Artificial Intelligence in Critical Reading Learning of Junior High School: A Systematic Literature Review

Abstract

This article reports on a systematic review of literature on the application of Artificial Intelligence (AI) in improving critical reading skills among junior high school students. It includes an analysis of specific aspects such as the types of AI tools employed, the learning outcomes targeted, and the challenges in implementation. This study aims to systematically review how AI technologies such as intelligent tutoring systems, natural language processing, and adaptive learning platforms have been integrated into reading instruction to enhance students’ critical thinking and text analysis skills. The review analyzed peer-reviewed journal articles published between 2015 and 2023, focusing on studies conducted in undergraduate and graduate-level educational contexts.

To achieve this, a Systematic Literature Review (SLR) method was employed, resulting in a final selection of 20 studies. The analysis revealed that most research on AI in critical reading learning is situated in the fields of computer science, engineering, and language education. The findings indicate that AI-based approaches offer personalized learning experiences, immediate feedback, and interactive engagement, all of which contribute positively to the development of students' critical reading abilities. However, challenges such as limited access to technology, insufficient teacher training, and potential over-reliance on automated feedback remain significant barriers. This review concludes by discussing the implications for educators and policymakers and provides recommendations for future research and practice in AI-driven critical reading instruction