Artificial Intelligence in Education: Impact on Students’ Critical Thinking, Logical Reasoning, and Cognitive Development: A Comprehensive Review

Abstract

Educational institutions have adopted Artificial Intelligence (AI) tools at a pace that has outstripped careful study of their cognitive consequences for students. This paper synthesizes findings from 25 recent journal articles and empirical studies to examine how AI integration in school, undergraduate, and graduate settings shapes critical thinking, logical reasoning, and broader cognitive development. Generative large language models, intelligent tutoring systems, and adaptive learning platforms clearly strengthen personalized instruction and raise measurable academic performance; the evidence reviewed here, however, points to a separate risk: when such tools are adopted without safeguards, they can gradually erode independent reasoning, deductive problem-solving, and metacognitive self-regulation. One especially striking pattern emerges from the empirical literature: students who rely heavily on AI support tend to earn higher grades yet perform worse on assessments that require unassisted thinking, a divergence they term the grade-competence gap. To address this tension, the paper proposes the CLEAR Framework: contextual use, literacy, engagement, assessment reform, and reflection, a five-component approach for capturing AI's instructional value without undermining students' cognitive growth. The paper closes with practical recommendations for educators, curriculum designers, and policymakers navigating AI's growing role in education.