Enhancing Student Engagement and Academic Performance through AI-Based Tutoring Systems: A Comparative Study of Traditional vs. AI-Driven Education Models

Abstract

The introduction of Artificial Intelligence (AI) in educational sector has shifted the paradigm in facilitating, testing and customizing of education to people. The suggested paper is a theoretical discussion of how AI-based tutoring tools would enable student interaction and results to a higher standard compared to the conventional system of education. It has already formed a theory of quantitative foundations formulated through constructivist learning theory, cognitive load theory (CLT), self-determination theory (SDT) and theoretical idea of technological determinism. In the paper, conceptual measures are used such as Student Engagement Index (SEI), Adaptive Learning Efficiency (ALE), Motivation Alignment Coefficient (MAC), and Academic Performance Potential (APP) in order to explain that adaptivity offered by AI is linked to a better level of cognitive activity and higher performance. Using the analysis, it is possible to observe that AI tutoring systems are not only instructional assistance, but learners are dynamic learning partners, which is grounded on the premise of real time data, cognitive load relief and long-term motivation. The comparison of the AI-oriented with the classical model reveals that AI offers freedom, responsiveness, and adaptability based on learners which is not present in the traditional systems. The findings indicate that AI tutoring is a new model of education, a new learning ecosystem, which combines data analytics with humanistic pedagogy balancing personalization and equity. The paper does state that future of education will be founded on justifiable correspondence between precision of AI and the relational warmth of human instructing to create adaptive inclusive and ethically alert learning environments.