Does AI Homogenize Student Thinking? A Multi-Dimensional Analysis of Structural Convergence in AI-Augmented Essays
This addresses the problem of potential homogenization in student thinking due to AI tools, with implications for educators and AI developers, though it is incremental in exploring interaction design effects.
The study investigated the impact of AI-assisted writing on the structural diversity of student essays, finding a Quality-Homogenization Tradeoff where quality improvements co-occurred with significant homogenization, such as a 70-78% loss in variance in cohesion architecture, though prompt specificity could reverse this effect.
While AI-assisted writing has been widely reported to improve essay quality, its impact on the structural diversity of student thinking remains unexplored. Analyzing 6,875 essays across five conditions (Human-only, AI-only, and three Human+AI prompt strategies), we provide the first empirical evidence of a Quality-Homogenization Tradeoff, in which substantial quality gains co-occur with significant homogenization. The effect is dimension-specific: cohesion architecture lost 70-78% of its variance, whereas perspective plurality was diversified. Convergence target analysis further revealed that AI-augmented essays were pulled toward AI structural patterns yet deviated significantly from the Human-AI axis, indicating simultaneous partial replacement and partial emergence. Crucially, prompt specificity reversed homogenization into diversification on argument depth, demonstrating that homogenization is not an intrinsic property of AI but a function of interaction design.