Semantic Alignment With an AI-Derived Reference in Relation to Children’s Scientific Creativity

Abstract

As generative artificial intelligence (AI) becomes increasingly visible in education, an important question concerns how AI-related knowledge structures are associated with children’s scientific creativity. Prior studies have mainly examined this issue through performance outcomes. In contrast, this study focused on knowledge representation. We examined whether children’s scientific creativity was related to the similarity between their semantic organization and an AI-derived semantic reference. A total of 125 sixth-grade students completed a scientific questioning task and a word-association task. In parallel, we generated responses from 120 AI agents based on six contemporary large language models to construct an aggregated AI semantic reference. Individual semantic networks were derived from the word-association data, and representational similarity analysis was used to estimate each child’s alignment with the AI reference. The results showed a nonlinear relationship. Originality followed an inverted-U pattern, whereas the composite creativity index showed a peak followed by a plateau. In both cases, the highest performance was observed at intermediate levels of alignment. As a contextual comparison, AI agents showed higher originality but lower flexibility than children, and their semantic networks were sparser and more modular. These findings indicate that scientific creativity was highest at intermediate levels of alignment with the AI-derived semantic reference. At higher levels of alignment, originality declined, whereas composite creativity plateaued. The study provides a representational perspective on the nonlinear association between children’s semantic organization and scientific creativity.