<![CDATA[Digital transformation has shifted student assessment management toward more efficient and data-driven practices, yet schools still face challenges in automated data processing, timely feedback, and data-informed decision-making. This study aimed to examine the relationships between Artificial Intelligence (AI)-based automated assessment, ClassPoint-assisted assessment, and student assessment management effectiveness. A quantitative correlational design was employed involving 90 public elementary school teachers in Tretep District, Temanggung Regency, Indonesia. Data were collected using five-point Likert-scale questionnaires and analyzed using descriptive statistics, prerequisite tests, and simple and multiple linear regression. The findings showed that AI-based automated assessment was positively and significantly associated with student assessment management (R = 0.780, p < 0.001), with R² = 0.609, indicating that AI statistically explained 60.9% of the variance in the separate simple regression model. ClassPoint-assisted assessment was also positively and significantly associated with student assessment management (R = 0.683, p < 0.001), with R² = 0.466, indicating 46.6% explained variance in its separate simple regression model. These percentages represent explained variance rather than unique contributions. In the multiple regression model, both predictors were jointly and significantly associated with student assessment management (R = 0.844, F = 107.614, p < 0.001), with Adjusted R² = 0.706, indicating 70.6% jointly explained variance. The findings suggest that AI-based and interactive digital assessment technologies are complementary components of digital assessment management.]]>
