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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.

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References

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Correspondence to Dan Morris.

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Morris, D., Fiebrink, R. Using machine learning to support pedagogy in the arts. Pers Ubiquit Comput 17, 1631–1635 (2013). https://doi.org/10.1007/s00779-012-0526-1

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  • DOI: https://doi.org/10.1007/s00779-012-0526-1

Keywords

  • Machine learning
  • Education
  • Creativity