An Agent-Based Model of GenAI in Creative Writing: Novelty Gains and Diversity Declines

Generative artificial intelligence (GenAI) is being rapidly adopted in education. Its effects on creativity unfold at both individual and collective levels. We develop an agent-based model that represents students’ creative outputs in a two-dimensional idea space to examine how GenAI assistance in creative writing shapes individual creativity and classroom-level idea diversity. Simulations show that GenAI increases individual creativity, with disproportionately larger gains for less creative students, while reducing collective diversity through idea homogenization. Critically, the model reveals a more nuanced, non-linear pattern of collective diversity than empirical accounts of homogenization alone: collective diversity can increase at low-to-moderate adoption rates before reaching a tipping point and declining at high adoption rates. These findings highlight the need to evaluate GenAI in education using both individual performance metrics and collective system outcomes.