Multimodal Large Language Models vs. Human Authors: A Comparative Study of Chinese Fairy Tales for Young Children

In the realm of children’s education, multimodal large language models (MLLMs) are already being utilized to create educational materials for young learners. But how significant are the differences between image-based fairy tales generated by MLLMs and those crafted by human authors? This paper addresses this question through the design of multi-dimensional human evaluation and actual questionnaire surveys. Specifically, we conducted studies on evaluating MLLM-generated stories and distinguishing them from human-written stories involving 50 undergraduate students in education-related majors, 30 first-grade students, 81 second-grade students, and 103 parents. The findings reveal that most undergraduate students with an educational background, elementary school students, and parents perceive stories generated by MLLMs as being highly similar to those written by humans. Through the evaluation of primary school students and vocabulary analysis, it is further shown that, unlike human-authored stories, which tend to exceed the vocabulary level of young students, MLLM-generated stories are able to control vocabulary complexity and are also very interesting for young readers. Based on the results of the above experiments, we further discuss the following question: Can MLLMs assist or even replace humans in writing Chinese children’s fairy tales based on pictures for young children? We approached this question from both a technical perspective and a user perspective.