Using machine learning to support pedagogy in the arts

Abstract

Teaching artistic skills to children presents a unique challenge: High-level creative and social elements of an artistic discipline are often the most engaging and the most likely to sustain student enthusiasm, but these skills rely on low-level sensorimotor capabilities, and in some cases rote knowledge, which are often tedious to develop. We hypothesize that computer-based learning can play a critical role in connecting “bottom-up” (sensorimotor-first) learning in the arts to “top-down” (creativity-first) learning, by employing machine learning and artificial intelligence techniques that can play the role of the sensorimotor expert. This approach allows learners to experience components of higher-level creativity and social interaction even before developing the prerequisite sensorimotor skills or academic knowledge.