https://matjournals.net/engineering/index.php/JoDEKD/issue/feed 2026-08-26T05:17:22+00:00 Open Journal Systems <p><strong>JoDEKD</strong> is a peer reviewed journal of Computer Science domain published by MAT Journals Pvt. Ltd. It is a print and e-journal focused towards the rapid publication of fundamental research papers on all areas of Data Engineering and Knowledge Discovery. This journal focuses on Data Architecture, Data Integration and Data Exchange, Data Mining, Knowledge Acquisition, Representation, Dissemination, Codification, Discovery Techniques, and their Technologies. JoDEKD also covers the areas of Knowledge Representation Techniques, Knowledge Retrieval, Text Mining, Intelligent System Design, Data Integration and Exchange, Data security and Data Integrity, Algorithms for Data Mining, Conceptual Data Models and Knowledge Visualization; Interactive Data Exploration and Discovery.</p> https://matjournals.net/engineering/index.php/JoDEKD/article/view/4043 2026-08-26T05:17:22+00:00 Ashok Sadavare dattatrayakumbhar4@gmail.com Dattatraya Kumbhar dattatrayakumbhar4@gmail.com Aditya Sadavare dattatrayakumbhar4@gmail.com

<p><em>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.</em></p>

2026-08-26T00:00:00+00:00 Copyright (c) 2026 Journal of Data Engineering and Knowledge Discovery