A Comprehensive Review of AI-Driven Personalized Study Partners in Modern Education

Abstract

Artificial intelligence (AI) has significantly transformed the landscape of personalized learning, enabling adaptive systems that respond intelligently to individual learner needs. This paper presents a comprehensive review of AI-driven personalized study partners, examining their underlying architectures, methodologies, and applications in modern education. The review synthesizes findings from recent studies on natural language processing, recommender systems, and cognitive modeling used to enhance learner engagement, motivation, and retention. It explores how these AI systems leverage data analytics and learner profiling to deliver customized content, feedback, and study schedules. Furthermore, the paper identifies key challenges such as data privacy, model bias, scalability, and the need for transparent AI decision-making. Emerging trends, including multimodal learning interfaces and emotional-adaptive systems, are also discussed. By consolidating current knowledge, this review aims to provide educators, researchers, and developers with insights into designing effective, ethical, and scalable AI-based personalized study partners for the future of adaptive learning environments.