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Detecting Artificial Intelligence-Generated Versus Human-Written Medical Student Essays: Semirandomized Controlled

Berin Doru¹, Christoph Maier¹, Johanna Sophie Busse¹

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Summary

Experts can identify AI-generated scientific texts by recognizing linguistic patterns like redundancy and repetition, not content familiarity. This study highlights key features for distinguishing human from artificial intelligence writing.

Keywords:

AI ChatGPT LLMs artificial intelligence authorship chatbot decision-making detection large language models linguistic quality logical coherence medical student textual analysis writing style

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Area of Science:

Artificial Intelligence (AI) in Academia
Scientific Writing Authenticity
Medical Education Technology

Background:

Sophistication of AI models like ChatGPT challenges human vs. AI text differentiation.
Academic integrity concerns are rising, especially in medicine, due to AI text generation.
Accurate authorship attribution is critical for scholarly work.

Purpose of the Study:

To assess medical professionals' and humanities scholars' ability to distinguish AI-generated from human-written German medical texts.
To analyze the reasoning behind expert identification, focusing on content familiarity and linguistic cues.
To evaluate the impact of specific linguistic features on authorship identification.

Main Methods:

A semirandomized controlled study involving 35 experts (22 medical, 13 humanities) evaluating paired medical texts.
Experts identified AI-generated (ChatGPT 3.5) vs. student-written texts and provided justifications.
Qualitative analysis of justifications and quantitative analysis of text characteristics and expert ratings.

Main Results:

Experts accurately identified AI-generated texts in 70% of cases, with minimal difference between medical and humanities groups.
Identification accuracy was largely independent of content familiarity.
Stylistic features, including redundancy (OR 6.90), repetition (OR 8.05), and coherence (OR 6.62), were crucial for AI text detection.

Conclusions:

Medical and humanities experts can identify AI-generated texts based on linguistic attributes, not content expertise.
Redundancy, repetition, and coherence are key indicators of AI authorship in scientific writing.
Further research should explore training methods based on these linguistic features to improve AI text detection.