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

• URL: http://arxiv.org/abs/2607.15006v1
• Date: Thu, 16 Jul 2026 13:52:59 GMT
• Title: When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration
• Abstract summary: We introduce the concept of authorship calibration, defined as users awareness of their actual authorship when interacting with AI.<n>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.
• Score: 1.9336815376402718
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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