Leveraging an LLM-enhanced bilingual conversational agent for EFL children’s dialogic reading: Insights from children, parents, and educators

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Dialogic reading, a technique in which adults and children engage in interactive discussions around a story, has been shown to improve children’s language and literacy development. Despite its evidence-based benefits, its adoption amongfamilies with English as a Foreign Language (EFL) backgrounds has been particularly challenging due to limited English proficiency, restricted conversational skills, and a low inclination to read in English. This paper presents “Storio'', an e-book integrated with a bilingual large language model (LLM)-based conversational agent named “Mia'', used as a design probe to investigate interactions between EFL children (N=17) and the agent, and to gather insights from parents (N=19) and educators (N=2). The findings indicate that the bilingual agent effectively supports language output, fosters interactive experiences, and promotes language skills. The study offers valuable design implications for the development of LLM-based and children’s interactive e-books tailored to the needs of children with diverse linguistic and cultural backgrounds.