The role of large language models in personalized learning: a systematic review of educational impact

The rapid evolution of technology has significantly transformed the educational landscape, with the advent of Large Language Models (LLMs) introducing new possibilities for personalized learning. This systematic review examines the educational impact of LLM-based learning systems compared to traditional educational approaches, focusing on six critical research questions. These questions explore the effectiveness of LLMs in enhancing student engagement, emotional and social development, real-time progress monitoring, and their role in creating fair and rigorous examination environments. Furthermore, the review addresses challenges such as ethical considerations, privacy concerns, and the extent to which LLMs can simulate real-world teaching experiences. A total of 55 studies, published between 2020 and 2024, were systematically analyzed to explore the impact of Large Language Models (LLMs) on educational outcomes, including emotional, social, and academic development. These studies included a combination of peer-reviewed articles, conference papers, and journal publications, which were selected through a set of predetermined inclusion and exclusion criteria. Quality assessment criteria ensured the inclusion of high-quality research focusing on the application of LLM-based AI technology in education. The review also highlights key challenges and limitations, including issues of accessibility, ethical dilemmas, and the integration of AI into traditional education systems. Findings underscore the potential of LLMs to revolutionize education through personalized learning, while also addressing the critical need for rigorous evaluation and ethical deployment to ensure equitable and effective outcomes.