mlsanityData to 14 Oct 2026

AI, write an essay for me: A large-scale comparison of human-written versus ChatGPT-generated essays

arXiv 2304.14276 ↗First posted 24 Apr 2023cs.CL cs.LGcited by 10

Not on the map. Why?

Abstract (arXiv)

Background: Recently, ChatGPT and similar generative AI models have attracted hundreds of millions of users and become part of the public discourse. Many believe that such models will disrupt society and will result in a significant change in the education system and information generation in the future. So far, this belief is based on either colloquial evidence or benchmarks from the owners of the models -- both lack scientific rigour. Objective: Through a large-scale study comparing human-written versus ChatGPT-generated argumentative student essays, we systematically assess the quality of the AI-generated content. Methods: A large corpus of essays was rated using standard criteria by a large number of human experts (teachers). We augment the analysis with a consideration of the linguistic characteristics of the generated essays. Results: Our results demonstrate that ChatGPT generates essays that are rated higher for quality than human-written essays. The writing style of the AI models exhibits linguistic characteristics that are different from those of the human-written essays, e.g., it is characterized by fewer discourse and epistemic markers, but more nominalizations and greater lexical diversity. Conclusions: Our results clearly demonstrate that models like ChatGPT outperform humans in generating argumentative essays. Since the technology is readily available for anyone to use, educators must act immediately. We must re-invent homework and develop teaching concepts that utilize these AI models in the same way as math utilized the calculator: teach the general concepts first and then use AI tools to free up time for other learning objectives.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 18 of its 49 references that are in the map. First posted on arXiv 24 Apr 2023.

Builds on

18 of 49 references.

References · 18

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6 more
  1. 2020Linguistic Features for Readability Assessmentcited by 35
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Cited by year

Citing papers per year: 2023: 5; 2024: 4; 2025: 1.

10 papers.