When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration

2026-07-16

AI Summary

The widespread adoption of generative AI blurs the boundaries of users’ actual contributions in creative processes, often leading to misperceptions of authorship. This work introduces the novel concept of “authorship calibration”—defined as users’ accurate self-assessment of their genuine contribution in human-AI collaboration—and presents an empirical analysis based on the CoAuthor dataset. The study reveals that frequent AI users systematically overestimate their own input, whereas infrequent users exhibit more accurate calibration, thereby uncovering a link between AI usage intensity and metacognitive bias. These findings offer a new theoretical lens and empirical foundation for understanding how generative AI reshapes human perceptions of creative agency and authorship.

Abstract

The broad adoption of Artificial Intelligence (AI), especially Generative AI, raises pressing questions about how users interact with these systems to produce new content. In this paper, we introduce the concept of authorship calibration, defined as users awareness of their actual authorship when interacting with AI. Using the CoAuthor dataset, we empirically examine how authorship calibration varies across users and how it relates to their frequency of AI use. Our results reveal high variability: users relying heavily on AI tend to misjudge their authorship, whereas those using AI less frequently exhibit more accurate authorship calibration. These findings suggest that AI can obscure users perception of their own authorship. In learning contexts, miscalibration can affect metacognitive monitoring and learning strategies, ultimately impacting learning outcomes. Fostering authorship calibration then appears essential for promoting responsible and educationally meaningful AI integration.

Problem

• authorship calibration
• Generative AI
• AI contribution
• metacognitive monitoring
• learning outcomes

Innovation

• authorship calibration
• generative AI
• human-AI collaboration
• metacognition
• AI in education