Review Article
AI-Assisted Academic Writing in Non-Native Researchers
From Language to Argument
2026 年 32 巻 2 号 p. 46-50
詳細
2026 年 32 巻 2 号 p. 46-50
Generative artificial intelligence (AI) is increasingly used in academic writing, especially by non-native researchers. AI helps non-native researchers to improve grammar, fluency, and overall readability, and reduce the burden of writing in English. However, its influence on the quality of argumentation has not been well discussed. This review argues that AI improves language but weakens argumentation through two mechanisms: linguistic smoothing and the rapid spread of formulaic vocabulary and expressions. Better English does not necessarily produce clearer arguments. Similar expressions are observed repeatedly, leading to more uniform writing. Recent trends in academic writing further amplify this problem. Clear and direct claims are now more strongly expected, and authors are required to control the strength of their statements. Non-native researchers often struggle to apply emphasis at the word, sentence, and contextual levels when editing AI-generated English. This review discusses the potential risks of AI-assisted writing, including a loss of emphasis, excessive verbosity, and “AI-like” uniformity. AI should not be used to generate arguments, but to evaluate them as an external reader. The careful and critical use of AI is essential to maintain the quality and integrity of academic writing.
Can better English produce worse science? This question lies at the center of the emerging tension in academic publishing. Many non-native researchers have received reviewer comments stating that their English is insufficient, even after professional editing. Generative artificial intelligence (AI) has substantially reduced language barriers for non-native researchers1–3). Such a development is widely regarded as beneficial for non-native researchers. The narrative review takes a paradoxical stance on this perception. Fluency and argumentation are not the same. This review hypothesizes that AI-assisted writing may improve fluency while simultaneously weakening the argumentative core of manuscripts. In this review, “argumentation” refers to the clarity and explicitness of central claims, coherence of the logical structure, and capacity to anticipate and address potential counterarguments.
This prediction is based on two main factors. The first is the smoothing of expressions by generative AI. Recent AI models can generate fluent and grammatically correct English. Uncritical use of such output can result in a more uniform writing style, with reduced variation in tone and logical emphasis4, 5). This homogenization may also reduce the variability in hedging and boosting strategies, which are essential for calibrating the strength of claims in academic writing. Therefore, it is hypothesized that the text may appear fluent but monotonous, lacking rhetorical emphasis at the word, sentence, and discourse levels, even if it is grammatically correct5). This ultimately undermines the persuasiveness of the manuscript. These effects can also occur in native speakers. For non-native researchers, the problem may be larger. They may have less stable control of expression and more difficulty adjusting the strength of their claims.
The second factor is the rapid spread of formulaic vocabulary and expressions through the use of generative AI. Traditionally, the existing literature has served as an important learning resource for non-native researchers, and the process of referencing and adapting expressions has contributed to the development of writing skills. With the widespread use of AI, expressions from past literature are quickly aggregated and reformulated5). Consequently, specific phrases and expressions (e.g., “crucial”, “notably”, or template-like expressions such as “It is important to note that…”) may lose their distinctiveness at an accelerated pace6). Recent corpus-based analyses have shown that the use of such stylistic expressions has increased markedly since the introduction of large language models6). This may indicate that these expressions are beginning to function as markers of AI-assisted writing. Simultaneously, even properly cited phrases that closely resemble the original sources may still be flagged by automated plagiarism detection systems. Non-native researchers who have relied on prior literature as a form of “textbook” may therefore face an increased risk of being misidentified as using AI, even when they employ previously standard academic expressions.
The style of academic writing has evolved in recent years. There is now a stronger expectation for authors to clearly articulate their positions and claims7, 8). In the AI era, this trend may become even more pronounced, leading to the development of new evaluation criteria. In particular, greater importance is now placed on the clarity of the argument structure and the ability to address counterarguments explicitly. Authors are expected not only to present their claims, but also to control how strongly they are expressed. Balancing hedging and emphasis is not a trivial task, especially for non-native researchers. The conceptual mechanism proposed in this review is illustrated in Fig. 1.
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Manuscript preparation requires substantial effort from non-native researchers. In practice, they often spend extra time finding the right words, fixing grammar, and adjusting how they express their ideas. This can make the writing process longer than that for native speakers. In addition, non-native researchers may face extra costs for language editing9, 10).
Generative AI can rapidly improve the quality of English expression through functions such as grammatical corrections and sentence restructuring. This helps non-native researchers produce manuscripts that meet a certain level of linguistic quality with less effort than before. Furthermore, generative AI is particularly useful in the early stages of writing, including creating outlines, reorganizing paragraph structures, and drafting abstracts. It can also be applied to high-workload practical tasks, such as generating titles and preparing response letters to reviewers of academic papers. These functions reduce the time required to structure a manuscript and enable more efficient completion of academic writing. It has been suggested that reviewers may unconsciously equate the naturalness of English with the overall quality of a manuscript11). Moreover, the presence of multiple spelling errors has been shown to significantly lower the perceived competence of the author12). Thus, researchers with limited English proficiency should consider accurate and fluent English expression to be advantageous.
About two to three decades ago, the introduction in biomedical papers often consisted of a straightforward summary of prior studies, with the discussion providing a relatively cautious interpretation. Recent abstracts are no longer simple bullet-like summaries but increasingly serve to present the overall structure of the study at an early stage8). Recent studies have noted several changes in language use. Hedging appears to be less frequent, while more direct and sometimes emphatic expressions are increasingly observed. Sentences also tend to be shorter, although noun-based expressions have become more common. These tendencies suggest a shift away from more cautious and reserved descriptions toward a more direct style writing13). In addition, words such as novel, critical, and key are now used more often, possibly reflecting growing competition and evaluation pressures14). The use of promotional language is associated with higher citation counts and increased media attention8). The results suggest that linguistic style is not merely a matter of expression, but it may also influence how research is perceived and evaluated.
This tendency may be related to several factors, such as increased competition in publishing and changes in journal editorial policies. Simultaneously, as large language models increasingly standardize common expressions, authors may find it necessary to state their positions more directly to remain distinctive. The future development of this trend remains unclear. The evaluation of academic writing may also change in response to these shifts.
When generative AI is not used carefully, it may affect both the logical structure and strength of arguments. This can reduce the overall quality of manuscripts. For example, AI-generated text is sometimes excessively verbose. It can also obscure the main focus and weaken central claims. In addition, generative AI may present nonexistent references or inaccurate bibliographic information in a plausible manner1, 2). Such errors can immediately undermine the credibility of a manuscript unless all cited sources are carefully verified by the author.
In practice, AI tools may have access only to a limited subset of open-access journal articles. Therefore, summaries sometimes combine findings from different groups in ways that appear coherent at first glance. For experts, this type of mosaic-like interpretation can be misleading, even when it appears to be reasonable.
For non-native researchers, referring to expressions used in the existing literature and partially reusing them has traditionally been an important way of learning academic writing. However, in recent years, automated detection systems have increased the risk that similarity to prior texts may be interpreted as plagiarism. Rephrasing well-established statements in entirely new forms is not always easy for non-native authors15, 16). Consequently, non-native researchers may increasingly rely on AI for language support. However, evaluating the quality of AI-generated English is not straightforward for non-native speakers. This may further amplify issues such as linguistic smoothing and attenuation of argument strength, as described above.
What is perceived as “AI-like” English has changed with the advancement of generative AI. In earlier models (before 2020), AI-generated text was relatively easy to detect, often sounding overly polite and verbose with somewhat unnatural structures. As grammatical accuracy and fluency improved, the output became more natural at first glance. However, it often showed an overly balanced tone, frequent hedging, and a tendency to produce statements that were technically correct, but conceptually vague. More recently, AI-generated text has become highly fluent, but also more predictable, often relying on common lexical choices and formulaic expressions, resulting in reduced stylistic variation17). Along with these changes, the way “AI-like” text is recognized has shifted. Earlier, it was mainly identified by grammatical errors and by awkward phrasing. Currently, attention is more often drawn to features such as predictability and stylistic uniformity18). Taken together, these observations point to an interesting paradox that requires further investigation. As the surface quality of AI-generated English improves, even well-trained non-native researchers may have their writing flagged as AI-generated by AI detection tools.
Currently, non-native researchers are expected to write papers with clear emphasis and well-defined claims. Simply asking AI to produce grammatically correct English is therefore not sufficient. The main issue is how clearly authors can express and structure their own arguments. Unless implicit claims are clearly articulated, manuscripts may appear fluent, but remain conceptually unclear for readers. In addition to improving language, it is essential to make the main claims explicit and to organize them into a coherent argument. Generative AI can support this process (Table 1). For example, it may help clarify central claims, suggest alternative interpretations, and even generate reviewer-like counterarguments. It can also help adjust the strength of the author’s claims through appropriate prompting. When used in this way, generative AI may help maximize its benefits while reducing potential risks.
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AI can be considered an external reader. However, in practice, what authors have in mind when writing does not always match how the text is received. For non-native researchers, implicit assumptions or small gaps in reasoning may go unnoticed. In such situations, attention can drift toward linguistic correctness rather than the clarity of an argument.
In this context, AI can be used as a “virtual reviewer”. It can help identify logical gaps, evaluate the strength of claims, and support the iterative refinement of arguments (Table 1). However, the use of AI must be approached with caution when handling unpublished or sensitive data19).
As discussed in this review, generative AI has significantly impacted academic writing by non-native researchers. An increasing number of reports have addressed the appropriate use of AI in the preparation of manuscripts. Future research should therefore focus specifically on non-native researchers and examine both quantitatively and qualitatively how AI assistance influences the quality of academic writing. In this context, authorship is becoming increasingly important. An author’s identity is reflected in the originality of their ideas, consistency of their thinking, and integrity of their research stance. Ultimately, the ability to respond to questions or criticisms in a way that reflects one’s own intellectual position cannot simply be handed over to AI systems.
Generative AI appears to substantially reduce the burden of English expression for non-native researchers and lowers the barriers to academic writing. However, optimizing language with AI may also affect a manuscript’s fundamental quality, potentially leading to weaker arguments, superficial contextual understanding, and diminished authorial presence. Accordingly, the central challenge of AI-assisted academic writing is maintaining and strengthening argumentation, authorial stance, and voice amid AI interventions. The careful and deliberate use of AI is essential to ensure the quality of academic writing in the AI era. Ultimately, recognizing implicit claims and building coherent arguments remain the responsibility of the human author, regardless of advances in technology.
AI useGenerative AI tools, including ChatGPT and Claude, were used only for language editing and to improve the clarity and readability of the manuscript. No new data were collected or analyzed in this narrative review. These tools were not used to identify or select references, generate images or graphics, create references, or develop the central arguments or conclusions of the manuscript. The author reviewed and revised all AI-assisted text and takes full responsibility for the content of the manuscript.
FundingNone.
DisclosuresDr. Sei Komatsu is a technical consultant for Nemoto Kyorin-do Co. Ltd.
Data availability statementNo new data were generated or analyzed in this study.
Ethics approval and consent to participateNot applicable.
Patient consent for publicationNot applicable.
