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Conversational agents (CAs) have become increasingly prominent in education, yet most research focuses on disembodied chatbots for higher education. Because visual representation is central for early childhood, we developed a web application with an LLM-based CA to examine how embodiment and visual appearance relate to children’s dialogic interactions. In an online study with 33 children ages 4–8, participants were randomly assigned to interact with a text-based CA, an embodied CA with a random avatar, or an embodied CA whose appearance children could customize. Children showed high verbal engagement regardless of CA type. Across interactions, children largely remained on topic, though playfulness, disengagement, and social connectedness varied. Interactions with custom avatar CAs more often included playful and disengaged behaviors, while customization patterns suggested that children created avatars resembling themselves or explored fantastical identities. This study provides initial insights into design mechanisms for child-centered pedagogical CAs. 

Interactive media can be effectively enhanced through advancements in artificial intelligence (AI). Through a research–practice partnership with PBS KIDS, we integrated AI into popular PBS KIDS shows to support dialogic interactions that actively engage children in STEM learning. These interactions help ground abstract STEM concepts in socially meaningful experiences. In this work, we present an interactive web app designed to scaffold children’s learning and engagement at home. We discuss design considerations, challenges related to children’s speech recognition, and insights from iterative testing that informed the app’s current stage of development. 

Abstract As generative AI becomes ubiquitous, writers must decide if, when, and how to incorporate generative AI into their writing process. Educators must sort through their role in preparing students to make these decisions in a quickly evolving technological landscape. We created an AI-enabled writing tool that provides scaffolded use of a large language model as part of a research study on integrating generative AI into an upper division STEM writing-intensive course. Drawing on decades of research on integrating digital tools into instruction and writing research, we discuss the framework that drove our initial design considerations and instructional resources. We then share our findings from a year of design-based implementation research during the 2023–2024 academic year. Our original instruction framework identified the need for students to understand, access, prompt, corroborate, and incorporate the generative AI use effectively. In this paper, we explain the need for students to think first, before using AI, move through good enough prompting to agentic iterative prompting, and reflect on their use at the end. We also provide emerging best practices for instructors, beginning with identifying learning objectives, determining the appropriate AI role, revising the content, reflecting on the revised curriculum, and reintroducing learning as needed. We end with an indication of our future directions. 

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. 

According to an ecological affordances perspective, any static curriculum has a set of affordances, and differences in teachers, students, and the teaching environment change how those affordances are viewed and used. Therefore, teaching is a relationship between the curriculum, the teacher, and the students. As such, it is not only possible but expected that a teacher will diverge from the details of a lesson plan to better accommodate the needs of themselves as a teacher and their students as learners. In this study, we report on a mixed-methods investigation that explores the different ways upper-elementary and middle-school (7- 13 y.o. students) teachers implement the Scratch-based TIPP&SEE learning strategy and the reasoning for their approaches. As expected, we find that teachers across grade levels often deviate from lesson plan details to cater to their own classrooms. For example, teachers serving younger grades were far more likely to keep scaffolds that lesson plans suggest removing. The varied degree of deviation suggests that the repeated use of a learning strategy, alongside lesson plans that present a variety of scaffolded implementations, is beneficial in enabling teachers to adapt lesson content to serve the needs of their specific classroom.