The Collaboration Gap in Human-AI Work

2026-04-20

AI Summary

This study addresses the fragility of collaboration with large language models (LLMs) in domains such as programming, design, and writing, where misinterpretations, missing assumptions, and response biases often undermine effective interaction. Drawing on in-depth interviews with 16 designers, developers, and AI practitioners, alongside a review of human–AI collaboration literature, the authors employ a constructivist grounded theory approach to develop a conceptual framework for understanding LLM collaboration vulnerabilities. The framework identifies three collaborative structures: one-off assistance, weak collaboration characterized by asymmetric repair, and stable collaboration enabled by “grounding conditions.” It further reveals that collaboration failures frequently arise when the appearance of partnership exceeds the actual capacity for grounded interaction. This work provides a systematic theoretical foundation for analyzing and enhancing the quality of LLM-mediated collaborative work.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Humans and AI: Human-AI Collaboration / Human-AI Teaming

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production 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.

Problem

• human-AI collaboration
• large language models
• grounding
• interaction structure
• repair

Innovation

• grounding
• human-AI collaboration
• LLM-enabled systems
• interaction structure
• repair