Pillars of Artificial Intelligence for Enhancing Reading Comprehension in Formal Education: A Scoping Review (#324)

Authors

Chung Sanchez, Kenji Alberto

Umán Juarez, Steve Jason

Alcántara Cuba De Byrne, Giovanna Irayda

Hurtado Laura De Mera, Carmen Rosa

Byrne Jaramillo, Luis Alberto

Abstract

Although Artificial Intelligence (AI) has shown a significant impact in various educational fields, its specific application in enhancing reading comprehension remains limited and fragmented. Most existing studies focus on isolated tools rather than offering an integrated perspective on how AI technologies can support reading processes across formal education levels. To address this gap, the present study aimed to identify and systematize the fundamental AI pillars with potential application in reading comprehension. A scoping review was conducted following the Arksey and O’Malley framework, comprising five stages. A total of 696 documents were retrieved from Scopus and Web of Science, from which 8 empirical studies were ultimately selected based on their practical evidence of AI use in literacy-related educational contexts. Using co-occurrence analysis through VOSviewer and qualitative synthesis, four key pillars were identified: (1) generative models for text simplification, (2) automatic question generation and narrative datasets, (3) personalized adaptive learning platforms, and (4) AI-supported moderation in online discussion environments. These pillars reflect a shift from instrumental to cognitively supportive uses of AI in educational settings. The findings offer both theoretical insights and practical guidance for educators, policymakers, and researchers interested in the ethical, inclusive, and meaningful integration of AI to foster reading comprehension.