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Quantifying Artificial Intelligence Contribution in Academic Writing: Development of the Transparency and Reporting
Marianne B Vyas1, Lori M Rhudy2, Suzanne Weckman3
1Marianne B. Vyas, Inova Health System, Falls Church, VA.
Summary
Authors can now self-report their artificial intelligence (AI) use with the Transparency and Reporting of Artificial Intelligence Contribution for Evaluating Submissions (TRACES) instrument. This tool helps journals assess AI contributions in manuscripts.
Area of Science:
- Scientific publishing
- Artificial intelligence in research
Background:
- Increasing use of artificial intelligence (AI) in manuscript preparation presents challenges for identifying AI's extent.
- Difficulty for peer reviewers, editors, and readers to ascertain AI's role in published research.
Purpose of the Study:
- To introduce a standardized method for authors to disclose their AI utilization.
- To enhance transparency in scientific publishing regarding AI contributions.
Main Methods:
- Development of the Transparency and Reporting of Artificial Intelligence Contribution for Evaluating Submissions (TRACES) instrument.
- The TRACES instrument is an AI scoring rubric for author self-disclosure.
Main Results:
- The TRACES instrument yields a score from 0 to 40 across mechanics, writing, and illustrations.
- Higher TRACES scores correlate with increased authorial use of AI in manuscript development.
Conclusions:
- Authors across all disciplines should self-report their TRACES score upon manuscript submission.
- Journals are encouraged to mandate TRACES scores for manuscript submissions to improve transparency.
- The TRACES score indicates the level of AI use but does not dictate manuscript acceptance or rejection criteria.
