Adaptive role switching in socially interactive agents for children's language learning

• Main Author: Chen, Huili, S.M. Massachusetts Institute of Technology
• Other Authors: Cynthia Breazeal.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2018
• Subjects: Program in Media Arts and Sciences ()
• Online Access: http://hdl.handle.net/1721.1/119081

Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2018. === Cataloged from PDF version of thesis. === Includes bibliographical references (pages 75-84). === Learning language and literacy at a young age is important, as children's early language ability can impact their later educational success [1][2]. However, one of the major barriers to early language and literacy learning for many children around the globe is a lack of resources in homes and schools. A variety of technological interventions, such as TV series and educational apps, were designed to help overcome such barriers and support children's learning. However, not all of them necessarily provide children with conversational experiences, which have been found to significantly impact the children's language-related neural development [3]. Among a variety of educational media, embodied interactive agents (e.g., social robots) seem to be an effective yet resource-efficient tool that can enable children to learn through conversational turn taking. Specifically, embodied interactive agents can serve as learning companions for young children and provide more interactive and immersive learning experience. I explored how social robots could help promote children's language and literacy learning. More specifically, I designed and computationally created a collaborative, engaging learning interaction between a robot and a child who play as peers. First, I designed a tablet-based literacy learning game called WordQuest using the design principles for educational games. Second, I developed a reinforcement learning model that enabled the robot to adaptively switch its collaborative roles (e.g., expert and novice roles) in a way that promoted children's best learning. Third, I conducted an experiment with three conditions, which were fixed expert robot, fixed novice robot, and adaptive role switching robot, and tested on 60 children recruited from a local primary school in Boston. Last, I evaluated how the robot's collaborative roles differentially affected children's learning performance, engagement, and perception of the learning experiences. I found out that children across the three conditions all learned new words and had a very positive experience of playing WordQuest with the robot. In addition, children interacting with the adaptive robot consistently outperformed children from the other two conditions in terms of vocabulary acquisition and retention. === by Huili Chen. === S.M.