Main Article Content
Purpose: This review research paper aims to investigate the impact of personalized learning paths facilitated by artificial intelligence (AI) on educational outcomes. It explores the evolving landscape of education and the potential benefits of tailoring curricula to individual learner needs through AI-driven technologies.
Theoretical Framework: The study is grounded in a theoretical framework that integrates principles of personalized learning, cognitive psychology, and AI algorithms. It seeks to understand how adaptive learning systems can enhance the educational experience by dynamically adjusting content delivery, pacing, and assessments based on individual student characteristics.
Design/Methodology/Approach: The research adopts a comprehensive review methodology, synthesizing existing literature on AI-driven personalized learning in diverse educational settings. It analyzes empirical studies, case reports, and theoretical frameworks to provide a nuanced understanding of the methodologies employed, challenges faced, and successes achieved in implementing personalized learning paths.
Findings: The findings of this review indicate a positive correlation between AI-driven personalized learning paths and improved academic performance, engagement, and retention. The paper identifies key success factors and potential pitfalls in the implementation of personalized learning, shedding light on the nuanced effects across different educational levels and subjects.
Research, Practical & Social Implications: This research contributes valuable insights for educators, policymakers, and researchers by highlighting the potential of AI-driven personalized learning to address diverse learning styles and individual needs. The paper discusses practical considerations for the integration of such technologies in educational institutions and underscores the social implications of fostering more inclusive and adaptive learning environments.
Originality/Value: The originality of this research lies in its comprehensive synthesis of existing literature, providing a holistic overview of the current state of AI-driven personalized learning. By identifying gaps in knowledge and offering practical implications, this paper contributes to the ongoing dialogue on the transformative potential of personalized learning paths in education.