Humanly: A Configurable and Traceable Environment for Human-AI Collaborative Writing
Teachers, conference chairs, and public readers judge writing from limited evidence, seeing only a finished document. Humanly is a writing platform that records the entire writing process and generates a sealed writing certificate with configurable anomaly review, supporting scenarios like assignments, peer review, and certification. User studies show its utility across roles, and a red-teaming study confirms its typing detector distinguishes human from automated typing.
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[Submitted on 23 Jul 2026]
Title:Humanly: A Configurable and Traceable Environment for Human-AI Collaborative Writing
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Abstract:Teachers, conference chairs, and public readers all judge writing from limited evidence, seeing only a finished document and not the process that produced it. Final text alone cannot reveal whether a document was produced through human typing, AI generation, or mixed human-AI collaboration. Existing process-tracking tools help, but many are tied to host-document histories, provide coarse activity records, and offer limited control over the writing environment. Humanly is a writing platform that makes the writing process itself the evidence. Users configure writing environments for personal documents or assigned tasks and draft in a workspace that records writing activity and in-platform AI assistance. Humanly can package a completed session into a sealed writing certificate with configuration-aware anomaly behavior review. It can support writing scenarios such as course assignments, peer review, and personal certification. Our user study shows that Humanly is helpful across roles, and a red-teaming study shows that the Humanly Typing Detector distinguishes human hand typing from automated typing.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2607.21758 [cs.CL]
(or arXiv:2607.21758v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.21758
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Shenzhe Zhu [view email] [v1] Thu, 23 Jul 2026 19:20:03 UTC (1,792 KB)
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