Understanding Reader Perception Shifts upon Disclosure of AI Authorship
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918299261992960 |
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| author | Nakano, Hiroki Takezawa, Jo Matulic, Fabrice Yang, Chi-Lan Yatani, Koji |
| author_facet | Nakano, Hiroki Takezawa, Jo Matulic, Fabrice Yang, Chi-Lan Yatani, Koji |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_24011 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Understanding Reader Perception Shifts upon Disclosure of AI Authorship Nakano, Hiroki Takezawa, Jo Matulic, Fabrice Yang, Chi-Lan Yatani, Koji Human-Computer Interaction 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. |
| title | Understanding Reader Perception Shifts upon Disclosure of AI Authorship |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2510.24011 |