The rapid integration of artificial intelligence (AI) into higher education has transformed the ways in which learners access information, engage with content, and receive feedback. Among these advancements, AI-driven personalization systems—such as adaptive learning platforms, predictive analytics, and intelligent tutoring systems—have demonstrated significant potential for improving learning outcomes. This thesis examines the extent to which AI-driven personalization enhances student performance, motivation, and retention in higher education environments. Drawing on contemporary theoretical frameworks and recent empirical findings, the study explores how these technologies tailor educational pathways, identify learner difficulties, and offer timely interventions. It also discusses challenges associated with algorithmic transparency, student data privacy, and the digital divide. The analysis highlights that while AI personalization offers substantial pedagogical advantages, its effectiveness depends on thoughtful implementation, ethical governance, and instructor readiness. Ultimately, this thesis argues that AI-driven personalization can significantly improve learning outcomes when embedded within a holistic, human-centered educational ecosystem that preserves equity, autonomy, and academic inte