How Does the Disclosure of AI Assistance Affect the Perceptions of Writing?
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Abstract (arXiv)
Recent advances in generative AI technologies like large language models have boosted the incorporation of AI assistance in writing workflows, leading to the rise of a new paradigm of human-AI co-creation in writing. To understand how people perceive writings that are produced under this paradigm, in this paper, we conduct an experimental study to understand whether and how the disclosure of the level and type of AI assistance in the writing process would affect people's perceptions of the writing on various aspects, including their evaluation on the quality of the writing and their ranking of different writings. Our results suggest that disclosing the AI assistance in the writing process, especially if AI has provided assistance in generating new content, decreases the average quality ratings for both argumentative essays and creative stories. This decrease in the average quality ratings often comes with an increased level of variations in different individuals' quality evaluations of the same writing. Indeed, factors such as an individual's writing confidence and familiarity with AI writing assistants are shown to moderate the impact of AI assistance disclosure on their writing quality evaluations. We also find that disclosing the use of AI assistance may significantly reduce the proportion of writings produced with AI's content generation assistance among the top-ranked writings.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 15 of its 36 references that are in the map. First posted on arXiv 6 Oct 2024.
Builds on
15 of 36 references.
References · 15
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2021All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Textcited by 287
- 2019Automatic Detection of Generated Text is Easiest when Humans are Fooledcited by 260
- 2022CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilitiescited by 245
- 2023PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traitscited by 229
- 2022Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionalscited by 191
- 2023Co-Writing with Opinionated Language Models Affects Users' Viewscited by 145
- 2021Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Textcited by 89
- 2024A Design Space for Intelligent and Interactive Writing Assistantscited by 76
- 2021The Impact of Multiple Parallel Phrase Suggestions on Email Input and Composition Behaviour of Native and Non-Native English Writerscited by 61
- 2023VISAR: A Human-AI Argumentative Writing Assistant with Visual Programming and Rapid Draft Prototypingcited by 46
- 2024The Value, Benefits, and Concerns of Generative AI-Powered Assistance in Writingcited by 37
3 more
- 2023Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Promptingcited by 36
- 2024LLMs as Writing Assistants: Exploring Perspectives on Sense of Ownership and Reasoningcited by 12
- 2023Comparing Sentence-Level Suggestions to Message-Level Suggestions in AI-Mediated Communicationcited by 9
Built on by
Citing papers per year: 2025: 4; 2026: 8.
12 papers.
- 2025From Text to Trust: Empowering AI-assisted Decision Making with Adaptive LLM-powered Analysiscited by 9
- 2026Can Good Writing Be Generative? Expert-Level AI Writing Emerges through Fine-Tuning on High-Quality Bookscited by 9
- 2025Readers Prefer Outputs of AI Trained on Copyrighted Books over Expert Human Writerscited by 8
- 2025A Multi-Task Evaluation of LLMs' Processing of Academic Text Inputcited by 5
- 2026"If You're Very Clever, No One Knows You've Used It": The Social Dynamics of Developing Generative AI Literacy in the Workplacecited by 2
- 2025Accepted with Minor Revisions: Value of AI-Assisted Scientific Writingcited by 1
- 2026From Use to Oversight: How Mental Models Influence User Behavior and Output in AI Writing Assistantscited by 1
- 2026Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMscited by 1
- 2026Effects of Varying LLM Access on Essay Writing Behaviorcited by 1
- 2026LLM or Human? Perceptions of Trust and Information Quality in Research Summariescited by 0
- 2026Human-AI Interaction Traces as Blackout Poetry: Reframing AI-Supported Writing as Found-Text Creativitycited by 0
- 2026AI-Generated Slides: Are They Good? Can Students Tell?cited by 0
- 2026Effects of Varying LLM Access on Essay Writing Behavior29 May 2026
- 2026Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs28 May 2026
- 2026AI-Generated Slides: Are They Good? Can Students Tell?13 May 2026
- 2026From Use to Oversight: How Mental Models Influence User Behavior and Output in AI Writing Assistants6 Apr 2026
- 2026Human-AI Interaction Traces as Blackout Poetry: Reframing AI-Supported Writing as Found-Text Creativity10 Mar 2026
- 2026"If You're Very Clever, No One Knows You've Used It": The Social Dynamics of Developing Generative AI Literacy in the Workplace1 Feb 2026
- 2026Can Good Writing Be Generative? Expert-Level AI Writing Emerges through Fine-Tuning on High-Quality Books26 Jan 2026
- 2026LLM or Human? Perceptions of Trust and Information Quality in Research Summaries22 Jan 2026
- 2025Accepted with Minor Revisions: Value of AI-Assisted Scientific Writing16 Nov 2025
- 2025Readers Prefer Outputs of AI Trained on Copyrighted Books over Expert Human Writers15 Oct 2025
- 2025A Multi-Task Evaluation of LLMs' Processing of Academic Text Input15 Aug 2025
- 2025From Text to Trust: Empowering AI-assisted Decision Making with Adaptive LLM-powered Analysis17 Feb 2025