Unraveling Creativity Through Variability: A Comparison of LLMs and Humans in an Educational Q&A Scenario
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Abstract
Large Language Models (LLMs) have demonstrated a remarkable capacity for generating human-quality text across diverse applications. In light of this, the use of these models is becoming increasingly widespread. Education is no exception and so literature is starting to explore the potential of LLMs in educational contexts: ranging from teaching assistant chatbot to personalized tutoring. Although the versatility of these models is evident, the critical question arises whether they can truly replicate the creativity and nuances of human languages, giving students the variability and richness of expression necessary for effective learning, and for nurturing critical and divergent thinking. This is particularly important in the pre-school or primary education context, where novel and insightful content is crucial for effective engagement. Framing this problem from an abstract linguistic perspective, it translates into wondering how effectively the “creativity” of LLM-generated text can be quantified and how it compares to human one. So, recognizing the inherent ambiguity in defining creativity this paper investigates the variability of LLM outputs as a potential proxy, and compares it with observed text variability in a large human-authored dataset. Specifically focusing on early childhood education (ECE), we compare the semantic and syntactic diversity of responses from LLMs and humans prompted to answer like 5-year-olds. Our empirical results show LLMs have lower variability than humans, particularly on repeated questions, though the gap narrows for different questions. This limitation raises concerns for educational suitability, where diverse explanations of concepts are crucial, suggesting a need to enhance LLM expressive range and diversity.
@article{braccini2026unraveling,
title = {Unraveling Creativity Through Variability: A Comparison of LLMs and Humans in an Educational Q\&A Scenario},
author = {Braccini, M. and Aguzzi, G. and Baldini, P.},
journal = {Technology, Knowledge and Learning },
year = {2026},
publisher = {Springer Nature}
}