The rapid advancement of artificial intelligence (AI) technologies has significantly transformed various sectors, including education. This paper explores the theoretical foundations and conceptual approaches underlying the integration of AI into educational systems. Drawing upon established learning theories such as behaviorism, cognitivism, constructivism, and connectivism, the study examines how AI-driven tools align with and enhance pedagogical practices. Additionally, the paper analyzes key conceptual approaches, including personalized learning, adaptive systems, data-driven decision-making, and human-AI collaboration. The research highlights the potential of AI to improve learning outcomes, optimize teaching processes, and increase accessibility in education. At the same time, it critically addresses challenges such as ethical concerns, data privacy, and technological inequality. The study concludes that the successful implementation of AI in education requires a balanced integration of theoretical insights and practical frameworks, ensuring that technological innovation supports human-centered learning.
Mastura KHUJANOVA (Mon,) studied this question.