Seeking common ground with a conversational chatbot

Abstract Conversational AI is advancing rapidly, enabling significant improvements in chatbots’ conversational abilities. Currently, available conversational chatbots (e.g., Snapchat’s MyAI) appear to generate fairly realistic, often human-like output. As collaboration between humans and machines becomes more common, and AI systems are increasingly viewed as more than just tools, understanding human communication in such contexts is crucial. Despite the vast array of applications and the increasing number of human-bot interactions, research on how humans interact with conversational chatbots is scarce. One possible reason for this gap is that studying human-computer communication may require adaptations of existing pragmatic frameworks, due to the unique characteristics of these interactions. A key feature of such conversations is their asymmetrical nature. In this paper, we present evidence that the sociocognitive approach (SCA), which takes into account the asymmetry between interlocutors as regards their possible common grounds, has explanatory potential to describe human-AI-powered chatbot interactions. We collected data from thirty-two L1 Hungarian participants interacting with a conversational chatbot on three consecutive days. The turn-by-turn analysis of the 96 conversations provides insights not only into the nature of common ground humans presuppose with a conversational agent, but also into the processes of building emergent common ground over time. Furthermore, we present linguistic evidence that both egocentrism and cooperation play a role in human-chatbot interaction. While the former is manifested in approaching the chatbot as if it were human, the latter appears to play a role in changing strategies that serve common ground seeking and building.