Momentum around AI disclosure in scholarly research is unmistakable. What was once encouraged is rapidly becoming required. Authors are being asked to state whether AI tools were used in drafting, analysis, or content preparation. Editorial policies are evolving. Submission systems are being updated. Transparency is gaining ground.
Yet a new challenge is emerging. Disclosure without classification remains ambiguous. A manuscript marked as “AI-assisted” reveals very little. Did the author use AI for language editing, statistical modeling, image enhancement, data cleaning, literature summarization, or full-text drafting? Each of these carries different implications for review, reproducibility, and integrity. Without structured differentiation, disclosure risks becoming a label rather than a meaningful signal.
The problem is not author intent. It is the lack of shared vocabulary. Broad disclosure statements fail to support consistent editorial decisions because they do not distinguish between fundamentally different types of AI involvement. Language polishing does not raise the same questions as synthetic data generation. Image enhancement is not equivalent to automated figure creation. Treating all AI use as a single category oversimplifies a complex reality.
This is where classification becomes essential. Publishers are now at a point where AI disclosure must evolve into AI taxonomy. Clear, standardized categories of AI use can transform transparency from a narrative statement into structured metadata. Such taxonomies could differentiate between writing support, translation, code generation, data analysis, modeling, image processing, content summarization, and workflow automation. They could also distinguish between assistive use and generative substitution.
Classification enables consistency. Editors reviewing submissions gain clearer signals about where closer scrutiny may be required. Peer reviewers understand what role AI played in the research process. Production and metadata teams can capture structured information that supports indexing, archiving, and future discovery.
Beyond immediate editorial needs, classification lays the groundwork for scalable governance. As AI becomes embedded across research workflows, oversight cannot rely solely on manual interpretation of disclosure statements. Structured categories allow publishers to track patterns of AI use across disciplines, identify emerging risks, and align policies with funder and institutional expectations. They also provide a foundation for future compliance requirements that may arise as regulators and research bodies formalize AI standards.
Importantly, classification is not about restriction. It is about clarity. Researchers are already integrating AI into their work in diverse ways. Providing a clear framework for describing that use supports both innovation and accountability. It reduces uncertainty for authors and increases confidence for readers.
Disclosure was the necessary first step. It signaled that AI use should no longer remain implicit. The next step is refinement. Without classification, disclosure remains incomplete. With it, publishers can move from acknowledging AI to governing it with precision.
As AI becomes part of the research record, structured transparency will define its credibility. The missing layer is now visible. Classification is the next move. Know more
Knowledgespeak Editorial Team
