# A Comprehensive Exploration of Personalized Learning in Smart Education: From Student Modeling to Personalized Recommendations

arXiv: https://arxiv.org/abs/2402.01666 · HTML: https://mlsanity.com/paper/2402.01666/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2024.

## Built on by (most cited first, 5 of 5)

- [Endowing Interpretability for Neural Cognitive Diagnosis by Efficient Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2405.14399/) (2024)
- [Tailoring Diagnostic Modeling to Individual Learners: Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction](https://mlsanity.com/paper/2508.11184/) (2025)
- [Marked Pedagogies: Examining Linguistic Biases in Personalized Automated Writing Feedback](https://mlsanity.com/paper/2603.12471/) (2026)
- [Advancing Problem-Based Learning in Biomedical Engineering in the Era of Generative AI](https://mlsanity.com/paper/2503.16558/) (2025)
- [Explainable Knowledge Tracing via Probabilistic Embeddings and Pattern-based Reasoning](https://mlsanity.com/paper/2605.09369/) (2026)

## Abstract (arXiv)

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.