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Evaluating generative AI’s potential to dispel misinformation about wind farms
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  • Published: 13 March 2026

Evaluating generative AI’s potential to dispel misinformation about wind farms

  • Samuel Pearson1,
  • Matthew J. Hornsey1,
  • Christian Bretter1,
  • Sarah MacInnes1,
  • Jarren L. Nylund1,
  • Saphira Rekker1,
  • Aimee E. Smith1 &
  • …
  • Belinda Wade1 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Environmental social sciences
  • Science, technology and society

Abstract

Public misinformation about wind farms threatens the global transition to net-zero and a more environmentally sustainable future. This study examines whether conversations with Generative AI (GenAI) can effectively address misinformation and improve attitudes and beliefs about wind farms. In two pre-registered experiments (collective N = 2405), participants with anti-wind farm beliefs engaged in three-round dialogues with ChatGPT, a widely used GenAI tool. Fact-checking showed no clear cases of the GenAI introducing misinformation. Furthermore, following GenAI conversations, participants displayed reduced agreement with misinformation about wind farms, increased policy support, and reduced confidence in their anti-wind farm views. However, some of these effects decayed over time and were not always more effective than static informational resources. These findings highlight both the potential and limitations of GenAI in combating sustainability misinformation, offering insights for leveraging AI in public communication strategies.

Data availability

The datasets generated and analysed during the current study are available in the OSF repository, [https://osf.io/e8m26/?view\_only=1f6ae3d63e1a41349d3a4bf9cba6fe49](https:/osf.io/e8m26/?view_only=1f6ae3d63e1a41349d3a4bf9cba6fe49).

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Funding

acknowledgement.

This research was supported by an ARC Laureate grant awarded to MJH (FL230100022).

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Authors and Affiliations

  1. The University of Queensland, 39 Blair Dr, St Lucia, Queensland, QLD, 4072, Australia

    Samuel Pearson, Matthew J. Hornsey, Christian Bretter, Sarah MacInnes, Jarren L. Nylund, Saphira Rekker, Aimee E. Smith & Belinda Wade

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Contributions

S.P., M.J.H.,C.B., S.M., A.S. designed the studies. S.P. and M.J.H. conducted the analyses. S.P. and M.J.H. wrote the paper, and C.B., S.M., A.S., S.R., B.W. and J.N. provided support in revising the paper.

Corresponding author

Correspondence to Samuel Pearson.

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The authors declare no competing interests.

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Pearson, S., Hornsey, M.J., Bretter, C. et al. Evaluating generative AI’s potential to dispel misinformation about wind farms. Sci Rep (2026). https://doi.org/10.1038/s41598-026-42790-8

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  • Received: 20 December 2025

  • Accepted: 27 February 2026

  • Published: 13 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-42790-8

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