The Collaboration Gap in Human-AI Work
For designers and practitioners of LLM-enabled systems, this work provides a conceptual framework to diagnose and address collaboration failures, though it is incremental as it synthesizes existing concepts.
The paper identifies a 'collaboration gap' in human-AI work with LLMs, where users must repeatedly repair misaligned responses. Based on 16 interviews, it proposes a framework distinguishing three interaction structures (one-shot assistance, weak collaboration, grounded collaboration) and argues that collaboration breaks down when perceived partnership exceeds grounding capacity.
LLMs are increasingly presented as collaborators in programming, design, writing, and analysis. Yet the practical experience of working with them often falls short of this promise. In many settings, users must diagnose misunderstandings, reconstruct missing assumptions, and repeatedly repair misaligned responses. This poster introduces a conceptual framework for understanding why such collaboration remains fragile. Drawing on a constructivist grounded theory analysis of 16 interviews with designers, developers, and applied AI practitioners working on LLM-enabled systems, and informed by literature on human-AI collaboration, we argue that stable collaboration depends not only on model capability but on the interaction's grounding conditions. We distinguish three recurrent structures of human-AI work: one-shot assistance, weak collaboration with asymmetric repair, and grounded collaboration. We propose that collaboration breaks down when the appearance of partnership outpaces the grounding capacity of the interaction and contribute a framework for discussing grounding, repair, and interaction structure in LLM-enabled work.