"Was this written by AI?" is the question schools, publishers, and teams keep asking, and the honest answer from detection tools is usually a shrug dressed up as a percentage. There is a more reliable approach available to anyone building writing software: stop guessing, and start recording.
The idea: provenance beats detection
AI-text detectors try to infer authorship from statistical fingerprints in the final text. They are unreliable in both directions: they flag careful human writers and miss polished machine output. The deeper problem is conceptual. They are trying to reconstruct history from a single artifact.
Revision provenance takes the opposite path. If the writing environment saves every revision and knows which saves involved AI assistance, then the history is not inferred at all. It is recorded. Questions like "what did this document look like before the AI pass?" or "which passages did the assistant rewrite?" become lookups, not guesses.
That shift enables better features:
- Contribution maps. Show exactly which passages were rewritten, added, or removed in an AI-assisted revision, with before-and-after comparisons.
- Honest attribution. Instead of "this document is 37% AI" (a meaningless claim), you can say how much of the measurable improvement happened during AI-assisted saves versus human ones.
- Defensible records for students and researchers. A writer can show their original questions, arguments, and groundwork existed before any AI refinement, and a supervisor can discuss writing development without relying on an opaque detector score.
The trade-offs
Provenance only works inside the system that recorded it. Paste in text from outside and the chain breaks. It also requires the platform to be trustworthy about its own records, and it produces developmental analytics, not verdicts: a revision history cannot tell you whether assistance was appropriate, only where it happened. Treating it as a disciplinary instrument would repeat the detector mistake with better data.
How we approached it at Andika
Andika, the knowledge workspace we are building, keeps a full revision history per document, and AI-assisted saves are marked as such in that history. Two features build on it:
Document History now includes a contribution map for AI-assisted revisions: passages rewritten, added, or removed, each with a before-and-after comparison you can inspect.
Grade Me compares the original draft with every saved revision and reports how language, structure, and ideas evolved: voice retention, semantic similarity, intent and entity preservation, lexical diversity, and reading-grade estimates. The report separates improvement from human revisions from improvement during recorded AI-assisted saves, and every AI-assisted revision is explainable: pick it on the timeline and see the exact passages that changed.
We are deliberate about what this is not. It is not a plagiarism verdict, an institutional mark, or a claim that some percentage of a document "belongs to AI." It is developmental analytics, built on recorded history rather than text statistics. AI should not erase the author from the page; it should help the author see where their work became stronger.
Adapted from posts on the Andika blog.
