Tebyan: An AI-powered system for estimating developmental levels from children's human figure drawings

ObjectiveTo develop and evaluate an AI-powered mobile application, Tebyan, to estimate children's developmental levels from Draw-A-Person (DAP) test drawings and support the early identification of developmental concerns.MethodsThe system predicts drawing-based developmental age and compares it with the child's chronological age, where an age gap may indicate the need for further evaluation. Nine deep learning models were created using MobileNet, ResNet, and EfficientNet architectures across binary, four-class, and eight-class configurations.ResultsAcross all nine model configurations, performance decreased as class granularity increased. The two-class models achieved strong and balanced results (≈80% accuracy), four-class models showed moderate performance (≈55-65%), and eight-class models performed lowest (≈30-40%). Macro-averaged sensitivity, specificity, precision, and F1-scores were reported with 95% confidence intervals. Based on balance and stability, the four-class MobileNet model was selected for integration into the Tebyan application, supporting a more precise evaluation of developmental progression from the drawings.ConclusionTebyan provides an AI-based approach for estimating developmental levels from children's drawings by comparing the model's predicted age group with the child's actual age. While not a diagnostic tool, the system offers a supportive resource that may help caregivers and educators identify developmental patterns that warrant further attention.