Author(s)

Hui Wu, Xiya Wang

Corresponding Author

Hui Wu

Abstract

This paper focuses on the innovative development of education assessment. In response to the limitations of traditional education assessment methods, this paper conducts in-depth research on an artificial intelligence-based education assessment system utilizing multimodal data fusion. By integrating multi-source data, including text, images, audio, and video, and combining machine learning and deep learning technologies, a comprehensive, objective, and personalized education assessment system is constructed. This paper discusses the theoretical foundation, technical framework, and the specific processes involved in system construction. It analyzes application examples in various contexts, such as school education and online learning. Additionally, it proposes optimization strategies from the perspectives of technology, education, teaching, management, and policy. Research shows that the evaluation system effectively enhances the accuracy and efficiency of educational evaluation, provides strong support for improving educational quality, and is also of great significance in promoting educational equity and personalized development.

Keywords

AI; Education Assessment; Multimodal Data; Personalized Evaluation; Learning Analytics