Harnessing Artificial Intelligence for Personalized Learning: A Systematic Review

Introduction: the document presents a comprehensive review of the utilization of Artificial Intelligence (AI) in personalized learning within the educational context. The study aims to investigate the various approaches to using ML algorithms for personalizing educational content, the impact and implications of these approaches on student performance, and the challenges and limitations associated with AI in personalized learning. The research questions are structured around these three broad areas, focusing on the AI methods used in education, their impact on students' academic outcomes, and the challenges and limitations associated with AI. Methods: the study employed a systematic literature review methodology, utilizing a structured and replicable search strategy to identify relevant research material from high-impact peer-reviewed journals published between 2015 and 2023. Inclusion and exclusion criteria were applied to select studies that focused on AI in education for personalized learning. Data collection involved extracting relevant data from the selected studies, and a thematic analysis was conducted to identify themes related to the research questions. The selected studies were graded based on their quality, and the results were summarized in a narrative synthesis. Results: the analysis of the selected research papers revealed the significance of adaptive learning systems, recommender systems, NLP techniques, and intelligent tutoring systems in tailoring educational content to individual students. These approaches have demonstrated their effectiveness in enhancing student engagement, improving learning outcomes, and providing personalized feedback. However, the study also identified challenges and limitations that need to be addressed for the successful implementation of AI in personalized learning. Conclusions: the study identified several limitations, including potential bias toward certain research areas, contextual factors influencing the effectiveness of ML algorithms, and the need for further research to examine the applicability of different approaches across diverse contexts. The findings highlight the research gaps, limitations, and potential future research areas in the field of AI-based personalized learning in education