mlsanityData to 7 Oct 2026

How Does the Disclosure of AI Assistance Affect the Perceptions of Writing?

arXiv 2410.04545 ↗First posted 6 Oct 2024cs.CLcited by 12

Not on the map. Why?

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

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2021All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Textcited by 287
  3. 2019Automatic Detection of Generated Text is Easiest when Humans are Fooledcited by 260
  4. 2022CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilitiescited by 245
  5. 2023PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traitscited by 229
  6. 2022Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionalscited by 191
  7. 2023Co-Writing with Opinionated Language Models Affects Users' Viewscited by 145
  8. 2021Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Textcited by 89
  9. 2024A Design Space for Intelligent and Interactive Writing Assistantscited by 76
  10. 2021The Impact of Multiple Parallel Phrase Suggestions on Email Input and Composition Behaviour of Native and Non-Native English Writerscited by 61
  11. 2023VISAR: A Human-AI Argumentative Writing Assistant with Visual Programming and Rapid Draft Prototypingcited by 46
  12. 2024The Value, Benefits, and Concerns of Generative AI-Powered Assistance in Writingcited by 37
3 more
  1. 2023Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Promptingcited by 36
  2. 2024LLMs as Writing Assistants: Exploring Perspectives on Sense of Ownership and Reasoningcited by 12
  3. 2023Comparing Sentence-Level Suggestions to Message-Level Suggestions in AI-Mediated Communicationcited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 8.

12 papers.