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Background Computational thinking (CT) is a crucial domain for children to develop in their early years. To increase children's access to CT learning resources, educational programs like PBS KIDS “Lyla in the Loop” have been developed to incorporate CT concepts through narrative structures where characters solve problems using the CT cycle. However, children need explicit guidance to effectively process both educational and narrative content. Engaging children in dialogues that connect educational content with the narrative has proven to enhance comprehension. Aims This study explores the effectiveness of using AI to enable this type of dialogues between children and a media character, supporting children in learning CT by connecting these concepts with everyday situations in “Lyla in the Loop.” Method Through a between-subject randomized control study with 160 children aged four to eight, we will compare children's learning and applications of CT concepts as well as narrative comprehension from AI-assisted dialogues to those who watched the broadcast version of the show without such dialogues. The study also examines the role of children's cognitive abilities and prior CT knowledge in their learning from the show, with or without AI-assisted dialogues. Expected results The findings could enhance our understanding of AI-based scaffolding strategies in children's media and offer practical implications for improving children's learning experiences. 

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. 

Recognizing the challenges bilingual children face in school readiness and the potential of bilingual dialogic shared reading in improving language and literacy, this study investigates the use of a bilingual conversational agent (CA) to enhance shared reading experiences in home environments. While current CAs hold promise in fostering young children's learning, they do not typically consider the linguistic and cultural needs of bilingual children and rarely involve parents intentionally. To this end, we developed a bilingual CA, embedded within ebooks, to support children's language learning and parent engagement for Latine Spanish-English bilingual families. A week-long home-based study with 15 families indicated that the bilingual CA elicited a high level of bilingual verbal engagement from children, thereby promoting their vocabulary acquisition. It also stimulated meaningful conversations among parents and children. This study provides design implications for developing CAs for bilingual children and parents. 

Conversational agents (CAs) are increasingly prevalent in children’s lives, serving as educational companions, particularly in shared reading activities. While effective for monolingual children’s learning, there exists a gap in meeting the unique needs of the rapidly expanding bilingual child population, who face dual challenges of school readiness and heritage language maintenance. Moreover, most current CAs, designed for one-to-one interactions with children, neglect the importance of parents’ active participation in shared reading. Our study introduces the development and home deployment of a bilingual CA, integrated within e-books, designed to foster parent-child joint engagement in shared reading, thereby promoting children’s bilingual language development. Results of the study indicated high levels of family engagement in co-reading activities over an extended period, with observable language learning gains in children. This study provides valuable design implications for designing effective and engaging CAs for bilingual families. 

Social chatbots are designed to build emotional bonds with users, and thus it is particularly important to design these technologies so as to elicit positive perceptions from users. In the current study, we investigate the impacts transparent explanations of chatbots’ mechanisms have on users’ perceptions of the chatbots. A total of 914 participants were recruited from Amazon Mechanical Turk. They were randomly assigned to observe conversation between a hypothetical chatbot and user in one of the two-by-two experimental conditions: whether the participants received an explanation about how the chatbot was trained and whether the chatbot was framed as an intelligent entity or a machine. A fifth group, who believed they were observing interactions between two humans, served as a control. Analyses of participants’ responses to post-observation survey indicated that transparency positively affected perceptions of social chatbots by leading users to (1) find the chatbot less creepy, (2) feel greater affinity to the chatbot, and (3) perceive the chatbot as more socially intelligent, thought these effects were small. Importantly, transparency appeared to have a larger effect in increasing the perceived social intelligence among participants with lower prior AI knowledge. These findings have implications for the design of future social chatbots and support the addition of transparency and explanation for chatbot users.