The study, published on February 9, 2026, centered on an online experiment in which adults in the United States were asked to use ChatGPT to evaluate climate-related claims. Rather than simply asking participants whether they trusted the chatbot, the researchers designed a two-track investigation. On one track, they measured how credible participants found both the original claims and the AI’s responses to them, using self-reported credibility judgments. On the other track, they applied computational text analysis to the actual conversation logs, dissecting the language of ChatGPT’s replies with a battery of language-model-based classifiers and link-level metadata.

That second track is what distinguishes the work from much of the earlier literature on AI and misinformation. Instead of treating a chatbot’s answer as an undifferentiated block of text, Tsang and Wang decomposed it into measurable linguistic properties: the valence, or emotional positivity, of the response; its formality; the degree of bias it expressed; how recent and current the information appeared; the presence of authority cues, such as references to experts or institutions; and its semantic richness, a measure of how much substantive, varied content the reply contained. Each of these dimensions was scored systematically across the corpus of conversations, allowing the team to test which features of AI language actually moved the needle on human perception.

The headline result is, in many ways, a humbling one for anyone who imagined that the mere authority of an AI system would sweep aside doubt. Credibility perceptions, the authors found, were driven primarily by individual differences among the people doing the asking. Participants who already accepted the scientific consensus on climate change rated both the claims and the chatbot’s responses as more credible. So did those who paid closer attention to climate news, those who had prior familiarity with ChatGPT, and, notably, younger participants. In other words, the audience arrived with predispositions, and those predispositions did much of the work in shaping what the conversation ultimately meant to them.

This pattern echoes a long-standing finding in communication research: people do not process information as blank slates. Motivated reasoning, selective attention, and prior knowledge all filter how a message lands. What is new here is that the same dynamics appear to govern encounters with a large language model, a technology often assumed to carry a kind of neutral, algorithmic authority. If a chatbot’s answer is filtered through the same psychological machinery as a cable news segment or a social media post, then the dream of AI as an automatic corrective to misinformation looks considerably more complicated.

Yet the study did find that the texture of ChatGPT’s language mattered, once individual differences were taken into account. Responses with positive valence and greater semantic richness were associated with higher perceived credibility. This suggests that when the chatbot offered answers that were substantively rich, varied, and framed in constructive rather than negative terms, participants found them more convincing. There is an intuitive logic to this: a reply that engages deeply with the substance of a claim, drawing on multiple facets of the evidence, gives a reader more to evaluate and more reason to feel informed, while a thin or dismissive reply may leave uncertainty unresolved.

The role of authority cues proved more surprising. When ChatGPT’s responses contained signals of authority, such as appeals to expert consensus or institutional sources, participants’ climate attitudes shifted slightly toward less extreme positions. The effect was modest, but its direction is intriguing. Rather than hardening views, the invocation of authoritative framing appeared to nudge people toward the middle ground. For a technology that is frequently criticized for hallucinating sources or overstating certainty, this finding hints that well-calibrated authority cues, deployed transparently, could play a constructive role in depolarizing contested topics.

The authors frame these results as support for a particular model of responsible climate communication: transparent, unbiased, and audience-tailored engagement. The emphasis on tailoring is significant. Because credibility judgments depended so heavily on who the user was, a one-size-fits-all chatbot response is unlikely to land the same way across a diverse public. A climate-attentive younger user with prior chatbot experience may find a detailed, evidence-dense reply persuasive, while a skeptical older user encountering the same text may remain unmoved. Designing conversational AI that adapts to these differences, without pandering or distorting the underlying science, emerges from the study as a central challenge for the field.

The implications extend beyond climate. Misinformation about vaccines, elections, and public health increasingly collides with AI assistants positioned as arbiters of fact. The PLOS Climate experiment offers a template for studying those collisions rigorously: capture the actual conversation logs, quantify their linguistic properties with computational classifiers, and link those properties to measured changes in human judgment. As large language models are embedded into search engines, messaging platforms, and voice assistants, this kind of evidence becomes essential for understanding not just what these systems say, but what their ways of saying it do to the people listening.

What the study ultimately delivers is a measured dose of both caution and optimism. Caution, because the chatbot did not override prior belief; the psychological characteristics of users remained the dominant force shaping credibility judgments, meaning AI alone cannot close the gap between scientific consensus and public understanding. Optimism, because specific, designable features of AI language, including positive framing, semantic depth, and calibrated authority cues, showed measurable associations with more credible and less extreme outcomes. The conversation between humans and machines about climate change, it turns out, is a genuine conversation, one whose outcome depends on both sides of the exchange, and one that communicators, developers, and researchers now have better tools to study and to shape.

Subject of Research: How large language model responses influence public credibility judgments of climate-related claims

Article Title: Countering climate misinformation with large language models: Evidence from ChatGPT

Article References: Tsang, S. J., & Wang, D. (2026). Countering climate misinformation with large language models: Evidence from ChatGPT. PLOS Climate, 5(9), e0000930. https://doi.org/10.1371/journal.pclm.0000930

Image Credits: AI Generated

DOI: 10.1371/journal.pclm.0000930

Keywords: ChatGPT, climate misinformation, large language models, PLOS Climate, science communication, credibility, public opinion, computational text analysis, climate attitudes, AI chatbots, misinformation, audience effects