Application and Challenges of Multi-Modal Data Fusion in Adaptive Learning System

With the rapid development of artificial intelligence and machine learning technology, multi-modal fusion focuses on how to fuse multi-modal data with certain architectures and methods to jointly contribute to solving goals and tasks, and multi-modal fusion emphasizes the design of the use of multiple information in the algorithm framework for solving tasks, which provides a rich source of information for adaptive learning, so as to more accurately identify students' learning status, behaviors and needs. However, despite its great potential, multi-modal data fusion still faces many challenges in practical applications. The purpose of this study is to explore the application of multi-modal data fusion in adaptive learning systems, and to analyze the main technical and practical challenges it faces. Finally, we put forward some coping strategies and look forward to future research directions.