Design ideation is a prime creative activity in design. However, it is challenging to support computationally, because of its quickly evolving and exploratory nature. This paper studies cooperative contextual bandits (CCB) as a machine learning method for interactive ideation support. CCBs can learn to propose domain-relevant contributions and adapt their exploration/ exploitation strategy. We developed a CCB for an interactive design ideation tool that 1) suggests inspirational and situationally relevant materials (“may AI?”), 2) explores and exploits inspirational materials with the designer, and 3) explains suggestions to aid reflection. As an application case, we study digital mood board design, wherein visual inspirational materials are collected and curated in collages. In a controlled study, 14 of 16 professional designers preferred the CCB-augmented tool. CCBs are promising for ideation activities where adaptive and steerable support is welcome but designers need to retain full control of the outcome.

Citation: Koch, Janin, Andrés Lucero, Lena Hegemann and Antti Oulasvirta. 2019. “May AI? Design Ideation with Cooperative Contextual Bandits”, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, 2019.

Project Page with Code and Additional Material: https://userinterfaces.aalto.fi/ccb/

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