The AI-driven age detection in children's drawings: a deep learning approach for late childhood assessment

Artificial Intelligence (AI) has revolutionized various fields, including psychology and behavioral sciences, by offering novel approaches to understanding human development and behavior. Recent advancements in machine learning have enabled the analysis of children's drawings to gain insights into their cognitive and emotional states. Despite progress, there remains a significant challenge in accurately detecting the age of older children from their drawings. While numerous studies have focused on early childhood development through drawings, there is a paucity of research on late age detection using AI. Specifically, the ability to determine the age of older children from their artwork remains underexplored and imprecise. Our research addresses this gap by developing an AI-based model that accurately detects the age of older children based on their drawings. We employ a deep learning approach, utilizing Long short-term memory (LSTM) networks to analyze various features in children's artwork that correlate with age. We compiled a dataset of drawings from children aged 2-10 years. Our model was trained and validated on this dataset, achieving a high accuracy rate in age detection. Key features such as complexity, use of space, and thematic content were analyzed. The results demonstrate that our AI model can predict the age of children with a significant degree of accuracy, outperforming existing methods. These findings enhance our understanding of cognitive and artistic development in older children. The implications of this research are far-reaching, offering potential applications in educational psychology, developmental assessments, and personalized education plans.