Let's talk! Socially intelligent agents for language conversation training

Abstract

This paper promotes socially intelligent animated agents for the pedagogical task of English conversation training for native speakers of Japanese. Since student-agent conversations are realized as role-playing interactions, strong requirements are imposed on the agents' affective and social abilities. As a novel feature, social role awareness is introduced to animated conversational agents, that are by now strong affective reasoners, but otherwise often lack the social competence observed in humans. In particular, humans may easily adjust their behaviour depending on their respective role in a social setting, whereas their synthetic pendants tend to be driven mostly by emotions and personality. Our main contribution is the incorporation of a "social filter program" to mental models of animated agents. This program may qualify an agent's expression of its emotional state by the social context, thereby enhancing the agent's believability as a conversational partner. Our implemented system is web-based and demonstrates socially aware animated agents in a virtual coffee shop environment. An experiment with our conversation system shows that users consider socially aware agents as more natural than agents that violate conventional practices.