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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.
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Making Design Tools Like a Weaver: Four Rules
What would happen if we designed CAD systems like a weaver designs cloth? Drawing from our ongoing collaborations with weavers, we suggest four rules to bring these qualities to your own practice: follow the materials, privilege the present and personal, form kinships with the past, and design systems of notations.
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- Award ID(s):
- 1943109
- PAR ID:
- 10482918
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- XRDS: Crossroads, The ACM Magazine for Students
- Volume:
- 29
- Issue:
- 4
- ISSN:
- 1528-4972
- Page Range / eLocation ID:
- 54 to 58
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3596929
