[2510.24011] Understanding Reader Perception Shifts upon Disclosure of AI Authorship

Skip to main content

arXiv is now an independent nonprofit!<br>Learn more<br>&times;

Search arXiv

Press Enter to search &middot; Advanced search

-->

Computer Science > Human-Computer Interaction

arXiv:2510.24011 (cs)

[Submitted on 28 Oct 2025 (v1), last revised 22 Jan 2026 (this version, v2)]

Title:Understanding Reader Perception Shifts upon Disclosure of AI Authorship

Authors:Hiroki Nakano, Jo Takezawa, Fabrice Matulic, Chi-Lan Yang, Koji Yatani<br>View a PDF of the paper titled Understanding Reader Perception Shifts upon Disclosure of AI Authorship, by Hiroki Nakano and 4 other authors

View PDF<br>HTML (experimental)

Abstract:As AI writing support becomes ubiquitous, how disclosing its use affects reader perception remains a critical, underexplored question. We conducted a study with 261 participants to examine how revealing varying levels of AI involvement shifts author impressions across six distinct communicative acts. Our analysis of 990 responses shows that disclosure generally erodes perceptions of trustworthiness, caring, competence, and likability, with the sharpest declines in social and interpersonal writing. A thematic analysis of participants' feedback links these negative shifts to a perceived loss of human sincerity, diminished author effort, and the contextual inappropriateness of AI. Conversely, we find that higher AI literacy mitigates these negative perceptions, leading to greater tolerance or even appreciation for AI use. Our results highlight the nuanced social dynamics of AI-mediated authorship and inform design implications for creating transparent, context-sensitive writing systems that better preserve trust and authenticity.

Subjects:

Human-Computer Interaction (cs.HC)

Cite as:<br>arXiv:2510.24011 [cs.HC]

(or<br>arXiv:2510.24011v2 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2510.24011

Focus to learn more

arXiv-issued DOI via DataCite

Submission history<br>From: Hiroki Nakano [view email]<br>[v1]<br>Tue, 28 Oct 2025 02:34:52 UTC (1,438 KB)

[v2]<br>Thu, 22 Jan 2026 08:50:11 UTC (1,736 KB)

Full-text links:<br>Access Paper:

View a PDF of the paper titled Understanding Reader Perception Shifts upon Disclosure of AI Authorship, by Hiroki Nakano and 4 other authors<br>View PDF<br>HTML (experimental)<br>TeX Source

view license

Current browse context:

cs.HC

next >

new<br>recent<br>| 2025-10

Change to browse by:

cs

References & Citations

NASA ADS<br>Google Scholar

Semantic Scholar

export BibTeX citation<br>Loading...

BibTeX formatted citation

&times;

loading...

Data provided by:

Bookmark

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)

Related Papers

Recommenders and Search Tools

Link to Influence Flower

Influence Flower (What are Influence Flowers?)

Core recommender toggle

CORE Recommender (What is CORE?)

Author

Venue

Institution

Topic

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Which authors of this paper are endorsers? |<br>Disable MathJax (What is MathJax?)

Major funding support from