Using Machine Learning to Support Pedagogy in the Arts (2011)

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 higherlevel creativity and social interaction even before developing the prerequisite sensorimotor skills or academic knowledge.