Reproducing Hierarchies
This study examines how AI-generated children’s stories produced by large language models (LLMs, e.g., ChatGPT) reflect and reproduce social biases in the social representations of intelligence across gender, racial identity, socioeconomic status, and educational level. We ask three questions: (1) how these narratives mirror hegemonic representations of intelligence; (2) in what ways they legitimize social inequalities; and (3) whether repeated exposure could shape children’s self-perception and aspirations. Grounded in a social-representations/power-relations framework, we generated 216 stories via two prompt scenarios that systematically varied protagonist attributes, ensuring stability through repeated prompting and saturation procedure. We then combined critical discourse analysis, qualitative analytical questioning, and lexicographic analysis to chart fields of possibility, mechanisms of legitimation, and the internalization of power relations. Results show a recurrent alignment with dominant hierarchies: intelligence is frequently naturalized as intrinsic to high-status groups; marginalized protagonists are underrepresented or steered toward alternative (emotional/social) forms of intelligence; and formal schooling is repeatedly positioned as the primary marker of being “truly intelligent.” We discuss implications for stereotype reinforcement, potential constraint of children’s aspirations, and the need for bias-aware narrative design and governance of LLM outputs.