03 · What You Need to Know
The Essential Verification Checks for AI-Assisted Journal Submissions
Why AI-Assisted Submission Materials Require Independent Verification
Generative AI can produce fluent academic language without reliably distinguishing verified information from plausible assumptions. This creates a particular risk when researchers use it to prepare documents that contain factual claims, declarations, or representations about completed research.
The International Committee of Medical Journal Editors (ICMJE) emphasizes that authors remain responsible for submitted work, including material prepared with AI assistance. Its recommendations also address transparency about AI use and the need to review AI-generated content carefully.
Publisher policies similarly place responsibility for manuscript content on human authors, although specific disclosure requirements and permitted uses vary.
Verification should therefore be based on the underlying evidence rather than the apparent quality of the generated writing.
A well-written statement can still be incorrect. In academic publishing, stylistic confidence is not a substitute for evidentiary support.
Which AI-Assisted Submission Materials Should Be Checked?
Verification should cover the entire submission package, not only the main manuscript.
The following materials commonly warrant attention when AI has contributed to their preparation.
| Submission Material |
Principal Verification Concern |
Authoritative Evidence |
| Manuscript |
Accuracy of claims, methods, results, and interpretations |
Research data, analysis records, verified references, and approved manuscript |
| Abstract |
Consistency with actual objectives, methods, findings, and conclusions |
Final manuscript and analysis results |
| Cover letter |
Accurate statements about novelty, journal fit, submission status, and author approval |
Final manuscript, author confirmations, and journal instructions |
| Highlights and key points |
Overstatement or misrepresentation of findings |
Results and discussion sections |
| Keywords |
Scientific relevance and consistency with the study |
Manuscript content and journal requirements |
| Data availability statement |
Accuracy of repository links, access conditions, and restrictions |
Actual data records, repositories, consent conditions, and institutional policies |
| Author contribution statement |
Correct attribution of contributions |
Author-confirmed contribution records |
| Funding and conflict-of-interest declarations |
Truthful and complete disclosure |
Funding records and declarations from responsible authors |
| Submission metadata |
Correct names, affiliations, identifiers, and manuscript information |
Verified author records and final manuscript |
| Supplementary materials |
Consistency, completeness, confidentiality, and technical compliance |
Original research records and journal specifications |
Not every submission includes all these materials. The journal's current instructions determine which documents are required for the particular manuscript.
Verify That AI Has Not Changed the Scientific Meaning
One of the most consequential risks of AI-assisted manuscript preparation is the introduction of subtle changes in meaning.
For example, a researcher may originally write that an intervention was associated with improved student engagement. An AI-generated revision might state that the intervention caused the improvement.
The second statement makes a stronger causal claim that may not be justified by the research design.
Similar problems can occur when AI changes statistical terminology, removes qualifications, exaggerates practical significance, or presents exploratory findings as confirmatory evidence.
Researchers should compare AI-assisted revisions against the original analysis and approved findings. Particular attention should be given to numerical results, causal language, methodological descriptions, limitations, and conclusions.
Language improvement is acceptable only when the scientific meaning remains accurate.
Verify Every Reference and Citation Affected by AI
Generative AI can produce references that appear bibliographically convincing but contain nonexistent articles, incorrect publication details, mismatched identifiers, or unsupported claims.
Even when the cited publication exists, the source may not support the statement attached to it.
Authors should verify references against authoritative bibliographic records, publisher pages, DOI registration information, or the original publications.
For each consequential citation, determine whether the source exists, whether its bibliographic details are correct, and whether the cited evidence supports the manuscript's claim.
A citation manager can help organize references, but neither citation-management software nor generative AI removes the need to check evidentiary relevance.
Check That the Cover Letter Accurately Represents the Manuscript
AI-generated cover letters often contain polished statements about originality, significance, and suitability for the journal.
These statements should be evaluated rather than accepted automatically.
For example, an AI-generated letter might describe the study as the first investigation of a particular topic, claim that it provides definitive evidence, or state that all authors approved the submission.
Each statement requires a defensible basis.
A claim of being the first study should not be made without an appropriately scoped literature assessment. Claims about author approval must reflect actual confirmation. Statements about originality and simultaneous submission must be accurate.
The process of drafting a journal submission cover letter with AI can improve organization and wording, but it cannot establish facts about the research team's decisions.
Verify Data Availability Statements Against Actual Access Conditions
Data availability statements require particular care because they describe whether and how other researchers may access supporting research materials.
An AI tool might generate a conventional statement claiming that data are publicly available, deposited in a repository, or obtainable from the corresponding author upon reasonable request.
None of those arrangements should be reported unless they accurately reflect the researcher's circumstances and any applicable restrictions.
Researchers should verify repository identifiers, working links, access permissions, consent conditions, and institutional requirements.
For studies involving confidential participant information, the statement must not imply that restricted data can be released without appropriate authorization.
AI can help prepare a data availability statement, but the underlying access conditions must be established independently.
Confirm Authorship, Contributions, Funding, and Conflicts of Interest
Declarations about authorship and competing interests represent factual statements about people, responsibilities, and relationships.
AI cannot reliably determine these facts from the manuscript alone.
For author contributions, researchers should confirm that the statement accurately describes each person's actual role. Where the journal uses the CRediT taxonomy, the assigned roles should correspond to the contributors' work rather than being inferred from author order.
Funding information should match actual award records. Conflict-of-interest declarations should reflect information provided by the relevant authors and the journal's applicable disclosure criteria.
Missing information should be resolved through author confirmation, not completed using plausible AI-generated assumptions.
Check Ethics Statements and Research Registration Information
Statements about ethics approval, informed consent, research registration, and related requirements may have significant research-integrity implications.
Researchers should verify approval identifiers, approving bodies, relevant dates, consent arrangements, and registration details against the original records.
AI must not invent an approval number, assume that ethical approval was unnecessary, or claim that participants consented when the available documentation does not establish that fact.
Requirements differ across disciplines, jurisdictions, institutions, and research designs. The relevant journal policies and institutional ethics decisions should determine what must be reported.
Verify AI-Use Disclosures Against the Journal's Current Policy
Some journals and publishers require authors to disclose particular uses of generative AI in manuscript preparation or research activities.
However, disclosure rules differ. A publisher may distinguish language editing from substantive content generation, while another may require broader reporting.
Researchers should identify what AI tools were used, for which tasks, and whether those uses fall within the journal's disclosure requirements.
Where disclosure is required, the statement should accurately describe the assistance provided. It should not imply that AI performed analyses, verified sources, or made research decisions that it did not actually perform.
Authors should consult current official policies rather than relying on an AI-generated description of the rules.
Check for Confidentiality and Data Protection Problems
Verification should also consider how AI tools were used during manuscript preparation.
Uploading confidential manuscripts, identifiable participant data, unpublished findings, proprietary information, or restricted research records may create confidentiality or data protection concerns.
The appropriate safeguards depend on institutional policies, contractual terms, consent arrangements, applicable law, and the AI service's data-handling conditions.
Researchers should establish whether the use of the service was authorized and whether any additional action is required if restricted information was exposed.
Removing sensitive information from the final manuscript does not necessarily resolve a previous unauthorized disclosure to an external AI system.
Ensure That Every Submission Document Uses the Same Final Information
AI-assisted submission materials are often prepared at different stages of manuscript development.
The abstract may be generated from an early draft, while the cover letter reflects a later version. Keywords may refer to a research question that has since been revised. The submission portal may contain an earlier title.
These inconsistencies are easy to overlook because each document may appear reasonable when examined separately.
A final verification should compare the manuscript, cover letter, highlights, declarations, supplementary files, and journal submission metadata against the same approved manuscript version and verified records.
Check Current Journal Requirements Rather Than AI's Recollection
Submission verification is incomplete if it checks factual accuracy but ignores the journal's actual requirements.
Authors should confirm the intended article category, file specifications, manuscript structure, required declarations, reporting checklists, and submission-system fields.
Generative AI may assist with comparing the manuscript against author guidelines, but its assessment cannot guarantee that every requirement has been identified.
The journal's current official instructions remain the governing source for submission compliance, subject to applicable ethical and legal obligations.
06 · What This Means for You
Build a Risk-Based Final Verification Process
A useful verification process distinguishes between routine formatting checks and statements that could materially affect the scientific record.
Not every AI-assisted sentence requires the same degree of scrutiny. However, claims about research findings, ethical approval, data availability, authorship, funding, and conflicts of interest should receive direct verification against authoritative evidence.
A practical verification framework
If AI assisted only with language or formatting
Compare the revised text with the original meaning and check compliance with journal formatting requirements.
If AI generated or revised factual claims
Verify each consequential statement against research records, original sources, and the final analysis.
If AI drafted ethics, funding, authorship, or disclosure statements
Confirm the underlying facts with the responsible authors and relevant records before approving the wording.
If AI contributed to data processing or analysis
Validate the analytical outputs, document the procedures, and ensure that the reported findings are supported by the verified results.
If AI advice conflicts with the journal's official requirements
Follow the current applicable instructions and obtain clarification when official sources are inconsistent.
Assign Verification Responsibility Before Submission
For multi-author research, the corresponding author may coordinate the final submission review, but verification should involve the people best positioned to confirm the relevant information.
For example, the researcher responsible for statistical analysis should confirm numerical results and analytical descriptions. Authors should verify their own affiliations and contributions. The appropriate research team members should confirm ethics documentation and data availability conditions.
This division of responsibility can reduce the risk that one author approves statements without access to the underlying evidence.
The exact allocation of responsibilities should follow the journal's authorship policies and the research team's agreed procedures.
Use a Final Submission Register
A practical final review can use a simple register containing the submission material, the item requiring verification, its authoritative source, the person responsible, and its verification status.
For consequential items, record whether a discrepancy was found and how it was resolved.
The purpose is not to create unnecessary administrative work. It is to ensure that important claims and declarations have actually been checked rather than assumed correct because the documents look finished.
Watch Out
Do not ask AI to invent missing approvals, declarations, repository links, author confirmations, or methodological details merely to complete the submission package. If required information is unavailable, resolve the underlying issue before submission.
Before the final submission action, review the assembled package as a whole. The manuscript, cover letter, metadata, declarations, and supporting files should tell the same accurate account of the research.
