  Assessing artistic creativity has long challenged researchers, with
traditional methods proving time-consuming. Recent studies have applied machine
learning to evaluate creativity in drawings, but not paintings. Our research
addresses this gap by developing a CNN model to automatically assess the
creativity of students' paintings. Using a dataset of 600 paintings by
professionals and children, our model achieved 90% accuracy and faster
evaluation times than human raters. This approach demonstrates the potential of
machine learning in advancing artistic creativity assessment, offering a more
efficient alternative to traditional methods.
