Application of Neural Network and Structural Model in AI Educational Performance Analysis

Abstract

In this study, we examine the effects of AI on education, specifically on students' cognitive and motivational factors and behaviors. Artificial neural networks (ANNs), including recurrent and deep neural networks, are used to analyze critical elements of educational training. Our research involves training ANNs and using Smart partial least squares regression (Smart PLS) for deep learning analysis. The findings indicate a high accuracy rate of 94% in factor analysis by ANNs, indicating a positive effect of AI on educational training. Smart PLS results show that each dimension positively affects student behavior. The study shows that AI technology can effectively increase learning efficiency in education, benefiting student education and training. The integration of ANN and Smart-PLS analyses supports the conclusion that AI technology can increase learning efficiency in education.