Quit Playing Language Games with My Heart; or, Conversational AI and Knowledge Sharing

This paper shares guidance for designing conversational AI based on findings from linguistic and social analysis of core shifts introduced by large language models. When Duolingo and Babbel added AI features to their language learning apps, the games that users play on the apps was transformed, giving us a window into the structure of AI‐driven user interactions in general. Dynamic, turn‐taking dialogues resembling natural conversation now appear alongside multiple‐choice and drag‐and‐drop vocabulary games. The transformation is not just technological: It also lays bare the shifting language games that we have come to play more broadly in our interactions with LLMs such as Chat‐GPT, Claude, and Gemini. People engage with them in back‐and‐forth conversations in which different kinds of speech acts are at play. By contrast, traditional conversational agents based on decision‐tree learning rely on discrete, finite, and close‐ended commands. The paper argues that the future of AI conversation is one in which rule‐based interactions co‐exist with intention‐based interactions powered by generative AI. Design elements on Duolingo, in particular, draw on diverse styles of communication and offer guidance to ensure that conversational AI platforms in general remain open to new forms of dialogue that enrich both artificial and natural conversation alike.