Quantifying Artificial Intelligence Contribution in Academic Writing: Development of the Transparency and Reporting of Artificial Intelligence Contribution for Evaluating Submissions Instrument.

Abstract

Background: There has been an increase in the availability and use of artificial intelligence (AI) across many professional domains, and there are traces of AI in nearly every manuscript published using modern technology. It is becoming increasingly difficult for peer reviewers, editorial teams, and journal readers to identify the degree to which authors have used AI in the development of their manuscripts.

Methods: Our research group developed an AI scoring rubric to provide authors with an opportunity to self-disclose their use of AI.

Results: The Transparency and Reporting of Artificial Intelligence Contribution for Evaluating Submissions (TRACES) instrument provides a score from 0 to 40 across 3 domains: mechanics, writing, and illustrations. Higher scores indicate increased use of AI by the authors when writing or preparing a manuscript for submission.

Conclusion: Authors in any field should self-report a TRACES score when submitting their manuscript. Journals may benefit from requiring authors to include a TRACES score when submitting a manuscript for peer review. While higher TRACES scores indicate greater use of AI, there is no specific cutoff provided to determine manuscript acceptance or rejection.