When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration
Human-Computer InteractionArtificial Intelligence
Summary
The authors study how people feel about who really 'creates' content when they use AI tools to help them. They call this feeling 'authorship calibration,' meaning how well users understand their own role in making something with AI. Their research shows that people who use AI a lot tend to wrongly think they contributed less or more than they actually did, while those who use AI less often have a better sense of their authorship. This misunderstanding could affect how people learn and monitor their work, especially in educational settings. The authors suggest it is important to help users better understand their authorship to use AI responsibly and effectively.
Authors
Célina Treuillier, Denis Lalanne
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.