The Great AI Witch Hunt: Reviewers Perception and (Mis)Conception of Generative AI in Research Writing
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Abstract (arXiv)
Generative AI (GenAI) use in research writing is growing fast. However, it is unclear how peer reviewers recognize or misjudge AI-augmented manuscripts. To investigate the impact of AI-augmented writing on peer reviews, we conducted a snippet-based online survey with 17 peer reviewers from top-tier HCI conferences. Our findings indicate that while AI-augmented writing improves readability, language diversity, and informativeness, it often lacks research details and reflective insights from authors. Reviewers consistently struggled to distinguish between human and AI-augmented writing but their judgements remained consistent. They noted the loss of a "human touch" and subjective expressions in AI-augmented writing. Based on our findings, we advocate for reviewer guidelines that promote impartial evaluations of submissions, regardless of any personal biases towards GenAI. The quality of the research itself should remain a priority in reviews, regardless of any preconceived notions about the tools used to create it. We emphasize that researchers must maintain their authorship and control over the writing process, even when using GenAI's assistance.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 16 of its 91 references that are in the map. First posted on arXiv 27 Jun 2024.
Builds on
16 of 91 references.
References · 16
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4 more
- 2024The Dearth of the Author in AI-Supported Writingcited by 9
- 2024Sora OpenAI's Prelude: Social Media Perspectives on Sora OpenAI and the Future of AI Video Generationcited by 8
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Built on by
Citing papers per year: 2025: 2; 2026: 4.
6 papers.
- 2026What happens when reviewers receive AI feedback in their reviews?cited by 1
- 2026The Emerging AI Paper-Review Arms Race: Adversarial Co-Evolution in Scholarly Publishingcited by 1
- 2025Who is Responsible When AI Fails? Mapping Causes, Entities, and Consequences of AI Privacy and Ethical Incidentscited by 0
- 2025AI-Generated Content in Cross-Domain Applications: Research Trends, Challenges and Propositionscited by 0
- 2026LLM or Human? Perceptions of Trust and Information Quality in Research Summariescited by 0
- 2026Magic, Madness, Heaven, Sin: LLM Output Diversity is Everything, Everywhere, All at Oncecited by 0
- 2026The Emerging AI Paper-Review Arms Race: Adversarial Co-Evolution in Scholarly Publishing7 Sep 2026
- 2026Magic, Madness, Heaven, Sin: LLM Output Diversity is Everything, Everywhere, All at Once2 Apr 2026
- 2026What happens when reviewers receive AI feedback in their reviews?14 Feb 2026
- 2026LLM or Human? Perceptions of Trust and Information Quality in Research Summaries22 Jan 2026
- 2025AI-Generated Content in Cross-Domain Applications: Research Trends, Challenges and Propositions14 Sep 2025
- 2025Who is Responsible When AI Fails? Mapping Causes, Entities, and Consequences of AI Privacy and Ethical Incidents28 Mar 2025