Few-Shot Deep Learning Empowers Education Personalization: Technical Principles, Application Pathways, and Challenges

The demand for personalized education is becoming increasingly urgent. However, artificial intelligence (AI) applications in education, which rely on big data paradigms, face fundamental limitations such as cold starts, and data scarcity. Few-shot learning (FSL), as a "bionic" learning paradigm, exhibits a high degree of theoretical alignment with the personalized needs of educational scenarios. It offers a promising path for building data-efficient personalized education systems. This paper systematically analyzes the core technical principles of FSL, explores its applicability to key educational scenarios, and constructs a technical implementation framework. It provides theoretical and architectural guidance for the application of FSL in the field of personalized education. Moreover, we prospectively identify potential risks and future directions.