The rapid integration of artificial intelligence (AI) into communication systems has significantly altered how users interact with digital tools and collaborate with AI agents. This study investigates the dynamics of politeness in human-AI interactions through a controlled experiment with 1,684 participants, each completing sequential text-based tasks with a conversational AI system. Participants were randomly assigned to one of several conditions that varied in the AI's visual identity (no icon, robot icon, or human face), allowing us to examine the role of perceived anthropomorphism through a minimal visual cue. Politeness was measured using linguistic markers and analyzed using statistical models that account for task sequence and individual differences. Our findings show that politeness toward AI declines over time, with a temporary increase at the start of a second task. Compared to human-human interactions in a benchmark dataset, politeness in human-AI interactions eroded more quickly. Younger participants were less polite overall, and although frequent AI users also appeared less polite descriptively, adjusted models showed a small positive association with daily AI use. Anthropomorphic visual cues, especially human-like avatars, led to more sustained polite behavior. These results offer insight into how users adapt social norms in AI-mediated collaboration and suggest design strategies for fostering respectful and effective human-AI communication.

If large language models like GPT-3 preferably produce a particular point of view, they may influence people’s opinions on an unknown scale. This study investigates whether a language-model-powered writing assistant that generates some opinions more often than others impacts what users write – and what they think. In an online experiment, we asked participants (N=1,506) to write a post discussing whether social media is good for society. Treatment group participants used a language-model-powered writing assistant configured to argue that social media is good or bad for society. Participants then completed a social media attitude survey, and independent judges (N=500) evaluated the opinions expressed in their writing. Using the opinionated language model affected the opinions expressed in participants’ writing and shifted their opinions in the subsequent attitude survey. We discuss the wider implications of our results and argue that the opinions built into AI language technologies need to be monitored and engineered more carefully.

Peer production communities often struggle to retain contributors beyond initial engagement. This may be a result of contributors' level of motivation, as it is deeply intertwined with activity. Existing studies on participation focus on activity dynamics but overlook the accompanied changes in motivation. To fill this gap, this study examines the interplay between contributors' fun motives and activity over time. We combine motivational data from two surveys of Wikipedia newcomers with data of two periods of editing activity. We find that persistence in editing is related to fun, while the amount of editing is not: individuals who persist in editing are characterized by higher fun motives early on (when compared to dropouts), though their motives are not related to the number of edits made. Moreover, we found that newcomers' experience of fun was reinforced by their amount of activity over time: editors who were initially motivated by fun entered a virtuous cycle, whereas those who initially had low fun motives entered a vicious cycle. Our findings shed new light on the importance of early experiences and reveal that the relationship between motivation and participation levels is more complex than previously understood.