# Using a coloring activity to identify children’s development of visual–motor integration: an application of artificial intelligence

## TL;DR

This study uses a coloring activity and AI to assess children's visual-motor integration skills, showing promising results for early screening.

## Contribution

The novel use of AI models (SVM and XGBoost) with a coloring activity to evaluate children's visual-motor integration development.

## Key findings

- The AI model achieved 86.2% accuracy in training data for predicting VMI developmental status.
- The model showed good performance on testing data with 80.20% accuracy and 81.71% specificity.
- Combining coloring activities with AI has potential as a screening tool for visual-motor integration.

## Abstract

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.

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.

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.

Combining the coloring activity with the AI technique has great potential as a screening tool to identify children’s VMI developmental status.