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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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This content will become publicly available on June 23, 2026
StoryPal: Supporting Young Children's Dialogic Reading with Large Language Models
Early literacy skills are crucial predictors of children’s academic success. Dialogic reading—an interactive approach where adults and children engage in discussions about stories—has proven highly effective in developing these skills. However, many families face barriers implementing this practice due to time constraints, limited resources, or linguistic challenges. We present StoryPal, an LLM-powered conversational agent that facilitates dialogic reading through contextual questioning, adaptive scaffolding, and personalized feedback. In a study with 23 children ages 4-7 from diverse socioeconomic and linguistic backgrounds, we found high levels of verbal engagement with distinct patterns between English-dominant and bilingual children. The system’s dynamic scaffolding effectively supported struggling readers while challenging proficient ones. Parents valued StoryPal as a supplementary tool that maintained children’s reading engagement when they were unavailable, but emphasized that it should not replace parent-child interactions. Our findings demonstrate the potential of LLM-powered agents to support dialogic reading by adhering to established educational practices.
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- PAR ID:
- 10615882
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400714733
- Page Range / eLocation ID:
- 494 to 511
- Format(s):
- Medium: X
- Location:
- Reykjavik Iceland
- Sponsoring Org:
- National Science Foundation
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