How Artificial Intelligence Is Changing Scientific Publishing?

Scientific publishing has always been at the core of academic progress. But today we are witnessing a turning point: artificial intelligence (AI) is beginning to actively influence the way research is created, published and perceived. The traditional model of “scientist writes – reviewer checks – publisher publishes” is gradually transforming into a more complex system where technologies are becoming full-fledged participants in the process.

However, along with the benefits come new risks. If the use of AI in the scientific environment remains without proper regulation and control, it could lead to a situation comparable to a casino sin licencia – formally available, but lacking transparency, rules and trust.

Below we look at five key areas where AI is already changing the scientific environment.

From hypothesis to manuscript: the role of AI in the research process

In the past, a researcher would spend weeks or months formulating hypotheses, preparing experiments, and writing papers. Today, AI is able to take over some of this work.

AI capabilities in the early stages of research:

Idea and hypothesis generation. Algorithms analyze publication databases and suggest potential research directions.
Automated planning of experiments. Some systems can suggest optimal conditions for experiments, reducing time and costs.
Drafting articles. Language models such as GPT create text blocks for introductions, method descriptions, and even discussions.
Creating visualizations. AI helps generate graphs and charts based on data, making it easier to understand the results.

Example: in 2024, the experimental AI system Scientist-v2 was able to produce a scientific text that was selected for a conference. This showed that AI is no longer just a tool and has become a co-authorin the truest sense.

Thus, the scientist increasingly acts as a curator of the process: he or she sets the direction, checks the validity and draws conclusions, while the algorithm does the routine work.

New publication formats and interactive knowledge

A classic scientific article is a fixed text with tables and references. But AI is changing the very form of knowledge presentation.

Innovative publication formats:

Agentic Publications. These are articles with AI agents embedded inside them. They allow you not only to read the author’s conclusions, but also to check them, run additional calculations or analyze the original data.
Discovery Engine. Publications turn into dynamic knowledge bases. Data are represented as graphs or multidimensional structures, and special algorithms help to “unfold” new relationships.
Living documents. Instead of a static PDF, articles appear that can be updated and supplemented. They become part of an evolving knowledge base rather than a one-time publication.

Why is it necessary?

Increased transparency: everyone can check the results. This helps reduce the risk of falsification and builds trust in scientific data.
Flexibility: articles are adapted to new discoveries. This format allows research to be updated quickly without having to wait for a new issue of the journal.
Accelerating science: data become available for re-analysis and meta-research. Scientists can use already published data to test hypotheses and draw interdisciplinary conclusions.

In fact, the scientific article ceases to be a “final product” and becomes an interactive tool.

Personalized tools for researchers

AI is also radically changing the process of searching and analyzing scientific texts. While previously a researcher had to manually review dozens of articles, today AI services do it automatically.

Key examples of tools:

Semantic Scholar: automatically creates short abstracts (TL;DR), shows the most cited papers, helps to understand an unfamiliar topic faster.
Scopus AI: analyzes large arrays of publications, finds experts on a particular topic, builds maps of scientific areas.
ScienceDirect AI (Elsevier): helps to group articles by meaning, creates recommendations based on researcher’s interests, tracks the development of specific concepts over time.

Benefits for researchers:

Time saving: instead of searching manually, the researcher receives a ready-made collection. This allows the researcher to focus on interpreting data and developing new ideas rather than on routine work.
Reduces the risk of missing important research. Algorithms analyze many more sources than a single person can cover and take into account even low-circulation publications.
The possibility of deeper analytics: AI reveals hidden connections between different areas of science. The possibility of deeper analytics: AI reveals hidden connections between different areas of science.

In this way, AI becomes a navigation system for the scientist, allowing him or her to focus on interpretation rather than search.

False data and challenges to trust

However, along with the benefits come serious risks.

Key issues:

AI hallucinations. Language models can come up with non-existent links and studies. In some experiments, up to 70% of the links generated by ChatGPT turned out to be fake.
Interpretation errors. Even correct data can be misrepresented.
Paper mills. With the emergence of AI, the number of fake publications has increased. For example, publisher Wiley had to withdraw more than 11,000 articles and close 19 journals between 2023 and 2024.
Lack of standards. There is not yet a uniform protocol for exactly how to indicate the use of AI in scientific papers.

What does this mean for science?

A threat to the credibility of publications: readers do not always understand where there is a human and where there is a machine.
Additional burden on reviewers and publishers: verification systems need to be implemented.
Risk of reducing the quality of the scientific dialog: fake articles can make it difficult to find true data.

The key challenge is not the speed of publication, but its credibility.

The Future of Scientific Publishing: Human-AI Partnerships

AI has already proved that it is not an enemy to scientists, but a tool that can increase the efficiency of research. It is only important to set up the right rules of interaction.

Trends of the future:

Transparency and ethics: each article will contain a declaration on the use of AI, responsibility for the final text will remain with humans.
Open science: development of open platforms accessible without barriers, reducing dependence on large publishing corporations.
Interactive publishing: articles will be updated in real time, researchers will be able to run built-in algorithms for verification.
AI as a scientific partner: scientists will divide tasks: algorithm – data processing and structuring, human – verification and interpretation.

Scientific publications will become more dynamic, verifiable and personalized, but credibility will depend on transparency and accountability.