Personalized Learning Through Artificial Intelligence: A Literature Review

Abstract

Artificial intelligence (AI) offers pedagogical solutions to address the global learning crisis, but the literature specifically examines its application to learning personalization is limited. This study aims to examine how AI technology can personalize students' learning experiences and why AI needs to be mastered as an educational productivity tool, not avoided. The method used is a literature review with a search through Google Scholar using the combined keywords "Artificial Intelligence", "Personalized Learning", and "Literature Review", with publication inclusion criteria for 2021–2025. Of the 50 articles found, eight passed the inclusion and exclusion selection for analysis. The results of the review show that adaptive AI systems have been shown to improve learning outcomes by 14–30% compared to conventional methods through three main mechanisms: real-time content adaptation, automatic misconception detection, and targeted feedback. AI acts as a teacher's tool, not a substitute. The main implementation challenges include data privacy, algorithm bias, and teacher readiness gaps. It was concluded that AI is a productivity tool that must be gradually integrated with the support of teacher training, ethical regulations, and cross-stakeholder collaboration so that its benefits can be felt equally.