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.

Developing an educational test can be expensive and time-consuming, as each item must be written by experts and then evaluated by collecting hundreds of student responses.Moreover, many tests require multiple distinct sets of questions administered throughout the school year to closely monitor students' progress, known as parallel tests.In this study, we focus on tests of silent sentence reading efficiency, used to assess students' reading ability over time.To generate high-quality parallel tests, we propose to fine-tune large language models (LLMs) to simulate how previous students would have responded to unseen items.With these simulated responses, we can estimate each item's difficulty and ambiguity.We first use GPT-4 to generate new test items following a list of expert-developed rules and then apply a fine-tuned LLM to filter the items based on criteria from psychological measurements.We also propose an optimal-transport-inspired technique for generating parallel tests and show the generated tests closely correspond to the original test's difficulty and reliability based on crowdworker responses.Our evaluation of a generated test with 234 students from grades 2 to 8 produces test scores highly correlated (r=0.93) to those of a standard test form written by human experts and evaluated across thousands of K-12 students.