Does AI Homogenize Student Thinking? A Multi-Dimensional Analysis of Structural Convergence in AI-Augmented Essays

TL;DR

This study investigates how AI-assisted writing affects the diversity of student essay structures, revealing a tradeoff between quality improvements and homogenization, with effects varying across different structural dimensions.

Contribution

It provides the first empirical analysis of the structural convergence effects of AI on student essays, highlighting the role of prompt design in influencing diversity.

Findings

AI-assisted essays show significant homogenization in cohesion architecture.

Prompt specificity can reverse homogenization into diversification.

AI-augmented essays are structurally pulled toward AI patterns but remain distinct from human-only essays.

Abstract

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.