Collaborative Making of a Boundary Object for Classifying Generative AI Learning Activities

Abstract Educational research has seen a surge in publications investigating the use of Generative AI (GenAI) for learning. The aim of this article is to initiate a discussion of how to systematise GenAI learning activities in a way that reflects the postdigital entanglement of humans and technology in contemporary learning, identifying key constituent aspects and how they mesh. We explore potential core categories of such a systematisation, and one way that concrete examples of GenAI learning activities might potentially be classified within them. We do this by proposing, testing, and critically reflecting on a taxonomy to describe key relationships between human and GenAI elements of GenAI-infused learning activities. Our iterative development of the taxonomy took the form of a collaborative, non-linear, postdigital dialogue. Building on pedagogical theory, the initial categories of Learning Objective, GenAI Objects and Processes, Representation Format, Epistemic Engagement, Human-GenAI Labour Distribution, and Artefacts are proposed. Then, using concrete examples, these categories are tested, amended, elaborated, and challenged in a multi-voiced discussion between the authors. The outcomes suggest that the taxonomy, as a generalised artefact, inevitably falls short of adequately portraying all aspects of specific entangled GenAI learning activities. However, our testing and dialogue about the taxonomy show that it can be concretised for each particular situation, and the level of generality of the categories allows comparison across specific learning activities. The dialogue also shows how the taxonomy can serve as a prompt to stimulate educators’ imagination about GenAI activities.