Help me and I'll help you: Speakers' and listeners' collaborative effort and the division of labour in human-agent collaborative communication

Collaboration is a key use case for conversational AI agents. Yet we know little about how agents’ collaborative effort affects users’ reciprocal effort and how this relates to perceptions of agent conversational capability. Through an online director-matcher task (n = 267) whereby participants interacted with agents that varied in their collaborative effort, we found that users rated agents that were less communicatively collaborative as less competent partners. Yet, contrary to the division of labour principle in communication, users only increased their own collaborative effort as speakers when communicating with more collaborative agents, whilst also benefitting more as listeners when interacting with such agents. We discuss the implications of these findings bringing together partner modelling and division of labour principles in driving human-agent collaborative communication in both speaker and listener effort, and consider the strategic application of agent collaborative effort in the design of conversational AI.