# Team Learning as a Lens for Designing Human-AI Co-Creative Systems

arXiv: https://arxiv.org/abs/2207.02996 · HTML: https://mlsanity.com/paper/2207.02996/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 6 Jul 2022.

## Builds on (most cited first, 2 of 2)

- [An Interaction Framework for Studying Co-Creative AI](https://mlsanity.com/paper/1903.09709/) (2019)
- [SimuLearn: Fast and Accurate Simulator to Support Morphing Materials Design and Workflows](https://mlsanity.com/paper/2007.15065/) (2020)

## Built on by (most cited first, 1 of 1)

- [Evaluating Human-AI Collaboration: A Review and Methodological Framework](https://mlsanity.com/paper/2407.19098/) (2024)

## Abstract (arXiv)

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.