crea.blender: A Neural Network-Based Image Generation Game to Assess Creativity
This work addresses the need for systematic assessment of human creativity in human-computer collaborative tasks, though it is incremental as a pilot study.
The researchers tackled the problem of assessing human creativity in co-creative systems by developing crea.blender, a neural network-based image generation game, and found in a pilot study that it provided a playful experience, gave players control, and elicited varied behaviors, supporting its potential for scalable creativity assessment.
We present a pilot study on crea.blender, a novel co-creative game designed for large-scale, systematic assessment of distinct constructs of human creativity. Co-creative systems are systems in which humans and computers (often with Machine Learning) collaborate on a creative task. This human-computer collaboration raises questions about the relevance and level of human creativity and involvement in the process. We expand on, and explore aspects of these questions in this pilot study. We observe participants play through three different play modes in crea.blender, each aligned with established creativity assessment methods. In these modes, players "blend" existing images into new images under varying constraints. Our study indicates that crea.blender provides a playful experience, affords players a sense of control over the interface, and elicits different types of player behavior, supporting further study of the tool for use in a scalable, playful, creativity assessment.