Team Learning as a Lens for Designing Human-AI Co-Creative Systems
This addresses the challenge of improving collaboration process quality in co-creative AI systems for users in creative domains, but it is incremental as it builds on existing team learning concepts.
The paper tackles the problem of achieving effective human-AI collaboration in open-ended creative tasks by reframing it as a learning problem, proposing that team learning strategies from human-human teams can enhance collaboration effectiveness and quality, and outlines a preliminary framework and research agenda for designing co-creative systems.
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.