Towards Emotionally Expressive Virtual Human Agents to Foster L2 Production: Insights from a Preliminary Woz Experiment

Abstract

In second-language communication, emotional feedbacks play a preponderant role in instilling positive emotions and thereby facilitating the production of the target language by second-language learners. In contrast, facial expressions help convey emotion, intent, and sometimes even desired actions more effectively. Additionally, according to the facial feedback hypothesis, a major component of several contemporary theories of emotion, facial expressions can regulate emotional behavior and experience. The aim of this study was to determine whether and to what extent emotional expressions reproduced by virtual agents could provide empathetic support to second-language learners during communication tasks. To do so, using the Facial Coding Action System, we implemented a prototype virtual agent that can display a collection of nonverbal feedbacks, including Ekman’ six basic universal emotions and gazing and nodding behaviors. Then, we designed a Wizard of Oz experiment in which second-language learners were assigned independent speaking tasks with a virtual agent. In this paper, we outline our proposed method and report on an initial experimental evaluation which validated the meaningfulness of our approach. Moreover, we present our next steps for improving the system and validating its usefulness through large-scale experiments.