<![CDATA[The rapid advancement of digital technology has transformed language learning, particularly reading comprehension, with Artificial Intelligence (AI) emerging as a promising tool that enhances learning through adaptive support, instant feedback, and personalized instruction. This study investigates the roles and challenges of AI in reading comprehension learning through a literature review. The increasing use of AI-based platforms, such as ChatGPT, Grammarly, QuillBot, and ELSA Speak, highlights the need to evaluate their pedagogical contributions and potential risks. Guided by Sweller’s Cognitive Load Theory and Skinner’s Operant Conditioning framework, this study analyzes findings from national and international academic journals retrieved through Google Scholar. Data were examined using descriptive qualitative content analysis. The results identify four major roles of AI in reading comprehension learning: providing immediate feedback, facilitating personalized learning experiences, offering cognitive scaffolding for complex texts, and increasing student motivation and engagement. Nevertheless, five significant challenges emerge: learners’ tendency to avoid lengthy reading materials, excessive dependence on AI-generated answers, reduced opportunities for productive cognitive struggle, increased cognitive load when processing complex information, and a growing preference for quick-answer seeking behavior. These findings suggest that AI can serve as an effective instructional support tool when integrated thoughtfully into reading instruction. However, its implementation should be accompanied by active teacher guidance to prevent overreliance and to encourage critical thinking. The study advocates a hybrid instructional approach in which AI functions as a scaffold that complements, rather than replaces, learners’ cognitive processes, thereby promoting deeper and more sustainable reading comprehension development.]]>
