Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

The promise and limitations of using GenAI to reduce climate scepticism

Nature Climate Change volume 15, pages 1183–1189 (2025)Cite this article

Subjects

Abstract

Large language models (LLMs) can support climate literacy by offering accessible, multilingual and personalized climate information. However, the effectiveness of these new communication tools remains unexplored. Here we conducted two studies to examine whether dialogues with LLMs can effectively reduce climate scepticism. In study 1 (n = 949), climate sceptics who engaged in short conversations with ChatGPT showed small but significant increases in pro-environmental action intentions and decreased confidence in their initial sceptical views. In study 2 (n = 333), similar effects were observed and extended to modest reductions in climate scepticism and policy support. It is noteworthy that the effects on reducing scepticism among sceptics are inconsistent across two studies and susceptible to decay over time. Increasing the conversation length from three to six rounds yielded no additional benefit. These mixed findings underscore the need for cautious, evidence-based integration of LLMs into climate communication strategies.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Example conversation between participant and ChatGPT.
Fig. 2: Effects of the two informational interventions on sceptics (n = 207) and believers (non-sceptics; n = 742).
Fig. 3: Effects of three-round and six-round conversations with ChatGPT on the views of climate sceptics.

Similar content being viewed by others

Data availability

The data files are available on the Open Science Framework: https://osf.io/mtv39/.

Code availability

The codes underpinning the analyses are available on the Open Science Framework: https://osf.io/mtv39/.

References

  1. Whitmarsh, L. Scepticism and uncertainty about climate change: dimensions, determinants and change over time. Glob. Environ. Change 21, 690–700 (2011).

    Article  Google Scholar 

  2. Capstick, S. B. & Pidgeon, N. F. What is climate change scepticism? Examination of the concept using a mixed methods study of the UK public. Glob. Environ. Change 24, 389–401 (2014).

    Article  Google Scholar 

  3. Ding, D., Maibach, E. W., Zhao, X., Roser-Renouf, C. & Leiserowitz, A. Support for climate policy and societal action are linked to perceptions about scientific agreement. Nat. Clim. Change 1, 462–466 (2011).

    Article  Google Scholar 

  4. Lewandowsky, S., Risbey, J. S. & Oreskes, N. The “pause” in global warming: turning a routine fluctuation into a problem for science. Bull. Am. Meteorol. Soc. 97, 723–733 (2016).

    Article  Google Scholar 

  5. Hornsey, M. J., Chapman, C. M. & Humphrey, J. E. Climate skepticism decreases when the planet gets hotter and conservative support wanes. Glob. Environ. Change 74, 102492 (2022).

    Article  Google Scholar 

  6. Leiserowitz, A. et al. International Public Opinion on Climate Change (Yale Program on Climate Change Communication and Facebook Data for Good, 2021).

  7. Tyson, A., Funk, C. & Kennedy, B. What the Data says About Americans’ Views of Climate Change (Pew Research Center, 2023).

  8. Winter, K., Hornsey, M. J., Pummerer, L. & Sassenberg, K. Public agreement with misinformation about wind farms. Nat. Commun. 15, 8888 (2024).

    Article  CAS  Google Scholar 

  9. Lamb, W. F. et al. Discourses of climate delay. Glob. Sustain. 3, e17 (2020).

    Article  Google Scholar 

  10. Hart, P. S. & Nisbet, E. C. Boomerang effects in science communication: how motivated reasoning and identity cues amplify opinion polarization about climate mitigation policies. Commun. Res. 39, 701–723 (2012).

    Article  Google Scholar 

  11. Bernauer, T. & McGrath, L. F. Simple reframing unlikely to boost public support for climate policy. Nat. Clim. Change 6, 680–683 (2016).

    Article  Google Scholar 

  12. Hamilton, L. C. Education, politics and opinions about climate change evidence for interaction effects. Clim. Change 104, 231–242 (2011).

    Article  Google Scholar 

  13. Drummond, C. & Fischhoff, B. Individuals with greater science literacy and education have more polarized beliefs on controversial science topics. Proc. Natl Acad. Sci. USA 114, 9587–9592 (2017).

    Article  CAS  Google Scholar 

  14. Fischer, H., Huff, M. & Said, N. Polarized climate change beliefs: no evidence for science literacy driving motivated reasoning in a U.S. national study. Am. Psychol. 77, 822–835 (2022).

    Article  Google Scholar 

  15. Hornsey, M. J. The role of worldviews in shaping how people appraise climate change. Curr. Opin. Behav. Sci. 42, 36–41 (2021).

    Article  Google Scholar 

  16. van der Linden, S., Leiserowitz, A., Rosenthal, S. & Maibach, E. Inoculating the public against misinformation about climate change. Glob. Chall. 1, 1600008 (2017).

    Article  Google Scholar 

  17. Kahan, D. M. Fixing the communications failure. Nature 463, 296–297 (2010).

    Article  CAS  Google Scholar 

  18. Bretter, C. & Schulz, F. Why focusing on “climate change denial” is counterproductive. Proc. Natl Acad. Sci. USA 120, e2217716120 (2023).

    Article  CAS  Google Scholar 

  19. Carmichael, J. T. & Brulle, R. J. Elite cues, media coverage, and public concern: an integrated path analysis of public opinion on climate change, 2001–2013. Environ. Politics 26, 232–252 (2017).

    Article  Google Scholar 

  20. Antonio, R. J. & Brulle, R. J. The unbearable lightness of politics: climate change denial and political polarization. Sociol. Q. 52, 195–202 (2011).

    Article  Google Scholar 

  21. Druckman, J. N. & McGrath, M. C. The evidence for motivated reasoning in climate change preference formation. Nat. Clim. Change 9, 111–119 (2019).

    Article  Google Scholar 

  22. Hornsey, M. J., Bierwiaczonek, K., Sassenberg, K. & Douglas, K. M. Individual, intergroup and nation-level influences on belief in conspiracy theories. Nat. Rev. Psychol. 2, 85–97 (2023).

    Article  Google Scholar 

  23. Atkins, C., Girgente, G., Shirzaei, M. & Kim, J. Generative AI tools can enhance climate literacy but must be checked for biases and inaccuracies. Commun. Earth Environ. 5, 226 (2024).

    Article  Google Scholar 

  24. Vaghefi, S. A. et al. ChatClimate: grounding conversational AI in climate science. Commun. Earth Environ. 4, 480 (2023).

    Article  Google Scholar 

  25. Nguyen, H., Victoria, N., Sara, L. & and Santagata, R. Misrepresentation or inclusion: promises of generative artificial intelligence in climate change education. Learn. Media Technol. https://doi.org/10.1080/17439884.2024.2435834 (2024).

  26. van der Ven, H. et al. Does artificial intelligence bias perceptions of environmental challenges? Environ. Res. Lett. 20, 014009 (2024).

  27. Costello, T. H., Pennycook, G. & Rand, D. G. Durably reducing conspiracy beliefs through dialogues with AI. Science 385, eadq1814 (2024).

    Article  CAS  Google Scholar 

  28. O’Mahony, C., Brassil, M., Murphy, G. & Linehan, C. The efficacy of interventions in reducing belief in conspiracy theories: a systematic review. PLoS ONE 18, e0280902 (2023).

    Article  Google Scholar 

  29. Bretter, C. et al. Mapping, understanding and reducing belief in misinformation about electric vehicles. Nat. Energy 10, 869–879 (2025).

    Article  Google Scholar 

  30. DeVerna, M. R., Yan, H. Y., Yang, K.-C. & Menczer, F. Fact-checking information from large language models can decrease headline discernment. Proc. Natl Acad. Sci. USA 121, e2322823121 (2024).

    Article  CAS  Google Scholar 

  31. Hornsey, M. J. Why facts are not enough: understanding and managing the motivated rejection of science. Curr. Dir. Psychol. Sci. 29, 583–591 (2020).

    Article  Google Scholar 

  32. Hornsey, M. J., Fielding, K. S., Marshall, G. & Louis, W. R. Intergroup conflict over climate change: problems and solutions. Eur. J. Soc. Psychol. 55, 243–250 (2025).

    Article  Google Scholar 

  33. Yan, L., Greiff, S., Teuber, Z. & Gašević, D. Promises and challenges of generative artificial intelligence for human learning. Nat. Hum. Behav. 8, 1839–1850 (2024).

    Article  Google Scholar 

  34. Goos, M. & Savona, M. The governance of artificial intelligence: harnessing opportunities and mitigating challenges. Res. Policy 53, 104928 (2024).

    Article  Google Scholar 

  35. Alemohammad, S. et al. Self-consuming generative models go MAD. In ICLR Conference Proceedings (ed. Kim, B. et al.) (ICLR, 2024).

  36. Shumailov, I. et al. AI models collapse when trained on recursively generated data. Nature 631, 755–759 (2024).

    Article  CAS  Google Scholar 

  37. Brundage, M. et al. The malicious use of artificial intelligence: forecasting, prevention, and mitigation. Preprint at https://doi.org/10.48550/arXiv.1802.07228 (2018).

  38. Kemene, E., Valkhof, B. & Tladi, T. AI and Energy: Will AI Help Reduce Emissions or Increase Demand? Here’s What to Know (World Economic Forum, 2024).

  39. Sharma, N., Liao, Q. V. & Xiao, Z. Generative echo chamber? Effect of LLM-powered search systems on diverse information seeking. Proc. CHI Conf. Hum. Factors Comput. Syst. 2, 1033 (2024).

    Google Scholar 

  40. de Graaf, J. A., Stok, F. M., de Wit, J. B. F. & Bal, M. The climate change skepticism questionnaire: validation of a measure to assess doubts regarding climate change. J. Environ. Psychol. 89, 102068 (2023).

    Article  Google Scholar 

  41. Hornsey, M. J., Chapman, C. M. & Oelrichs, D. M. Ripple effects: can information about the collective impact of individual actions boost perceived efficacy about climate change? J. Exp. Soc. Psychol. 97, 104217 (2021).

    Article  Google Scholar 

Download references

Acknowledgements

We thank S. Read and N. Taylor for their work fact-checking the ChatGPT statements. This research was supported by an ARC Laureate grant awarded to M.J.H. (FL230100022).

Author information

Authors and Affiliations

  1. The University of Queensland, St Lucia, Queensland, Australia

    Matthew J. Hornsey, Samuel Pearson, Christian Bretter, Sarah MacInnes, Jarren L. Nylund & Saphira Rekker

Authors
  1. Matthew J. Hornsey
    View author publications

    Search author on:PubMed Google Scholar

  2. Samuel Pearson
    View author publications

    Search author on:PubMed Google Scholar

  3. Christian Bretter
    View author publications

    Search author on:PubMed Google Scholar

  4. Sarah MacInnes
    View author publications

    Search author on:PubMed Google Scholar

  5. Jarren L. Nylund
    View author publications

    Search author on:PubMed Google Scholar

  6. Saphira Rekker
    View author publications

    Search author on:PubMed Google Scholar

Contributions

M.J.H. led the conceptualization and validation of the study, prepared the original draft, supervised the work and secured funding. S.P. and C.B. developed the methodology, conducted the formal analyses, curated the data and contributed to the review and editing of the paper, with both also preparing visualizations. S.M. carried out coding, managed aspects of the project and contributed to review and editing of the paper. J.L.N. contributed to coding, visualization and review and editing of the paper. S.R. contributed to coding and to the review and editing of the paper.

Corresponding author

Correspondence to Matthew J. Hornsey.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Climate Change thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Table 1 Study 1 full ANOVA results
Full size table
Extended Data Table 2 Study 1 pairwise comparisons across time
Full size table
Extended Data Table 3 Study 2 full ANOVA results for 2 ×3 ANOVAs
Full size table
Extended Data Table 4 Study 2 pairwise comparisons across time
Full size table
Extended Data Table 5 Study 2 full ANOVA results for 2 ×2 ANOVAs
Full size table

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2, Tables 1–7 and Note.

Reporting Summary

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted paper version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hornsey, M.J., Pearson, S., Bretter, C. et al. The promise and limitations of using GenAI to reduce climate scepticism. Nat. Clim. Chang. 15, 1183–1189 (2025). https://doi.org/10.1038/s41558-025-02425-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41558-025-02425-8

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing