Using Machine Learning to Support Pedagogy in the Arts

Fiebrink, Rebecca. 2013. Using Machine Learning to Support Pedagogy in the Arts. Personal and Ubiquitous Computing, 17(8), pp. 1631-1635. ISSN 1617-4909 [Article]

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Abstract or Description

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.

Item Type:


Identification Number (DOI):


Machine learning, Education, Creativity

Departments, Centres and Research Units:

Computing > Embodied AudioVisual Interaction Group (EAVI)
Research Office > REF2014


21 April 2012Published Online
December 2013Published

Item ID:


Date Deposited:

04 Nov 2013 11:09

Last Modified:

27 Apr 2021 12:54

Peer Reviewed:

Yes, this version has been peer-reviewed.


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