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.
翻译:由ML驱动的交互式系统具有改变人们如何在创造性进程中与计算机互动的潜力 -- -- 将工具转化为共同培养器。然而,我们如何在开放式任务领域实现有效的人类 -- -- AI合作仍不清楚。在与ML驱动的系统的互动中,在沟通方面存在着一些已知的挑战。在共同创造系统的设计中,一个被忽视的方面是用户在学习与这些系统合作方面如何得到更好的支持。我们在这里将人类 -- -- AI合作重新定位为一个学习问题:在团队学习研究的启发下,我们假设适用于人类团队的类似学习战略也可能提高与共同创造型基因系统合作的人类的合作效率和质量。在本立场文件中,我们的目标是促进团队学习,作为设计更有效的共同创造型人类 -- -- -- AI合作的透镜,强调合作进程的质量,以此作为共同创造系统的目标。此外,我们概述了一个将团队学习支持团队学习纳入共同创造型AI系统的初步示意图框架。我们最后提出研究议程,并提出了关于支持人们学习与AI系统合作的进一步研究的开放问题。