Huang, Tzu-YunTzu-YunHuangChen, Kuan-LinKuan-LinChenLin, Gong-HongGong-HongLinCHIEN-YU HUANG2026-01-032026-01-032025-12https://scholars.lib.ntu.edu.tw/handle/123456789/735004Visual–motor integration (VMI) is an important indicator in children with learning disabilities. We aimed to use performance in a coloring activity to identify children’s VMI developmental status. Methods: A sample of 505 preschool children (mean = 57.64, SD = 11.10) were recruited. Among them, data from 404 and 101 children were used as the training and testing data, respectively. The Beery–Buktenica Developmental Test of Visual–motor Integration, fourth Edition, (VMI-4) was used as an indicator for the model of artificial intelligence (AI). The total scores of the VMI-4 were calculated, and then based on the children’s age, the total scores were transferred into standard scores and the developmental status of visual–motor integration. The AI model comprised a regression model and classification model to predict the developmental status rated by the VMI-4. Results: In the training data, we found that the AI model comprising the support vector machine (SVM) regression model and eXtreme Gradient Boostin (XGBoost) classification model exhibited the best performance (accuracy: 86.2%; sensitivity: 84.7%; and specificity, 85.4%). The results of the trained AI model on the testing data indicated good performance, with accuracy, sensitivity, and specificity of 80.20%, 73.68%, and 81.71%, respectively. Conclusions: Combining the coloring activity with the AI technique has great potential as a screening tool to identify children’s VMI developmental status.enArtificial intelligencecoloringvisual–motor integration[SDGs]SDG3journal article10.1080/07853890.2025.257872541178323