A Practical Guide to Reviewing AI-Generated Text in Academic Work

AI tools are now part of many academic writing workflows. Students may use them for brainstorming or language editing, researchers may use them to refine manuscripts, and journal authors may use AI-assisted tools during different stages of writing. But before submitting academic work, it is important to understand how AI was used, whether the final document reflects the author’s own work, and whether the use complies with the relevant policy.

This is where AI detection before submission can be useful. An AI detector can provide a signal about whether sections of text resemble AI-generated writing. However, a detection result should not be treated as definitive proof of authorship. AI detectors can produce both false positives and false negatives, so their results need to be interpreted alongside the writing process and other forms of review.

A practical pre-submission workflow combines AI detection with policy checks, manual review, source verification, and an understanding of how the document was created.

Why use AI detection before submission?

The purpose of checking for AI before submission is not simply to achieve a particular detector score. The more important question is whether the work is ready to be submitted under the rules that apply to it.

For students, this may mean checking whether AI-assisted writing is permitted and whether its use needs to be disclosed. For researchers and journal authors, it may involve reviewing whether AI was used for language editing, content generation, research assistance, or substantial rewriting.

Journal policies can differ. For example, Elsevier asks authors to disclose certain uses of generative AI during manuscript preparation while distinguishing substantive AI assistance from basic language improvements. Authors remain responsible for the final content. Elsevier’s generative AI policy

An AI detection check can therefore be one part of a broader pre-submission review. It can highlight passages that deserve attention, but it cannot determine whether a particular use of AI was acceptable.

A practical AI detection before submission workflow

A sensible workflow can be broken into five steps.

1. Check the applicable policy first

Before running an AI detector, identify the rules that apply to your work.

For an assignment, review your institution, department, or course policy. For a manuscript, check the journal’s current author guidelines and AI policy. Look for requirements related to AI-generated content, AI-assisted editing, disclosure, authorship, and acceptable uses of AI tools.

This step should come first because a detector cannot tell you whether your use of AI is allowed. A low AI detection result does not make an otherwise undisclosed or prohibited use acceptable.

2. Review how AI was used

Next, consider what role AI played in creating or revising the document.

Ask:

• Did AI generate any original text?
• Was it used to brainstorm ideas or create an outline?
• Did it rewrite or substantially restructure your writing?
• Was it used only for grammar, spelling, or language correction?
• Did it assist with research or analysis?
• Did you independently verify its claims and references?

These uses are not equivalent. A document that received language corrections from an AI tool has a different writing history from one in which an AI system generated substantial sections.

Keeping drafts, notes, tracked changes, research records, and earlier versions can help you understand and demonstrate that process.

3. Run an AI detection check

Once you understand how AI was used, run the document through an AI detector.

The purpose is to identify AI detection results that may warrant further review, rather than to prove that every flagged passage was generated by AI.

With Trinka AI Detector, writers can review AI detection results as part of their pre-submission process. If a section receives a higher AI likelihood, examine that section in context before deciding whether any action is necessary.

This distinction is important because AI detectors evaluate patterns in the final text. They do not have access to the complete history of how the document was written, edited, researched, or revised.

4. Review flagged sections manually

If the detector highlights particular sections, return to the writing itself.

Check whether the passage:

• accurately represents your research or argument
• reflects your intended meaning
• contains claims supported by reliable sources
• uses appropriate terminology for your field
• includes citations that support the claims
• was generated or substantially changed using AI
• requires disclosure under the applicable policy

Do not automatically rewrite a passage simply because it receives a high detection result. The objective should be to understand why the passage was flagged and whether there is an actual issue with the writing or the way AI was used.

This is particularly important for academic writing, where formal and structured language may sometimes resemble patterns associated with AI-generated text.

5. Complete a final submission review

AI detection should be one of the final checks, not the entire submission process.

Before submitting, review the document for factual accuracy, citations, references, language, formatting, and compliance with the relevant requirements. If AI was used, confirm whether that use needs to be disclosed.

For journal authors, this final step should include checking the specific publisher or journal policy rather than relying on a general assumption about acceptable AI use.

The final review should answer three basic questions:

• Is the content accurate?
• Does the submission follow the applicable policy?
• Can I explain how the work was created and what role AI played?

What should you do if AI detection results are high?

A high AI detection result does not automatically establish that a document contains inappropriate AI-generated content. Similarly, a low result does not prove that a document was entirely written without AI assistance.

Instead, treat the result as a reason to investigate.

Look at the specific sections identified by the tool. Compare them with your drafts, notes, and previous versions. Consider whether the text reflects your own research and intended argument. If AI substantially contributed to the passage, check whether that use needs to be disclosed.

For students, this can help identify sections that need closer review before submission. For researchers and journal authors, it can provide another opportunity to check whether the manuscript aligns with the journal’s requirements.

The goal of AI detection before submission is therefore not to produce a particular score. It is to identify areas that deserve attention while there is still time to review them.

AI detection is one part of a broader review

An AI detector can provide useful information, but it cannot reconstruct the complete writing process from the final document. This is why AI detection results are best considered alongside drafts, revisions, research notes, citations, and the author’s understanding of the work.

Trinka AI Detector can be used as one step in this process, alongside grammar review, citation checking, source verification, and a final policy review. The detector result is a signal to consider, not a standalone judgment about authorship.

A thoughtful pre-submission workflow ultimately focuses on the quality, accuracy, transparency, and policy compliance of the final work.

Final checklist before academic submission

Before submitting, ask yourself:

1. Have I checked the relevant AI-use policy?
2. Do I understand where and how I used AI?
3. Have I reviewed my AI detection results?
4. Have I manually checked sections that require attention?
5. Have I verified citations, references, and factual claims?
6. Do I need to disclose my use of AI?
7. Can I take responsibility for the final document?

Using AI detection before submission as part of this broader process can help students, researchers, and journal authors identify potential issues early. More importantly, it keeps the focus where it belongs: on understanding how the work was produced, reviewing the final document carefully, and meeting the requirements that apply to the submission.