Why human experts still outperform AI in proofreading subject-specific language for research publications

Researchers increasingly turn to generative AI for writing support, and large language models (LLMs) now produce fluent summaries, edits, and suggestions in seconds. Yet recent evaluations show significant risks when these tools handle subject-specific language that is, terminology, methodological detail, and nuanced claims tied to a particular discipline. A large-scale study found that LLMs were nearly five times more likely than humans to overgeneralize scientific conclusions when summarizing research, raising clear concerns about relying on AI alone for proofreading and technical language checks.

This article explains what makes subject-specific proofreading different from general copyediting, why humans still outperform AI for technical and disciplinary language, and how researchers can combine AI speed with human expertise to produce rigorous, publication-ready manuscripts. Practical tips and service options are provided to help authors choose the right workflow for their submission goals.

What subject-specific proofreading means and why it matters

Proofreading in publishing traditionally focuses on transcription errors, formatting, and final read-throughs to catch typos and layout problems; in research publishing, it must also preserve discipline-specific meaning. Subject-specific proofreading addresses domain terminology, methodological precision, statistical reporting, compliance with field conventions, and how claims are framed relative to evidence.

In research manuscripts, small changes to phrasing can alter the scientific claim. For example, converting a cautiously worded result about a limited population into a broader recommendation. Such shifts can mislead reviewers and readers, increase the risk of desk rejection, or introduce ethical concerns in fields like medicine. Accurate subject-specific proofreading therefore requires not only language fluency but also contextual domain knowledge and an ability to check methods and references.

Benefits and current strengths of AI in academic proofreading

AI tools provide clear value in early-draft polishing and routine error correction. They excel at:

• Rapid grammar and style corrections across long documents.
• Standardizing formatting and flagging obvious inconsistencies.
• Speeding up iterative revisions so authors can focus on substantive content.

These strengths make AI a useful first pass for time-pressed authors and for non-technical layers of editing. However, speed and fluency do not guarantee domain accuracy or preservation of nuanced scientific meaning.

How AI falls short on subject-specific language

Three interrelated limitations explain why AI lags behind skilled human experts when handling technical content.

1. Overgeneralization and scope errors
   LLMs tend to generalize results beyond what the original text supports. A Royal Society Open Science analysis of 4,900 AI-generated summaries found that many models produced broader conclusions than warranted and were nearly five times more likely to overgeneralize compared with human summaries. Newer model versions sometimes performed worse on this measure. This pattern illustrates an intrinsic risk: AI may remove critical qualifiers, caveats, or population constraints that matter for scientific accuracy.

2. Hallucinated or inaccurate references and factual errors
   Generative models can fabricate plausible-looking citations, misreport statistical details, or invent references. Comparative evaluations of LLMs in literature-search and citation tasks found high hallucination rates and low precision for generated references, indicating that any AI-supplied bibliography or in-text citation must be thoroughly verified by a human. These errors are especially problematic in systematic reviews, clinical research, and disciplines where precise citation and provenance are essential.

3. Limited ability to assess methodological rigor and discipline-specific conventions
   AI lacks the implicit knowledge and judgment that subject-matter experts apply when evaluating experimental design, statistical reporting, or discipline-specific phrasing. It may suggest rewordings that reduce technical clarity or fail to spot methodological inconsistencies a specialist would flag. Human experts, particularly editors with doctoral-level training or clinical backgrounds, can contextualize language choices within the conventions and expectations of the target journals.

Real-world comparison: AI vs. human editing

Controlled experiments and service evaluations illustrate the performance gap. A head-to-head proofreading experiment that compared a popular AI model with a professional human editor found that while both improved readability, the human editor produced more extensive, reliable changes, preserved citation accuracy, and supplied clear explanations for edits capabilities that AI did not match. The human editor’s ability to explain changes also helps authors learn and prevents inadvertent alteration of scientific meaning.

Why human experts still outperform AI: core strengths

• Domain expertise: Subject-matter editors recognize discipline-specific idioms (e.g., reporting effect sizes, clinical endpoints, or taxonomic conventions) and can judge whether phrasing accurately conveys scientific meaning.
• Contextual integrity: Humans assess whether a change preserves nuance such as qualifiers, limitations, and proper hedging essential in responsible scientific communication.
• Reference verification: Expert editors cross-check citations, validate DOIs, and identify mismatches between claims and cited literature.
• Interaction and feedback: Experienced editors ask clarifying questions and explain the rationale for edits, helping early-career researchers improve future writing.

Common mistakes to avoid when using AI for research proofreading

• Accepting AI-generated citations or bibliographies without verification.
• Allowing AI to rephrase methods or results without ensuring the original intent and scope are preserved.
• Using AI-only edits for manuscripts intended for clinical or high-impact publication.
• Neglecting to instruct editors which sections were AI-assisted.

How to combine AI and human expertise: practical workflows

An effective proofreading workflow leverages AI for speed while relying on human experts for subject-specific assurance. Recommended sequence:

1. Run an AI pass to correct grammar, punctuation, and low-level style inconsistencies.
2. Use an expert human editor preferably a subject-matter specialist for substantive editing: verify methodology language, check claims against citations, and ensure compliance with journal conventions.
3. Perform a final human proofread focused on formatting requirements and journal-specific style (including reference formatting and cover letter drafting).

When to choose human subject-matter editing

• The manuscript contains specialized terminology, complex methods, or field-specific reporting standards.
• The work is intended for high-impact or clinical journals where precision alters interpretation.
• The author used AI for drafting and needs validation of factual accuracy and citations.
• Peer reviewers previously flagged methodological clarity or misinterpretation.

Tips for authors: how to maximize editorial value

• Prepare a short note for your editor that lists model organisms, key terminology, statistical tests, and any parts previously revised by AI.
• Highlight any sections where a change in phrasing would alter interpretation (results, conclusions, limitations).
• Request a subject-area match when ordering professional editing an editor with domain expertise increases accuracy and preserves nuance.
• Keep a versioned track of AI suggestions to help the human editor identify potential hallucinations or introduced errors.

How to choose the right editing service

Professional editorial services that combine AI tools with human subject-matter expertise offer pragmatic solutions. For example, hybrid workflows use AI for an initial pass and then have native-English, PhD-level editors review and correct AI-introduced errors, verify scientific content, and match journal expectations. These services typically offer tiered options copyediting, substantive editing, and scientific developmental editing tailored to high-tier journals so authors can choose the level of technical review appropriate to their manuscript.