To address an important gap in creating children’s stories for vocabulary enrichment, we investigate the automatic evaluation of how well stories convey the semantics of target vocabulary words, a task with substantial implications for generating educational content. We motivate this task, which we call measuring contextual informativeness in children’s stories, and provide a formal task definition as well as a dataset for the task. We further propose a method for automating the task using a large language model (LLM). Our experiments show that our approach reaches a Spearman correlation of 0.4983 with human judgments of informativeness, while the strongest baseline only obtains a correlation of 0.3534. An additional analysis shows that the LLM-based approach is able to generalize to measuring contextual informativeness in adult-directed text, on which it also outperforms all baselines.

Reflection is a metacognitive skill that’s essential to creative discovery. As we design interactive technologies for reflection, how might we measure the impact of our designs? In this paper, we develop a coding scheme to explore reflective moments in the speech and language of young children during child-computer interaction. Using cross-disciplinary theories — from the learning sciences to cognitive neuroscience — we define and describe 13 reflective processes occurring within Baumer’s 3 conceptual dimensions of reflection. We then use this framework to measure the impact of a child-robot storytelling interaction with twelve children ages 4–5, and offer developmentally-appropriate transcript examples for each of the 13 reflective processes. This coding scheme provides a practical tool for exploring the impact of our designs on reflection, and can be used to guide design iteration.

With recent advances in large language models (LLMs), the concept of automatically generating children's educational materials has become increasingly realistic.Working toward the goal of age-appropriate simplicity in generated educational texts, we first examine the ability of several popular LLMs to generate stories with properly adjusted lexical and readability levels.We find that, in spite of the growing capabilities of LLMs, they do not yet possess the ability to limit their vocabulary to levels appropriate for younger age groups.As a second experiment, we explore the ability of state-ofthe-art lexical simplification models to generalize to the domain of children's stories and, thus, create an efficient pipeline for their automatic generation.In order to test these models, we develop a dataset of child-directed lexical simplification instances, with examples taken from the LLM-generated stories in our first experiment.We find that, while the strongest-performing lexical simplification models do not perform as well on material designed for children due to their reliance on LLMs, a model that performs well on general data strongly improves its performance on children-directed data with proper finetuning, which we conduct using our newly created child-directed simplification dataset.

Interactions between children and their caregivers represent an important factor of child development. Book sharing and other play interactions are common ways in which caregivers and their preschool-age children interact. Shared book reading has many benefits in early childhood, but some researchers have suggested that children may become passive in such interactions. Additionally, with caregivers having sole access to the information in the text, they may be less open to contributions the child puts forth if they conflict with the text. In contrast, a more symmetrical and cooperative activity, such as putting together a puzzle, may elicit more participation from the child and less categorical input from the caregiver. In a study with 59 2- and 3-year-olds and their caregivers engaging in one of these two activities, we find that interactions centered around the puzzle artifact are characterized by more meaningful participation on the part of the child and less definitive corrections on the part of the caregiver compared to book-based interactions. These findings suggest that alternatives to shared book reading with 2- and 3-year-olds may nudge children to express themselves more creatively when interacting with caregivers. Implications for the design of learning experiences for preschoolers are discussed.

Understanding child language development requires accurately representing children’s lexicons. However, much of the past work modeling children’s vocabulary development has utilized adult-based measures. The present investigation asks whether using corpora that captures the language input of young children more accurately represents children’s vocabulary knowledge. We present a newly-created toddler corpus that incorporates transcripts of child-directed conversations, the text of picture books written for preschoolers, and dialog from G-rated movies to approximate the language input a North American preschooler might hear. We evaluate the utility of the new corpus for modeling children’s vocabulary development by building and analyzing different semantic network models and comparing them to norms based on vocabulary norms for toddlers in this age range. More specifically, the relations between words in our semantic networks were derived from skip-gram neural networks (Word2Vec) trained on our toddler corpus or on Google news. Results revealed that the models built from the toddler corpus were more accurate at predicting toddler vocabulary growth than the adult-based corpus. These results speak to the importance of selecting a corpus that matches the population of interest.

When young children create, they are exploring their emerging skills. And when young children reflect, they are transforming their learning experiences. Yet early childhood play environments often lack toys and tools to scaffold reflection. In this work, we design a stuffed animal robot to converse with young children and prompt creative reflection through open-ended storytelling. We also contribute six design goals for child-robot interaction design. In a hybrid Wizard of Oz study, 33 children ages 4-5 years old across 10 U.S. states engaged in creative play then conversed with a stuffed animal robot to tell a story about their creation. By analyzing children’s story transcripts, we discover four approaches that young children use when responding to the robot’s reflective prompting: Imaginative, Narrative Recall, Process-Oriented, and Descriptive Labeling. Across these approaches, we find that open-ended child-robot interaction can integrate personally meaningful reflective storytelling into diverse creative play practices.