Using a coloring activity to identify children's development of visual-motor integration: an application of artificial intelligence.

AIM: Visual-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.