Exploring linguistic fingerprints in human and AI-generated texts: An NLP-based approach in second language writing

The aim of this study is to conduct a linguistic analysis of English essays produced by intermediate-level second language (L2) learners compared to those generated by AI, across five linguistic dimensions: lexical richness, syntactic complexity, semantic similarity, discourse cohesion, and surface-level errors. A parallel corpus of 160 essays, 80 AI-generated and 80 learner-written, was collected and analyzed using Natural Language Processing (NLP) techniques. The results revealed that AI essays tend to be longer and syntactically more complex, with significantly higher lexical diversity and greater use of content words. While both types of essays share similar sentiment and cohesion patterns, the AI essays demonstrate more advanced sentence structures and deeper syntactic tree depths. Readability metrics show that the learners’ essays are simpler and more accessible. Error analysis revealed that the human essays contain four times more errors, particularly in spelling and stylistic choices. The study highlights how AI-generated language diverges from learner-produced writing and offers insights into how AI tools can be effectively leveraged to support language development at this proficiency level. • A comparative linguistic analysis of English essays produced by intermediate-level second language (L2) learners • A total of 160 essays, 80 AI-generated and 80 human-written, were analyzed using NLP tools • Focusing on five linguistic dimensions: lexical richness, syntactic complexity, semantic similarity, discourse cohesion, and surface-level errors • AI essays were longer, lexically denser, and syntactically more complex, with deeper parse trees and more frequent use of content words • The human-authored essays demonstrated simpler sentence structures and greater readability, containing 4 times more surface-level errors