Child Data

Research on child data focuses on leveraging diverse data types – including biometric data (e.g., ear and facial features), linguistic input, and behavioral interactions with technology – to understand child development and improve AI systems for children. Current research employs deep learning models, such as VGG16, MobileNet, and Stable Diffusion, along with advanced techniques like image-to-image translation and large language models, to analyze and generate synthetic child data, addressing privacy concerns while enabling broader research. This work is significant for advancing our understanding of child development across cognitive, linguistic, and motor domains, as well as for developing more accurate and inclusive AI applications tailored to children's unique needs.