When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration
3.00T1 sourcearXiv cs.HC
arXiv cs.HCoriginal source ↗
Source record
Published by arXiv cs.HC (T1 source). The original is at https://arxiv.org/abs/2607.15006.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryEmpirical study introducing 'authorship calibration'—users' awareness of their own contribution when using generative AI. Using the CoAuthor dataset, the paper finds that heavy AI users tend to misjudge their own authorship, while lighter users calibrate more accurately.
Why it mattersFrames a measurable concept for tracking how AI use distorts self-perception of contribution. Useful background for those designing writing workflows or AI-use policies.
Cited by
No citations on record.