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Human or Machine? A Comparative Analysis of Artificial Intelligence-Generated Writing Detection in Personal

Margaret A Goodman1,2,3,4, Anthony M Lee1,2,3,4, Zachary Schreck1,2,3,4

  • 1Margaret A. Goodman , Program in Physical Therapy in the Mayo Clinic School of Health Sciences at the Mayo Clinic College of Medicine and Science and in the Department of Physical Medicine and Rehabilitation at the Mayo Clinic.

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PubMed
Summary
This summary is machine-generated.

Human readers, Recurrence Quantification Analysis (RQA), and GPTZero accurately distinguish AI-generated from human-written personal statements. These methods offer valuable tools for maintaining academic integrity in admissions processes.

Keywords:
Artificial intelligenceAuthorshipEducationProfessionalROC curveSchool admission criteriaWriting

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Area of Science:

  • Medical Education
  • Health Professions Education
  • Academic Integrity

Background:

  • The rise of advanced AI, including ChatGPT and Google Gemini, presents challenges to the authenticity of academic submissions.
  • Previous research indicates variable success rates in detecting AI-generated text, necessitating further investigation.

Purpose of the Study:

  • To evaluate the efficacy of human readers, Recurrence Quantification Analysis (RQA), and the GPTZero AI detection tool in differentiating AI-generated from human-written personal statements.
  • To assess the implications of these detection methods for physical therapist education program admissions.

Main Methods:

  • Fifty human-written and fifty AI-generated personal statements were analyzed.
  • Analysis was conducted by two human raters, RQA for lexical sophistication, and GPTZero for AI-specific text characteristics.

Main Results:

  • Human raters achieved high agreement (κ = 0.92) and accuracy (97%-99%).
  • RQA parameters (recurrence, max line) and GPTZero parameters (simplicity, perplexity, readability) effectively differentiated between human- and AI-generated text, with areas under the ROC curve > 0.768.

Conclusions:

  • Human readers, RQA, and GPTZero demonstrate varying but significant accuracy in distinguishing AI-generated from human-written personal statements.
  • Findings underscore the importance of these methods for academic admissions and suggest future research on integrated approaches.
  • Strategies are proposed for applicants, governing bodies, and institutions to uphold integrity in admissions processes.