Is Artificial Intelligence transforming research and publication? Discover AI trends in research and the AI-driven tools researchers are finding useful. 

Introduction

John McCarthy, an American computer scientist, coined the term "artificial intelligence" (AI) in 1956, marking a pivotal moment in technology.  McCarthy defines AI as the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. Since then, AI has evolved significantly, encompassing areas such as neural networks, robotics, and natural language processing. These advancements are driving technologies such as information extraction, retrieval, and speech recognition, revolutionizing various industries. 

AI enables machines to emulate human-like behavior and perform tasks once thought exclusive to humans. Its integration into industries is widespread, with applications continuing to expand and permeate our daily lives. In research, AI has experienced exponential growth, becoming indispensable across all fields. Its applications have become integral in various aspects of research, revolutionizing the way data is analyzed, patterns are identified, and solutions are developed. For example, scientists in medicine have been able to use AI to compile data in biomedical science and predict diseases. While AI has been beneficial to society, it also comes with some drawbacks. In this article, I delineate the pros and cons of AI and how researchers can optimize its benefits. 

Benefits of AI in Research

The history of AI in publishing traces back to 1951, when it first emerged as a tool for automating basic tasks such as spell-checking and typographic layout designs. By the 1990s and early 2000s, it had begun to revolutionize publishing by enabling more sophisticated applications. In the publishing field, where researchers will need to analyze large amounts of data and identify patterns in external and vertical data, different AI tools help produce accurate data-driven content. AI-powered tools in research have helped speed up and free up the researchers’ time.

It provides platforms that assist with grammar and language checks, formatting checks, and even plagiarism detection, selecting journals, suggesting reviewers, and statistical analysis. The technology’s ability to analyze data at scale also offers publishers unparalleled insights into reader preferences, market trends, and content performance. It is anticipated that the applications of AI in publishing will continue to expand and evolve rapidly.

What are the likely negative effects of AI? 

While the evolution of AI in research has been intriguing, relying solely on AI for research, which includes getting data, grammatical language checks, statistical analysis, and plagiarism, should be avoided.  

Lack of quality control is one of the main problems with employing AI models. Although some models, such as ChatGPT, Microsoft-copilot, and Claude, can produce a lot of text very quickly, there is a chance that the content they produce will not meet the requirements and quality needed for publication. It is imperative to implement suitable quality control protocols to guarantee the precision, pertinence, and dependability of the material. A recent study discovered that reviewers only detected 68% of the "fake" abstracts produced by ChatGPT. This implies that a significant amount of information created by AI may become hard to detect in the future as these models become more sophisticated.

AI models have the potential to produce fake content or manipulate data in unethical or misleading ways. Although the outputs are accurate and nearly identical to human writing, they are riddled with repetitions and ambiguous details. Therefore, the papers produced using AI may be less creative and of lesser quality.

AI-related Development in the Industry  

A large number of AI tools use machine learning and natural language processing to process large data sets. AI tools in research encompass a broad range of applications, some of which are highlighted below.

Article summarizer tools: This free tool uses sophisticated AI models to swiftly summarize any text without requiring the user to read every paragraph. Summarizer tools can also be used to verify the subject matter and relevance to the scope of a journal before it is submitted for peer review. An author can also benefit greatly from such tools when conducting a literature review. Some examples of summarizing tools include Quilbolt AI text summarizer, UNSILO, and Scholarcy. These tools are programmed to capture relevant text and keywords in an article. If you are looking to conserve time on literature reviews, these are your go-to.

Literature discovery tools: Researchers can face challenges with literature review, as it involves searching numerous databases and countless articles. Machine learning creates algorithms that comprehend what an individual reads and help make relevant suggestions for the user's specified interests. The use of AI for this activity can be a very effective time-saving strategy for researchers who need to go through mountains of literature. Notable examples Examples: R Discovery, Scite.AI, Elicit, and Consesus. These tools help the researchers have a large repository of scientific literature and recommend relevant papers to them.

AI academic editing tools: millions of research articles edited by skilled editors from a range of disciplines have served as the training set for this instrument. It not only ensures that language problems are efficiently corrected but also that domain-specific terminology is used correctly. Paperpal is an example of a popular tool used to ensure accurate usage of domain-specific technical terminology and identify grammatical errors.

Conclusion

By effectively using AI, authors may speed up procedures, improve accuracy, and guarantee openness by choosing the appropriate tools. These actions ultimately benefit the entire sector. Elsevier's policy permits writers to employ artificial intelligence (AI) techniques to enhance the readability and language of their submissions; however, it highlights that the generated output is ultimately subject to inspection by the author or authors to ensure accuracy. 

AI-assisted tools for research and publishing are here to stay. The problem comes when one cannot discern reality from the creations of a neural network, when an individual's writing is indistinguishable from a machine. Going forward, it is clear that both researchers and publishers need to agree on what practices are acceptable in this space and how to enforce them. Writers must acknowledge their limitations and strengths and navigate through them to get the best results.