AI Is Every ESL Researcher's First Editor. It Shouldn't Be the Last.

Being an ESL academic is demanding. You might be developing original research, learning new skills and concepts in your field, or working to understand complex writing conventions in your discipline… and doing all of it in your second or third language. This takes real effort and skill, and ESL researchers are increasingly turning to AI tools to help close the gap.

AI is pretty good at catching common ESL writing errors. Give an AI tool a paragraph with article mistakes, awkward phrasing, or subject–verb disagreement, and you’ll see immediate improvements. That’s why, for many writers—especially ESL academics working under intense pressure—AI is genuinely useful as a fast and accessible first-pass editor. It can clean up grammar, smooth out sentences, and make writing sound more fluent.

But academic writing is not simply a matter of grammatical correctness. A paper can be grammatically polished and still feel repetitive, vague, unfocused, or rhetorically unconvincing. Increasingly, reviewers are seeing AI-generated or AI-assisted writing that sounds fluent on the surface but communicates surprisingly little.

What AI Catches Well

AI can be very good at identifying sentence-level problems such as

• article errors (“a,” “an,” “the”)
• subject–verb agreement
• punctuation mistakes
• obvious preposition errors
• repeated wording
• sentence fragments and run-ons
• awkward literal translations

These kinds of corrections are often acceptable and necessary, but in some contexts, they don't capture what the author intended to say. Consider this example:

Original: “The results shows significant increase in patients.”

AI correction: “The results show a significant increase in patients.”

While there clearly is a grammar problem, there's also a precision problem that an AI tool wouldn't necessarily catch. In other words, the AI tool was able to revise the verb to agree with the noun, but it didn't have the context to understand that an added indefinite article ("a") wasn't the ideal correction.

Beyond the necessary subject-verb agreement revision, an experienced human editor would ask: a significant increase in what, exactly? From their perspective, the sentence is more likely missing an important variable than reporting a patient count increase. 

Where AI Struggles

Argument Structure and Logical Flow

AI evaluates individual sentences more effectively than arguments.

A paragraph may be grammatically correct while still failing to develop a clear line of reasoning. Sometimes the same point is repeated several times using slightly different wording. In other cases, the conclusion does not fully follow from the evidence presented. AI often misses these problems because each sentence appears acceptable in isolation.

In contrast, human reviewers don't read sentence by sentence. They read for overall progression, emphasis, coherence, and logical development, asking whether each paragraph advances the central argument and whether that argument holds up.

Cross-Linguistic Writing Patterns

Experienced academic editors frequently recognize recurring writing patterns associated with particular first-language backgrounds. These aren't always grammar problems. Often, they're issues of communication when writing habits from other traditions are translated into English and interact with English academic conventions.

For example, Arabic rhetorical traditions sometimes use repetition for emphasis and reinforcement. In English-language academic writing, however, repetition can be interpreted as redundancy. AI tools often leave this repetition untouched because the sentences are grammatically correct.

Finnish L1 authors may use less metatextual signposting­­—think "this section shows," "as I will argue," or "in other words"—that Anglo-American readers rely on to follow an argument. Research on Finnish ESL academic writing has shown that Finnish authors are less likely to orient their readers this way, which can leave English-language reviewers working harder to follow a text than the writer intended.  

Recognizing these patterns, understanding the reasoning behind them, and adjusting them while preserving the writer's meaning is precisely what experienced editorial reviewers do.

Register Mismatch

AI also often fails to register inconsistency, particularly when a shift in tone accumulates throughout a document. Consider these examples:

• The methods section of an article can be consistently written in a formal and passive tone, but the claims in the discussion section are presented with “I think” and “it seems like."
• Formal nominalization and passive voice are used in the methods section, which suddenly shifts to active voice with plain verb use (“we did X”).

These examples may be grammatically correct, but the shift creates tonal inconsistency. AI tools frequently overlook this kind of mismatch because they evaluate sentences individually rather than considering the overall rhetorical tone of the document. Human editors, however, revise for consistency of voice and to fulfill audience expectations.

Hedging

Academic writing requires caution. Scholars rarely make absolute claims, and appropriate hedging is an important part of responsible research writing.

For example, this sentence is appropriately cautious:

“These findings suggest a possible relationship between sleep quality and cognitive performance.”

But AI sometimes encourages a different kind of writing: prose so heavily hedged that it becomes vague or evasive.

For example,

“It may perhaps be suggested that there could be some form of relationship…”

The sentence no longer sounds careful. It sounds uncertain, even though its grammar is correct. Appropriate hedging demonstrates intellectual honesty, while overhedging errs on the side of caution rather than clarity.

Unnecessary Padding

A recurring problem with AI-generated writing is that it can be grammatically polished but conceptually empty. Think inflated phrasing, abstract nouns, generic transitions, and long sentences that fail to make a clear point. AI has become surprisingly good at producing paragraphs that sound credible but actually lack memorable thought.

The vocabulary of AI-padded text is surprisingly consistent. Research tracking word frequency in academic publications has documented a sharp rise in certain words after the release of ChatGPT—language that rarely appeared in human-written scholarship before 2023. In 2024, one study found that words like "commendable," "meticulous," and "intricate" showed up to 34-fold increases in AI conference peer reviews.

Paragraph Cohesion

Individual sentences can make sense, but the paragraph, as a whole, doesn't build logically. A typical example might look something like this:

• Sentence 1 introduces a topic.
• Sentence 2 presents a loosely related fact.
• Sentence 3 jumps to a conclusion that has not been fully supported.

The grammar may be flawless, but the reader is left making connections that the author should have made.

Strong academic writing guides the reader through an argument step by step. Human editors read for that progression. They ask questions such as

• Does this paragraph develop a central idea?
• Does the evidence support the conclusion?
• Does each sentence build naturally from the previous one?
• Does the writer sound confident, precise, and aware of audience expectations?

What Editorial Queries Reveal to ESL Authors

One of the most important differences between AI tools and experienced human editors is not what they correct but what they ask.

An experienced academic editor shouldn't just revise sentences. They must also read as an informed, critical audience member and raise questions where meaning, logic, or emphasis is unclear. Editorial queries, such as the ones below, should reflect a real reviewer's cognitive response:

• You've introduced this finding but haven't explained why it matters for your argument. Can you make that connection clear?
• You’ve written "Several studies suggest" here, but there are no citations to indicate what studies you’re referring to. Please include citations to sources that support this point.
• Would a reviewer outside your immediate subfield follow this without additional context? Consider whether more background should be provided on this subject.
• From my perspective, this is the most important finding, but it appears in the third paragraph of this section. Could it be moved earlier for greater impact?
• This claim appears to be stronger than the evidence presented. Can the claim be softened or supported with additional evidence?

These queries are interventions based on reading experience, disciplinary expectations, and familiarity with how arguments are evaluated in academic publishing. Without this kind of feedback, writers may assume certain connections are obvious or that a point has been sufficiently established, when a reader in another academic context would expect additional explanation or signposting.

AI tools rarely provide this kind of feedback. They tend to optimize for surface-level clarity and fluency rather than reader response. In other words, they can improve how a sentence looks without engaging with how an argument will be understood, questioned, or evaluated by reviewers.

Human editors, by contrast, act as a proxy for the reader. They don’t just ask whether the writing is correct—they ask whether it will work in the real context of academic evaluation.

AI Can Be the First Editor, but It Shouldn't Be the Last

AI is an extremely useful "first-step" writing tool. It can catch grammar errors, smooth awkward phrasing, and help ESL writers produce cleaner drafts faster than ever before.

But AI shouldn't be the final editor. Effective academic communication involves more than fluent sentences and requires what AI consistently misses: awareness of argument progression, rhetorical conventions, discipline-specific expectations, cultural context, and how a reviewer will engage with a manuscript.

This is the engagement that an experienced human editor can provide. In addition to correcting grammar and spelling, they stress-test your manuscript's argument and flag what a reviewer will question before submission.

Know how your manuscript will be read before you submit.
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