Conversational AI in children's home literacy learning: effectiveness, advantages, challenges, and family perception

This study investigates the effectiveness, affordances, limitations, and family perceptions of conversational AI for home literacy learning vs. human. We developed a large language model (LLM)-powered conversational AI system, named Vovo, to teach children vocabulary and co-construct stories using structured literacy pedagogy. The system was tested in home environments over six weeks with 10 families and their children aged 3–7 (M = 5.4). Across 150 learning sessions, Vovo delivered structured literacy instruction as effectively as parents, though children achieved higher learning outcomes when learning with parents. Video analysis revealed Vovo's advantages in pedagogical consistency, language modeling, and verbal socioemotional support, while facing challenges in speech recognition, instructional persistence, nonverbal social cues, and phoneme instruction. Parents perceived Vovo as intelligent, useful, and trustworthy, while expecting a multimodal design to improve engagement. Children perceived Vovo smart and fun but still preferred learning with parents due to emotional bonding. As one of the first studies to embed structured literacy pedagogy into home-based conversational AI system, this research contributes empirical insights into the evolving role of AI in home literacy environments. It also underscores the socially responsive AI design in early education and calls for future design that support parent-child-AI triadic interactions to optimize AI in home literacy learning.