With the development of artificial intelligence, personalized learning has attracted much attention as an integral part of intelligent education. In recent years, countries and regions such as China, the United States, and the European Union have increasingly recognized the importance of personalized learning, emphasizing its potential to integrate large-scale education with individualized instruction effectively. This survey provides a comprehensive analysis of personalized learning by reviewing relevant studies published in major conferences and journals between January 2017 and April 2025. We examine its definition, objectives, and underlying educational theories, highlighting its pedagogical significance. Furthermore, we explore personalized learning from two key dimensions: student modeling and personalized recommendations. Student modeling is analyzed from both cognitive and non-cognitive perspectives, while recommendation approaches are categorized based on their specific objectives. Additionally, we investigate the interplay between these components and their role in enhancing personalized learning. Beyond theoretical and algorithmic insights, this survey reviews real-world applications, demonstrating personalized learning's effectiveness in educational practice. Finally, we discuss key challenges and future directions, offering a multidimensional perspective that bridges theory and practice.

翻译：随着人工智能的发展，个性化学习作为智能教育的重要组成部分备受关注。近年来，中国、美国和欧盟等国家和地区日益认识到个性化学习的重要性，强调其有效整合大规模教育与个性化教学的潜力。本文通过对2017年1月至2025年4月期间在主要会议和期刊上发表的相关研究进行综述，对个性化学习进行了全面分析。我们审视了其定义、目标和基础教育理论，突出了其教学意义。此外，我们从两个关键维度探讨了个性化学习：学生建模和个性化推荐。学生建模从认知和非认知两个角度进行分析，而推荐方法则根据其具体目标进行分类。同时，我们研究了这些组成部分之间的相互作用及其在增强个性化学习中的作用。除了理论和算法见解外，本文还综述了实际应用，展示了个性化学习在教育实践中的有效性。最后，我们讨论了关键挑战和未来方向，提供了一个连接理论与实践的多维视角。