The Collaboration Gap in Human-AI Work

TL;DR

This paper explores why human-AI collaboration with large language models often fails, emphasizing the importance of grounding conditions and interaction structures for stable cooperation.

Contribution

It introduces a conceptual framework analyzing collaboration breakdowns, based on interviews and literature, highlighting the role of grounding and repair in human-AI work.

Findings

Collaboration often breaks down when partnership appears but grounding is insufficient.

Identifies three structures of human-AI work: one-shot, weak collaboration, grounded collaboration.

Proposes a framework for understanding grounding, repair, and interaction in LLM-enabled systems.

Abstract

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.