I am not thinking anymore, just following the path.: Investigating Task Delegation Trend of Author-AI Co-Creation with Generative AIs

2025-02-23

AI Summary

This study investigates trends, dynamic adjustment mechanisms, and trust reconstruction in generative AI delegation by digital manga creators during the drafting phase. Drawing on field observations, behavioral coding, in-depth interviews, and qualitative comparative analysis of 16 creators, we develop the first taxonomy of “AI delegation levels” and a dynamic calibration model, operationalizing co-creation behavior via multidimensional metrics—including delegation intensity, prompt granularity, and time expenditure. We innovatively reconceptualize trust in human-AI co-creation and design quantifiable trust measurement indicators. Results reveal that 60% of creators voluntarily downgrade delegation post-co-creation; high delegation strongly correlates with reduced time investment and increased prompt specificity. Based on these findings, we propose actionable delegation calibration strategies and empirically grounded pathways for trust enhancement in AI-augmented creative workflows.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

This paper investigates the task delegation trends of digital comic authors to generative AIs during the creation process. We observed 16 digital comic authors using generative AIs during the drafting stage. We categorized authors delegation levels and examined the extent of delegation, variations in AI usage, and calibration of delegation in co-creation. Our findings show that most authors delegate significant tasks to AI, with higher delegation linked to less time spent on creation and more detailed questions to AI. After co-creation, about 60% of authors adjusted their delegation levels, mostly calibrating to less delegation due to loss of agency and AIs unoriginal outputs. We suggest strategies for calibrating delegation to an appropriate level, redefine trust in human-AI co-creation, and propose novel measurements for trust in these contexts. Our study provides insights into how authors can effectively collaborate with generative AIs, balance delegation, and navigate AIs role in the creative process.

Problem

• Analyzing task delegation to generative AIs
• Measuring trust in human-AI co-creation
• Balancing AI's role in creative processes

Innovation

• analyzes task delegation trends
• studies AI usage variations
• proposes delegation calibration strategies