Human-Centered Distributed Conversational Modeling: Efficient Modeling of Robust Virtual Human Conversations

Abstract

Currently, applications that focus on providing conversations with virtual humans require extensive work to create robust conversational models. We present a new approach called Human-centered Distributed Conversational Modeling. Using this approach, users create conversational models in a distributed manner. To do this, end-users interact with virtual humans to provide new stimuli (questions and statements), and domain-specific experts (e.g. medical/psychology educators) provide new virtual human responses. Using this process, users become the primary developers of conversational models. We tested our approach by creating an example application, Virtual People Factory. Using Virtual People Factory, a pharmacy instructor and 186 pharmacy students were able to create a robust conversational model in 15 hours. This is approximately 10% of the time typical in current approaches and results in more comprehensive coverage of the conversational space. In addition, surveys demonstrate the acceptability of this approach by both educators and students.