Reflection Across AI-based Music Composition

Reflection is fundamental to creative practice. However, the plu-
rality of ways in which people reflect when using AI Generated
Content (AIGC) is underexplored. This paper takes AI-based mu-
sic composition as a case study to explore how artist-researcher
composers reflected when integrating AIGC into their music com-
position process. The AI tools explored range from Markov Chains
for music generation to Variational Auto-Encoders for modifying
timbre. We used a novel method where our composers would pause
and reflect back on screenshots of their composing after every hour,
using this documentation to write first-person accounts showcas-
ing their subjective viewpoints on their experience. We triangulate
the first-person accounts with interviews and questionnaire mea-
sures to contribute descriptions on how the composers reflected.
For example, we found that many composers reflect on future di-
rections in which to take their music whilst curating AIGC. Our
findings contribute to supporting future explorations on reflection
in creative HCI contexts.