Source note2 min read
When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration
Source
arXiv
arxiv.org/abs/2607.15006v1
Source
- Source name: arXiv
- Source kind: paper
- Authors / organization: Célina Treuillier, Denis Lalanne
- Venue: arXiv: cs.HC
- Published: 2026-07-16
- Collected: 2026-07-17
- URL: http://arxiv.org/abs/2607.15006v1
- PDF: https://arxiv.org/pdf/2607.15006v1
- DOI: UNKNOWN
Plain-Language Brief
This research introduces the concept of 'authorship calibration' – users' awareness of their actual contributions when working with AI. The study, based on the CoAuthor dataset, found that frequent AI users tend to underestimate their authorship role, while less frequent users have a more accurate perception. This misperception could affect learning and the integration of AI in education.
What the Document Says
- The paper discusses the concept of authorship calibration, defined as user awareness of actual authorship when interacting with AI.
- The study uses the CoAuthor dataset to examine authorship calibration across users based on their frequency of AI use.
- Results show that frequent AI users tend to underestimate their authorship, while less frequent users have more accurate perceptions.
- Miscalibration in learning contexts could affect metacognitive monitoring and learning strategies, potentially impacting learning outcomes.
- Promoting authorship calibration is suggested as essential for responsible and educationally meaningful AI integration.
Key Concepts Mentioned
- Artificial Intelligence
- Generative AI
- Authorship Calibration
- CoAuthor dataset
- Learning contexts
Claims Made by the Document
- Frequent AI users tend to misjudge their authorship.
- Less frequent AI users exhibit more accurate authorship calibration.
Evidence Used
- Results from the empirical study on the CoAuthor dataset.
Methods / Approach
UNKNOWN
Limitations Stated by the Authors
UNKNOWN
Limitations Noted by the Agent
Agent interpretation — not from the source.
UNKNOWN
Open Questions
UNKNOWN
Notes for Future Q&A
May include interpretation. Labeled where it does.
UNKNOWN
Raw Abstract / Excerpt
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.
