Towards Automatic Generation of Peer-Targeted Science Talk in Curiosity-Evoking Virtual Agent

Bhargavi Paranjape, Yubin Ge, Zhen Bai, Jessica Hammer, Justine Cassell

Abstract: Curiosity is a critical skill that spurs learning, but is often found to decline with age and schooling. Recent research has shown that peer interaction may serve a special role in inducing curiosity through increased uncertainty and conceptual conflicts, since peers have similar authority in knowledge. For a virtual agent to stimulate curiosity, it should be able to generate curiosity-eliciting verbal behaviors such as hypothesis verbalization and argumentation, in the manner that simulates peer-like cognitive and behavioral abilities. In this paper, we design and implement a virtual peer that can carry out key curiosity-eliciting science talk during a dialog-based multi-party board game. We propose a child-centered and data-driven approach to simulate the latent reasoning process of young children and age-appropriate language during open-ended game play. In particular, we use a combination of child knowledge-graph construction and child-child interaction driven modeling to generate game appropriate behaviors that are compatible with 9-14 year old children. Encouraging human evaluation of the generated behaviors and generalizability of the generation framework to other tasks opens up new directions in incorporating open-endedness and science talk in virtual agents that will make them truly play a peer role in learning.