Sooyeon Jeong

Tega: A Social Robot

Tega is a new expressive “squash and stretch”, Android-based social robot platform, designed to enable long-term interactions with children.

Robotic learning companions for early language development

Research from the past two decades indicates that preschool is a critical time for childrens oral language and vocabulary development, which in turn is a primary predictor of later academic success. However, given the inherently social nature of language learning, it is difficult to develop scalable interventions for young children. Here, we present one solution in the form of robotic learning companions, using the DragonBot platform. Designed as interactive, social characters, these robots combine the flexibility and personalization afforded by educational software with a crucial social context, as peers and conversation partners. They can supplement teachers and caregivers, allowing remote operation as well as the potential for autonomously participating with children in language learning activities. Our aim is to demonstrate the efficacy of the DragonBot platform as an engaging, social, learning companion.

Flat vs. Expressive Storytelling: Young Children’s Learning and Retention of a Social Robot’s Narrative

Prior research with preschool children has established that dialogic or active book reading is an effective method for expanding young children’s vocabulary. In this exploratory study, we asked whether similar benefits are observed when a robot engages in dialogic reading with preschoolers. Given the established effectiveness of active reading, we also asked whether this effectiveness was critically dependent on the expressive characteristics of the robot. For approximately half the children, the robot’s active reading was expressive; the robot’s voice included a wide range of intonation and emotion (Expressive). For the remaining children, the robot read and conversed with a flat voice, which sounded similar to a classic text-to-speech engine and had little dynamic range (Flat). The robot’s movements were kept constant across conditions. We performed a verification study using Amazon Mechanical Turk (AMT) to confirm that the Expressive robot was viewed as significantly more expressive, more emotional, and less passive than the Flat robot. We invited 45 preschoolers with an average age of 5 years who were either English Language Learners (ELL), bilingual, or native English speakers to engage in the reading task with the robot. The robot narrated a story from a picture book, using active reading techniques and including a set of target vocabulary words in the narration. Children were post-tested on the vocabulary words and were also asked to retell the story to a puppet. A subset of 34 children performed a second story retelling 4–6 weeks later. Children reported liking and learning from the robot a similar amount in the Expressive and Flat conditions. However, as compared to children in the Flat condition, children in the Expressive condition were more concentrated and engaged as indexed by their facial expressions; they emulated the robot’s story more in their story retells; and they told longer stories during their delayed retelling. Furthermore, children who responded to the robot’s active reading questions were more likely to correctly identify the target vocabulary words in the Expressive condition than in the Flat condition. Taken together, these results suggest that children may benefit more from the expressive robot than from the flat robot.

Toward Robotic Companions that Enhance Psychological Wellbeing with Smartphone Technology

Despite the fact that depression is becoming more and more prevalent, mental healthcare still has high barriers for many people. In order to lower these barriers, we plan to study the efficacy of using socially assistive robots with mobile phone technology to improve peoples psychological wellbeing. We propose a longitudinal experimental study that explores (1) the impact of a social agents physical presence on therapeutic interactions and (2) the benefits of leveraging mobile phone data to personalize and intelligently select psychological interventions for maximized therapeutic effect. Results from the study will provide insights into interactive technologies that deeply understand individuals.