Artificial Intelligence as a Tool for Personalized Learning in Higher Education

Abstract

Artificial Intelligence (AI) has become one of the most important technologies that will change the future of higher education. As students become more diverse, higher education becomes more accessible to more people, and digital learning environments grow quickly, traditional teaching methods typically can't match the demands of each student. Personalized learning, which focuses on changing the content, speed, and teaching methods to fit each learner, has become more important since AI technologies were included. This research investigates Artificial Intelligence as an instrument for individualized learning in higher education, focusing on its uses, advantages, problems, and prospective developments. The examination centers on AI-driven solutions, including intelligent tutoring systems, adaptive learning platforms, learning analytics, and conversational agents, which provide data-driven customisation of educational experiences. The results show that AI improves personalized learning by making it possible to present content in a way that adapts to each learner, give feedback in real time, use predictive analytics, and keep learners interested. When done right, AI-based individualized learning can help students do better in school, stay motivated, and stay in school longer, according to research. Nonetheless, the incorporation of AI engenders considerable apprehensions over data privacy, algorithmic bias, ethical governance, and inequitable access to modern technologies. The research posits that AI ought to be seen as a supplementary educational instrument rather than a substitute for teachers. The report asserts that the effective implementation of AI for individualized learning necessitates institutional preparedness, ethical guidelines, faculty enhancement, and inclusive policy formulation. This study enhances the expanding educational technology literature by delivering a thorough synthesis of AI-driven individualized learning and presenting implications for practice, policy, and subsequent research.