How to use AI to detect and reduce the AI-generated ratio in papers?

Employing AI to identify and mitigate the proportion of AI-generated text in academic papers is feasible through specialized detection tools and editorial strategies. These techniques help researchers maintain academic integrity and ensure original contribution. Detection primarily relies on analyzing text for AI-associated linguistic artifacts, such as low perplexity (predictability), specific syntactic patterns, or statistical inconsistencies distinguishing machine output. Tools range from publicly available detectors analyzing fluency features to advanced stylometric classifiers trained on known AI and human corpora; however, accuracy varies significantly between tools and can be confounded by editing tactics like paraphrasing. Crucially, these tools provide supplementary evidence, not definitive proof, and require cautious human interpretation within a broader context of manuscript evaluation. Their effective scope typically focuses on recent models and specific types of content generation. Implementation involves several key steps: (1) Preprocessing submissions by running text through selected detection tools (e.g., utilizing APIs or licensed software), flagging segments with high AI probability scores. (2) Critically reviewing flagged content to assess its necessity and integration; (3) Revising identified sections substantially to incorporate original analysis, specific data interpretation, critical appraisal, or deeper scholarly argumentation that reflects the author's unique perspective. Finally, (4) verifying the revised text ensures original contribution while transparently acknowledging legitimate AI use, such as for initial drafting assistance, adhering to journal policies. This process enhances authenticity without prohibiting responsible AI utilization.