This paper examines the role of artificial intelligence in advancing personalized learning and establishing a new educational paradigm. The primary purpose of the study is to explore how AI technologies support student-centered learning by adapting instructional content to individual needs, preferences, and learning paces. A qualitative secondary research methodology was employed, involving a systematic analysis of peer-reviewed journals, conference papers, and reputable reports published between 2023 and 2025. The study synthesizes existing literature on adaptive learning systems, intelligent tutoring systems, learning analytics, and large language models used in educational contexts. The findings indicate that AI-driven personalized learning significantly enhances student engagement, motivation, and academic performance across various educational levels, particularly in higher education. AI technologies enable real-time feedback, identification of learning gaps, and customized learning pathways, thereby improving instructional effectiveness and efficiency. However, the study also identifies key challenges, including data privacy concerns, algorithmic bias, unequal access to technology, and the need for adequate teacher training and ethical governance frameworks. In conclusion, the paper argues that AI-enabled personalized learning represents a transformative shift from traditional one-size-fits-all education toward adaptive and inclusive learning environments. While AI offers substantial benefits, its successful implementation requires responsible integration, human oversight, and continuous research to ensure equity, ethics, and long-term educational impact.

artificial intelligence, personalized learning, adaptive learning systems, intelligent tutoring systems, educational technology, student-centered education.

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