In automated essay scoring (AES), essays are automatically graded without human raters. Many AES models based on various manually designed features or various architectures of deep neural networks (DNNs) have been proposed over the past few decades. Each AES model has unique advantages and characteristics. Therefore, rather than using a single-AES model, appropriate integration of predictions from various AES models is expected to achieve higher scoring accuracy. In this article, we propose a method that uses item response theory to integrate prediction scores from various AES models while taking into account differences in the characteristics of scoring behavior among models. It is found that the proposed method achieves higher accuracy than that of individual AES models and conventional score-integration methods. Furthermore, the proposed method facilitates interpreting each AES model's scoring characteristics and score-integration mechanism.