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

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arxiv 2304.14276 v1 pith:LBDSB4C5 submitted 2023-04-24 cs.CL cs.LG

classification cs.CLcs.LG
keywords essaysmodelshuman-writtenchatgptresultsargumentativecharacteristicschatgpt-generated
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Mind the Gap! Choice Independence in Using Multilingual LLMs for Persuasive Co-Writing Tasks in Different Languages

    cs.CL 2025-02 conditional novelty 6.0 of 10

    Users who first used a Spanish AI writing assistant subsequently used the English AI writing assistant less, suggesting a spillover that violates choice independence.

  2. Transforming Chatbot Text: A Sequence-to-Sequence Approach

    cs.CL 2025-06 conditional novelty 4.0 of 10

    Fine-tuned T5-small and BART can lower the accuracy of GPT-text classifiers, but retraining the classifiers on the transformed text restores detection accuracy.

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