American study: 36% of participants used artificial intelligence to choose Senate candidates

A study conducted by researchers from Yale University revealed that 36 percent of participants in an experiment using chatbots asked to help them choose candidates to vote in the US congressional midterm elections, while the results showed differences between the models in their willingness to provide electoral recommendations, especially after learning about the users’ opinions and political priorities.

The study included about 2,500 people from 35 US states that witnessed Senate races, and was conducted within weeks of October 2026, prior to the elections that took place on November 3. It was attended by adults who use chatbots at least once a week, through 4 systems: “ChatGPT”, “Cloud”, “Grok”, and “Gemini”.

The study showed that most participants used chatbots to obtain basic information related to the elections, including the names of the candidates running in their states, but a portion of them went beyond informational inquiries to asking for help in identifying the most appropriate candidate to vote for.

Some users resorted to explaining their priorities and positions on various issues, then asked the chatbot to compare these positions with the candidates’ programs, and help them determine the extent of compatibility between them.

The researchers observed a difference in the responses of the four systems when they were asked to provide electoral recommendations. In conversations in which the user asked to choose a candidate without providing information about his or her personal opinions, Grok was the only model to provide recommendations on a relatively regular basis, in 14 percent of the conversations included in this case.

But the results differed after users shared their political positions with the systems. In these cases, ChatGPT recommended a candidate in 79 percent of the conversations, compared to 40 percent for Cloud, while the results of Grok and Gemini fell between these two percentages.

One of the aspects that the researchers focused on is the effect of the sequence of questions and answers within a single conversation on the nature of the recommendations provided by the system. Instead of just asking a single question, multi-stage conversations allow the model to ask additional questions, obtain information about the user, and then adjust its responses based on the answers it receives.

A recent scientific study showed that human participants outperformed artificial intelligence models in distinguishing between human and machine texts during an experiment based on personal descriptions written in German, despite the models’ ability to produce linguistic patterns that reflect specific personality traits and infer some of them from short texts. The average accuracy of humans in identifying the source of texts was 83.14 percent, compared to 66.90 percent for the language models participating in the evaluation.

The study was conducted by researchers from the Department of Psychology at Johannes Gutenberg University in Mainz, Germany, and was published in the journal Scientific Reports under the title “Expression and Perception of Personality among Large Linguistic Models and Humans.”