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Perceptions and detection of AI use in manuscript preparation for academic journals

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  • Nir Chemaya
  • Daniel Martin

Abstract

The rapid advances in Generative AI tools have produced both excitement and worry about how AI will impact academic writing. However, little is known about what norms are emerging around AI use in manuscript preparation or how these norms might be enforced. We address both gaps in the literature by conducting a survey of 271 academics about whether it is necessary to report ChatGPT use in manuscript preparation and by running GPT-modified abstracts from 2,716 published papers through a leading AI detection software to see if these detectors can detect different AI uses in manuscript preparation. We find that most academics do not think that using ChatGPT to fix grammar needs to be reported, but detection software did not always draw this distinction, as abstracts for which GPT was used to fix grammar were often flagged as having a high chance of being written by AI. We also find disagreements among academics on whether more substantial use of ChatGPT to rewrite text needs to be reported, and these differences were related to perceptions of ethics, academic role, and English language background. Finally, we found little difference in their perceptions about reporting ChatGPT and research assistant help, but significant differences in reporting perceptions between these sources of assistance and paid proofreading and other AI assistant tools (Grammarly and Word). Our results suggest that there might be challenges in getting authors to report AI use in manuscript preparation because (i) there is not uniform agreement about what uses of AI should be reported and (ii) journals might have trouble enforcing nuanced reporting requirements using AI detection tools.

Suggested Citation

  • Nir Chemaya & Daniel Martin, 2024. "Perceptions and detection of AI use in manuscript preparation for academic journals," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-16, July.
    • Handle: RePEc:plo:pone00:0304807
    DOI: 10.1371/journal.pone.0304807
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    as
    1. Bjorkegren, Dan & Blumenstock, Joshua & Knight, Samsun, 2022. "(Machine) Learning What Policies Value," CEPR Discussion Papers 17364, C.E.P.R. Discussion Papers.
    2. Harjit Singh & Avneet Singh, 2023. "ChatGPT: Systematic Review, Applications, and Agenda for Multidisciplinary Research," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 21(2), pages 193-212, April.
    3. Drew Fudenberg & Annie Liang, 2019. "Predicting and Understanding Initial Play," American Economic Review, American Economic Association, vol. 109(12), pages 4112-4141, December.
    4. Anja Lambrecht & Catherine Tucker, 2019. "Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads," Management Science, INFORMS, vol. 65(7), pages 2966-2981, July.
    5. David Dranove & Ginger Zhe Jin, 2010. "Quality Disclosure and Certification: Theory and Practice," Journal of Economic Literature, American Economic Association, vol. 48(4), pages 935-963, December.
    6. Korinek, Anton, 2023. "Language Models and Cognitive Automation for Economic Research," CEPR Discussion Papers 17923, C.E.P.R. Discussion Papers.
    7. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "The Economics of Artificial Intelligence: An Agenda," NBER Books, National Bureau of Economic Research, Inc, number agra-1, July.
    8. Chen-Fu Chien & Stéphane Dauzère-Pérès & Woonghee Tim Huh & Young Jae Jang & James R. Morrison, 2020. "Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies," International Journal of Production Research, Taylor & Francis Journals, vol. 58(9), pages 2730-2731, May.
    9. John J. Horton, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," NBER Working Papers 31122, National Bureau of Economic Research, Inc.
    10. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    11. John J. Horton, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," Papers 2301.07543, arXiv.org.
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    1. Li, Munan & Wang, Liang, 2025. "Leveraging patent classification based on deep learning: The case study on smart cities and industrial Internet of Things," Journal of Informetrics, Elsevier, vol. 19(1).

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