An Evidence-based Workflow for Studying and Designing Learning Supports for Human-AI Co-creation

Generative artificial intelligence (GenAI) systems introduce new possibilities for enhancing professionals' workflows, enabling novel forms of human–AI co-creation. However, professionals often struggle to learn to work with GenAI systems effectively. While research has begun to explore the design of interfaces that support users in learning to co-create with GenAI, we lack systematic approaches to investigate the effectiveness of these supports. In this paper, we present a systematic approach for studying how to support learning to co-create with GenAI systems, informed by methods and concepts from the learning sciences. Through an experimental case study, we demonstrate how our approach can be used to study and compare the impacts of different types of learning supports in the context of text-to-image GenAI models. Reflecting on these results, we discuss directions for future work aimed at improving interfaces for human–AI co-creation.