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.

Empirical 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.

Frames a measurable concept for tracking how AI use distorts self-perception of contribution. Useful background for those designing writing workflows or AI-use policies.

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