SearcharxivSearch

arXiv subjects

Benjamin Lira

Publications and source records attributed to Benjamin Lira.

2 recordsLinked to original sources

Synthetic Contact with AI Reduces Cross-Partisan Animosity

Americans' warmth toward members of the opposing political party has fallen sharply over the past three decades -- yet meaningful cross-partisan contact remains scarce, in part because people actively avoid it. Across five preregistered studies (total N = 3,960 U.S. partisans), we test whether brief conversations with AI chatbots representing the political outgroup can substitute for the contact people shun. Synthetic contact first lowers the barrier to entry: partisans would endure almost twice as long contemplating their own mortality to avoid a human outgroup partner as an AI one. These conversations then correct the misperceptions that fuel division. At baseline, Democrats placed Republicans more than a standard deviation past their actual position on environmental consumption attitudes -- enough to flip the average Republican from supportive to opposed -- and a single ten-minute conversation with an outgroup chatbot corrected those beliefs and warmed affect in a within-person study of both parties. A three-arm experiment ruled out pure engagement and sociality as drivers. Synthetic contact also moved behavior, in a sample of both parties and on a more affectively charged issue: participants who spoke with an outgroup bot about immigration were six percentage points more likely than controls to choose to have a real conversation with a partisan from the other side. A final study tested whether these gains last: the warmth effect replicated immediately in a new sample; most of it faded within a week, with a small residual concentrated among the most extreme partisans. Analyzing conversation content showed that information, more than friendliness, distinguishes outgroup bots from control chatbots. Together, these findings establish synthetic contact as a scalable, behaviorally consequential, and -- unlike face-to-face contact -- widely acceptable form of cross-partisan engagement.

cs.HC

Coach not crutch: Evidence that AI can improve writing skill despite reducing effort

In a series of highly-powered empirical studies, we examine the intuition that by sparing effort, using AI inevitably hinders learning. First, in a nationally representative survey of young adults, the majority expressed the view that using AI makes people lazier and less capable. Next, in a random-assignment experiment, we gave participants a tutorial on best practices in professional writing, then provided one group with access to an AI writing tool and asked another to practice writing on their own. Those who practiced with AI indeed exerted less effort while practicing -- yet wrote better cover letters in no-AI writing tests. In a second experiment with more rigorous control conditions, access to AI improved writing more than either googling cover letter examples and tips or receiving personalized feedback on their practice letters from experienced human editors. A third experiment explained these learning gains by showing that AI can teach by example: participants who viewed a cover letter that had been revised by the AI tool but did no further practice improved their writing as much as those who practiced writing with the original AI tool. Collectively, these pre-registered experiments suggest that AI can exert opposing effects on effort and learning rate -- making it possible in at least some cases to work less and learn more.

cs.HC