Lexical diversity, syntactic complexity, and readability: a corpus-based analysis of ChatGPT and L2 student essays

Abstract

This study compares AI-generated texts (via ChatGPT) and student-written essays in terms of lexical diversity, syntactic complexity, and readability. Grounded in Communication Theory—especially Grice’s Cooperative Principle and Relevance Theory—the research investigates how well AI-generated content aligns with human norms of cooperative communication. Using a corpus of 50 student essays and 50 AI-generated texts, the study applies measures such as Type-Token Ratio (TTR), Mean Length of T-Unit (MLT), and readability indices like Flesch–Kincaid and Gunning-Fog. Results indicate that while ChatGPT produces texts with greater lexical diversity and syntactic complexity, its output tends to be less readable and often falls short in communicative appropriateness. These findings carry important implications for educators seeking to integrate AI tools into writing instruction, particularly for second-language (L2) learners. The study concludes by calling for improvements to AI systems that would better balance linguistic complexity with clarity and accessibility.

Ähnliche Arbeiten