from product-oriented measures toward process-based and authentic evaluation of comprehension. Ethical considerations, including academic integrity, algorithmic bias, and data privacy, are examined as essential conditions for responsible AI integration. The study concludes that when grounded in evidence-based pedagogy, AI enhances reading instruction without diminishing the central role of the teacher, while simultaneously supporting differentiated learning and professional growth.