Authors: Dr. Shashi Kumar
Abstract: The integration of Artificial Intelligence (AI) into education has transformed pedagogical practices, enabling personalized learning, adaptive assessments, and data-driven insights. This paper presents a comparative study of AI-based teaching methodologies versus traditional approaches, highlighting their respective strengths, limitations, and applicability. While conventional teaching emphasizes teacher-centered instruction, face-to-face interaction, and standardized curricula, AI-based methods promote individualized learning pathways, real-time feedback, and interactive engagement. The study draws on theoretical perspectives and recent empirical evidence to assess how AI technologies, such as intelligent tutoring systems, machine learning algorithms, and virtual assistants, reshape the teaching-learning process. Findings suggest that AI enhances flexibility, efficiency, and inclusivity but raises challenges related to ethics, accessibility, and teacher-student relationships. Traditional approaches, on the other hand, remain vital for cultivating social, emotional, and critical thinking skills through human interaction. The research underscores the importance of a hybrid framework that integrates the personalization and scalability of AI with the empathy and contextual understanding of human educators. This balanced model is argued to be the most effective in addressing diverse learner needs, fostering holistic development, and preparing students for future challenges. The comparative analysis concludes that AI should be positioned as a complement, not a replacement, for traditional teaching.
DOI: http://doi.org/10.5281/zenodo.17837070
