According to Mehr reporter; In the past three years, generative artificial ielligence, especially in the form of large language models, has not only transformed human writing practices, but also fundameally challenged the logic of text production in the scieific sector. By significaly reducing the time and skill cost of text production, this technology has weakened the border between scieific writing and mass production of text and has created a new model of the process of producing specialized articles. What was initially iroduced as a tool to facilitate scieific writing and especially to remove the language barriers of non-English speaking researchers, today has become a structural phenomenon whose consequences go beyond the improveme of writing style.
In this framework, the phenomenon of producing low-quality scieific texts based on artificial ielligence refers to a situation in which a huge volume of pseudo-scieific texts is produced with minimal human ierveion and without going through the classic processes of reflection, revision and self-criticism It is produced scieifically. These texts often appear to have all the academic compones, including complex language, structure face Regular, specialized vocabulary and referencing are conveional. However, at the coe level, they often lack theoretical innovation, original problem, or meaningful advanceme in existing knowledge.
The ceral question in this situation is whether the expansion of the use of these tools has led to the improveme of the quality of science or has simply increased the acceleration of the production of apparely scieific texts. To be more precise, is it with a phenomenon that observesdemocratized We are facing “the becoming of knowledge” or “language inflation” that depreciates the value of classical signs of scieific quality. The answer to this question, according to the empirical evidence of new researches, is not very optimistic and shows that at least in the curre situation, the cuff The scale is weighted in favor of a quaitative increase in text production and not in the deepening of scieific coe.
New research findings about scieific writing
A rece study by researchers from the University of California, Berkeley and Cornell carried out and published in “Science” magazine, by analyzing more than one million abstracts of pre-pri articles, between the years 2018 and 2024, it has tried to evaluate the real impact of the use of artificial ielligence on scieific production. In this research, the number of articles of each author is considered as an index of productivity and the final acceptance of the article in scieific journals as an index of quality.
The results show that when writers start using artificial ielligence tools, their productivity has increased significaly. Depending on the publishing platform, the number of articles published mohly after the use of artificial ielligence has grown between 36 and nearly 60 perce. This increase is much more impressive for non-English-speaking researchers, especially Asian authors, and in some cases reaches about 90%.
These data show that artificial ielligence has been a tool for many non-English speaking researchers to overcome language barriers and improve English writing; A function that by itself can strengthen access to scieific publication.
Linguistic complexity and quality criterion inversion
However, the review of the quality of articles preses a worrying picture of the field of scieific research. Articles written with the help of artificial ielligence have more complex language on average. In non-AI articles, greater linguistic complexity usually correlates with a higher likelihood of acceptance and publication; This shows that scieific judges consider precise and complex language as a sign of scieific depth and quality.
But in the case of articles written with the support of artificial ielligence, this relationship is reversed; In other words, the more complicated the language, the less likely the article will be accepted. This significa inversion indicates that the linguistic complexity based on artificial ielligence, in many cases, has not been a reflection of scieific depth, but a cover for the methodological and coe weaknesses of the research.
In other words, what used to be considered a sign of quality, can now be a camouflage tool for “scieific vulgarity”; Texts that are adorned with technical vocabulary and complex structures, but lack innovation, solid reasoning, or valid scieific method.
Artificial ielligence and diversity of scieific resources
The aforemeioned research also deals with the effect of artificial ielligence on the pattern of researchers’ access to scieific resources and shows that this technology is not just a text production tool, but is gradually rearranging the mechanism of distribution and visibility of scieific knowledge. In this section, by comparing the data related to downloading articles through Google search engine and search engine Bing Microsoft, which has been equipped with iellige conversation capabilities based on artificial ielligence since early 2023, has been ideified by users Bing They have access to a more diverse and newer range of scieific articles and publications.
This difference is importa because the engines search In effect, they have become invisible gatekeepers of knowledge, and their suggestion pattern can directly affect what is read, referenced, and ultimately reproduced. The increasing diversity of sources in AI-based search shows that this technology has the poteial to overcome the traditional focus on a limited set of articles. refereial and weaken the domina publications to some exte and increase the possibility of newer or lesser known research being seen.
This variation is most likely due to use Bing It is from the “recovery enhanced production” method; The way in which, classical results search It is combined with artificial ielligence text generation processes and the final answer is formed not only based on popularity or number of references, but based on active retrieval of releva information. Corary to initial concerns that AI-based search will be limited to reproducing old and established sources, evidence from this research shows that such systems can facilitate access to more diverse and up-to-date scieific literature.
However, this developme has a double consequence. Such conditions, on the one hand, can lead to more dynamism of knowledge circulation and reducing the monopoly of resources, and on the other hand, it provides algorithms with the power to select and highlight resources more than before; This doubles the need for transparency, monitoring and critical understanding of search mechanisms based on artificial ielligence in the field of science.
Strategic Implications for Scieific Arbitration
The most importa consequence of these developmes for science is the weakening of the validity of language as a quick and low-cost indicator of scieific quality. In the popular tradition of academic refereeing, writing quality, linguistic coherence, and lexical complexity were often regarded as implicit indicators of conceptual rigor, theoretical depth, and research maturity. This reliance was largely due to the fact that the production of precise and complex scieific language requires a real mastery of the subject, spending considerable time and going through expensive thought processes.
With the spread of generative artificial ielligence, this historical link between language and coe has been seriously disrupted. Now it is possible to produce texts with an academic appearance, cohere structure and specialized vocabulary, without having deep scieific understanding or real participation in the advanceme of knowledge. In such a situation, relying on the quality of writing for the initial screening of articles is considered not only unreliable, but also poteially misleading and can lead to the acceptance of texts whose fundameal weaknesses remain hidden at the language level.
As a result, the process of scieific judgme has to move away from superficial and language-orieed evaluations and move towards deeper methodological investigations. Therefore, focusing on the logic of the research design, the validity of the data, the transparency of the analytical methods, the reproducibility of the results, and the real coribution of the article in solving a specific scieific problem, become the ceral compones of the judgme. Although this shifting of standards is necessary from a scieific poi of view, it also significaly increases the workload of reviewers and editors of publications.
This necessity arises in the coext that the scieific publishing system is already facing an increasing flood of sending articles, lack of expert reviewers and chronic time pressure, and weakening the credibility of language as a quality indicator is not only an epistemic challenge, but also an institutional crisis for the scieific review mechanism, which cannot be managed without structural rethinking in the evaluation process.
Fighting AI through AI
In such a situation, the use of arbitration tools based on artificial ielligence is proposed as a possible and somewhat inevitable solution. The logic of this approach is that the same technology that has fueled the quaitative increase and sometimes the qualitative decline of scieific texts can be used in reverse to refine and screen this accumulation. A promine example of this approach is the article review tools that were recely iroduced by Stanford University, and their goal is not to replace the human reviewer, but to strengthen the diagnostic capacity of the review process.
These tools can ideify stereotyped language patterns, methodological inconsistencies, weaknesses in research problem formulation, or signs of lack of coherence between data and results in the early stages of evaluation. In this way, the cognitive load of human judges is reduced, and their focus is on a deeper evaluation of scieific innovation, the validity of conclusions, and the added value of research, instead of the primary refineme of texts. However, the application of such tools itself requires institutional caution rather than turning the adjudication process further io an opaque algorithmic mechanism. lowered do not
In this sense, the metaphor of “fighting fire with fire” is merely an allegory technologically It is not, but it expresses an institutional necessity in the age of accumulation of scieific texts. Maiaining the standards of science does not go through the path of removing or completely rejecting artificial ielligence, but requires redefining quality criteria, redesigning the multi-layered judging process, and establishing a kind of critical and corolled coexistence with this technology. The future of scieific publishing depends on artificial ielligence not being the final arbiter of truth, but rather being a supporting tool in the service of scieific rationality and human judgme.
