AI chatbots can persuade voters better than political ads, but the most persuasive ones spread the most misinformation

A multi-university study, published in two papers in the journals Nature and Science, concludes that a single conversation with a politically biased AI chatbot can change voters' opinions. In the experiments, the effect was larger than that measured for political ads in previous campaigns. The MIT Technology Review article, written by Michelle Kim and published on December 4, 2025, reports the findings along with the researchers' own caveats.

The piece opens with a real example from 2024. Shamaine Daniels, a Democratic congressional candidate in Pennsylvania, used a chatbot called Ashley to call voters and talk with them. The calls began with Ashley introducing itself as an "artificial intelligence volunteer" for her campaign. Daniels did not win, but the research suggests that calls of this kind could have a real effect.

The Nature study recruited more than 2,300 participants. Each one had a conversation with a chatbot two months before the 2024 US presidential election. The chatbot was trained to advocate for one of the two main candidates and proved fairly persuasive, especially when it discussed policy platforms on topics such as the economy and health care.

Donald Trump supporters who talked with a model favoring Kamala Harris moved 3.9 points toward her on a 100-point scale. According to the article, that is roughly four times the measured effect of political ads in the 2016 and 2020 elections. The pro-Trump model moved Harris supporters 2.3 points toward him.

In similar experiments run ahead of Canada's 2025 federal election and Poland's 2025 presidential election, the effect was even larger. The chatbots shifted opposition voters' attitudes by about 10 points.

Classic theories of politically motivated reasoning hold that voters with strong partisan loyalties are impervious to facts that contradict their beliefs. The researchers found the opposite. The chatbots, which ran on several models including variants of GPT and DeepSeek, were more persuasive when told to use facts and evidence than when told not to. Thomas Costello, a psychologist at American University, says people update their views based on the facts and information the model gives them. Gordon Pennycook, a psychologist at Cornell University, explains that language models are more persuasive than advertising because they generate far more information in real time and deploy it strategically in the conversation.

The catch is that some of that "evidence" was false. In all three countries, chatbots advocating for right-leaning candidates made more inaccurate claims than those advocating for left-leaning ones. Costello attributes this to the fact that models are trained on vast amounts of human-written text and reproduce real-world phenomena. These include right-wing political communication, which studies of partisan social media posts have found tends to be less accurate.

The Science study, produced by a partly overlapping team, examined what makes these chatbots so persuasive. The researchers deployed 19 language models that interacted with nearly 77,000 participants in the UK on more than 700 political issues. They varied factors such as computing power, training techniques and rhetorical strategies.

The most effective way to make the models persuasive was to instruct them to pack their arguments with facts and evidence, and then give them additional training on examples of persuasive conversations. The most persuasive model shifted participants who initially disagreed with a political statement 26.1 points toward agreement. Kobi Hackenburg, a researcher at the UK AI Security Institute, describes these effects as very large.

Optimizing for persuasion came at a cost to truthfulness. The more persuasive the models became, the more misleading or false information they provided, and no one knows for sure why. Hackenburg offers a hypothesis: perhaps, as the models learn to deploy more facts, they reach the bottom of what they know and the quality of the facts deteriorates.

The authors warn of potentially profound consequences for democracy. Campaigns using chatbots could shape public opinion in ways that undermine voters' ability to form independent political judgments.

Still, the real extent of the impact remains to be seen. Andy Guess, a political scientist at Princeton, notes that it is unclear what future campaigns will look like or how they will incorporate these technologies. Competing for voters' attention is expensive and difficult, and getting them to engage in long political conversations with chatbots may be hard. He wonders whether this will become the usual way people learn about politics or remain a niche activity.

It is also unclear whether chatbots will amplify truth or fiction more. Alex Coppock, a political scientist at Northwestern, observes that misinformation often has an informational advantage in a campaign, so election chatbots could spell "a disaster." But it is also possible that accurate information becomes scalable. If everyone has their chatbots running, he asks, will we just persuade ourselves to a draw?

There are reasons to doubt that balance. Politicians' access to the most persuasive models may not be evenly distributed, and voters across the political spectrum may use chatbots with differing intensity. Guess points out that if one candidate's or party's supporters are more tech-savvy than the other's, the persuasive effects might not cancel out.

Finally, as people turn to AI to navigate their lives, they may start asking chatbots for voting advice even without any campaign prompting it. The article sees this scenario as a risk to democracy in the absence of robust safeguards. As a first step, it proposes auditing and documenting the accuracy of model responses in conversations about politics.