The role of large language models (LLMs) in education is increasing, yet little attention has been paid to whether LLM-generated text resembles child language. This study evaluates how LLMs replicate child-like language by comparing LLM-generated texts to a collection of German children's descriptions of picture stories. We generated two LLM-based corpora using the same picture stories and two prompt types: zero-shot and few-shot prompts specifying a general age from the children corpus. We conducted a comparative analysis across psycholinguistic text properties, including word frequency, lexical richness, sentence and word length, part-of-speech tags, and semantic similarity with word embeddings. The results show that LLM-generated texts are longer but less lexically rich, rely more on high-frequency words, and under-represent nouns. Semantic vector space analysis revealed low similarity, highlighting differences between the two corpora on the level of corpus semantics. Few-shot prompt increased similarities between children and LLM text to a minor extent, but still failed to replicate lexical and semantic patterns. The findings contribute to our understanding of how LLMs approximate child language through multimodal prompting (text + image) and give insights into their use in psycholinguistic research and education while raising important questions about the appropriateness of LLM-generated language in child-directed educational tools.

This looks perfect and it clearly explains different type of cars. Let's try to decode what can overfitting and underfitting mean in Machine Learning. This definitions are subjective and it is being discussed only for novice learners to ML. On the First Day, when father is teaching her daughter, he hasn't picked an image with enough of car examples, which made daughter failed to generalize car object. On the Second Day, when father father is teaching her daughter, he has picked an images with cars and her daughter exactly learn the shape/type of car in the image, which forced her daughter to believe that car can only be of two shapes/types, which made her daughter failed to generalize car object.