Generative, ML-driven interactive systems have the potential to change how
people interact with computers in creative processes - turning tools into
co-creators. However, it is still unclear how we might achieve effective
human-AI collaboration in open-ended task domains. There are several known
challenges around communication in the interaction with ML-driven systems. An
overlooked aspect in the design of co-creative systems is how users can be
better supported in learning to collaborate with such systems. Here we reframe
human-AI collaboration as a learning problem: Inspired by research on team
learning, we hypothesize that similar learning strategies that apply to
human-human teams might also increase the collaboration effectiveness and
quality of humans working with co-creative generative systems. In this position
paper, we aim to promote team learning as a lens for designing more effective
co-creative human-AI collaboration and emphasize collaboration process quality
as a goal for co-creative systems. Furthermore, we outline a preliminary
schematic framework for embedding team learning support in co-creative AI
systems. We conclude by proposing a research agenda and posing open questions
for further study on supporting people in learning to collaborate with
generative AI systems.Comment: ACM CHI 2022 Workshop on Generative AI and HC