Detecting Artificial Intelligence–Generated Versus Human-Written Medical Student Essays: Semirandomized Controlled Study
The findings suggest that both medical and humanities experts were able to identify ChatGPT-generated texts in medical contexts, with their decisions largely based on linguistic attributes. The accuracy of identification appears to be independent of experts' familiarity with the text content. As the decision-making process primarily relies on linguistic attributes-such as stylistic features and text coherence-further quasi-experimental studies using texts from other academic disciplines should be conducted to determine whether instructions based on these features can enhance lecturers' ability to distinguish between student-authored and AI-generated work.
Authors
- Maria Berger (ORCID: https://orcid.org/0009-0008-6191-9623)
- Thomas Luecke (ORCID: https://orcid.org/0000-0001-6875-6582)
- Elena Enax‐Krumova (ORCID: https://orcid.org/0000-0002-6162-9414)
- Marianne Tokic
- Berin Doru
- Judith Schönhoff
- Christoph Maier (ORCID: https://orcid.org/0000-0003-1212-744X)
- Johanna Sophie Busse
- Steffen Hessler
Institutions
- University Hospitals of the Ruhr-University of Bochum (DE)
- Institute for Prevention and Occupational Medicine (DE)
- St. Josef-Hospital (DE)
- BG University Hospital Bergmannsheil Bochum (DE)
- Ruhr University Bochum (DE)
Publication Details
- Journal
- JMIR Medical Education
- Published
- 2025-01-16
- DOI
- https://doi.org/10.2196/62779
- Citations
- 13
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 8.51