Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI Literature

2025-07-08

AI Summary

This study addresses the lack of systematic theoretical integration regarding dynamic agency allocation in human-AI co-creation. We conducted a scoping review of 134 papers published between 2003 and 2023 in top-tier HCI venues (CHI, UIST, CSCW). First, we propose an integrated *performative agency* framework, identifying four cross-contextual agency configuration patterns. Second, we develop the first actionable taxonomy of control mechanisms—categorizing interventions by timing, permission granularity, and negotiation modality. Third, we map agency configurations across domains including creative work, office productivity, and education. Our contributions advance the design of trustworthy, ethically grounded collaborative AI systems and foster a paradigmatic understanding of human–AI control relations within HCI and CSCW research.

Technology Category

Humans and AI: Planning and Decision Support for Human-Machine TeamsMultiagent Systems: Mechanism DesignCognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

Responsible Web: Machine-in-the-loop, human agency and autonomySemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsEconomics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

As Artificial Intelligence (AI) increasingly becomes an active collaborator in co-creation, understanding the distribution and dynamic of agency is paramount. The Human-Computer Interaction (HCI) perspective is crucial for this analysis, as it uniquely reveals the interaction dynamics and specific control mechanisms that dictate how agency manifests in practice. Despite this importance, a systematic synthesis mapping agency configurations and control mechanisms within the HCI/CSCW literature is lacking. Addressing this gap, we reviewed 134 papers from top-tier HCI/CSCW venues (e.g., CHI, UIST, CSCW) over the past 20 years. This review yields four primary contributions: (1) an integrated theoretical framework structuring agency patterns, control mechanisms, and interaction contexts, (2) a comprehensive operational catalog of control mechanisms detailing how agency is implemented; (3) an actionable cross-context map linking agency configurations to diverse co-creative practices; and (4) grounded implications and guidance for future CSCW research and the design of co-creative systems, addressing aspects like trust and ethics.

Problem

• Understanding agency dynamics in Human-AI co-creation
• Mapping agency configurations in HCI/CSCW literature
• Providing design guidance for co-creative systems

Innovation

• Integrated framework for agency patterns and control
• Catalog detailing agency implementation mechanisms
• Cross-context map linking agency to co-creation