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Award ID contains: 1906321

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  1. ; ; ; ; ; (, Child Development)
    Abstract Dialogic reading, when children are read a storybook and engaged in relevant conversation, is a powerful strategy for fostering language development. With the development of artificial intelligence, conversational agents can engage children in elements of dialogic reading. This study examined whether a conversational agent can improve children's story comprehension and engagement, as compared to an adult reading partner. Using a 2 (dialogic reading or non‐dialogic reading) × 2 (agent or human) factorial design, a total of 117 three‐ to six‐year‐olds (50% Female, 37% White, 31% Asian, 21% multi‐ethnic) were randomly assigned into one of the four conditions. Results revealed that a conversational agent can replicate the benefits of dialogic reading with a human partner by enhancing children's narrative‐relevant vocalizations, reducing irrelevant vocalizations, and improving story comprehension. 
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  2. ; ; ; ; (, Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems)
    null (Ed.)
    Full Text Available 
  3. (, Proceedings of the 19th ACM International Conference on Interaction Design and Children (IDC ’20))
    My dissertation project leverages an intelligent conversational agent embodied in an on-screen character to add social contingency into children’s science video watching experiences. This conversational agent has been developed in an iterative process and embedded in a new PBS KIDS science show, "Elinor Wonders Why." My research aims to understand the design, feasibility, and promise of virtual conversation with media characters in young children’s informal science learning through video watching. 
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    Accepted Manuscript Full Text Available