Dear fellow,

The following paper, "AI in Academic Writing: Ally or Foe?" will provide insightful answers.

Article AI in Academic Writing: Ally or Foe?

Good reading!

I view LLM-s as a sort of enhanced search engines. So I think that they can be very useful for example in gathering data for Systematization-of-Knowledge types of papers, or drafting state-of-the-art sections for your research reports. Of course, the scope and extent of LLM usage must be clearly indicated in the result.

On the other hand, it is not OK to let the LLM write core contribution sections of your paper and not mark this explicitly.

Dear Jan Willemson

Thank you for your answer. It was useful.

Dear Arthur William Fodouop Kouam

Thank you for your answer.

Your article is a very useful study on this subject.

My pleasure!

Dear Recep Baydemir

Dear Anna Be Lle

Thank you for your answer.

You have made a very important point.

It is likely that artificial intelligence will affect the academic field as well as many other fields in the future. The fact that some of the Nobel Prize winners have used Artificial Intelligence in their studies has really taken this issue to a different dimension.

Thank you for your discussion Anna Be Lle

In my opinion, one of the biggest handicaps of the use of artificial intelligence in academic research is the difficulties in detecting when ethical violations are made, and secondly, when ethical violations are made, this cannot be easily audited. In other words, there are no mechanisms that control or supervise this. As such, an unfair competition has emerged in academia. Someone who has no expertise and knowledge on any subject can write an article, make a discovery or create a video and draw a picture through artificial intelligence. Therefore, the use of artificial intelligence in academia is an extremely sensitive issue in terms of scientific ethical principles.

Dear Recep Baydemir,

In my opinion, the use of artificial intelligence (AI) technology in scientific research significantly increases the efficiency of diagnostics and analytics, while adhering to ethical principles. AI makes it possible to automate data analysis, allowing faster and more accurate processing of information, especially in areas such as biomedicine, where algorithms help identify patterns in genetic data, resulting in better diagnosis of diseases. Decision support is another area where AI can be useful, providing researchers with tools to make better methodological choices. Personalization of research in medicine, based on analysis of patient data, also demonstrates how AI can customize treatment, but this requires strict adherence to data protection regulations. In the context of education, AI can be used to develop researchers' analytical skills through interactive educational tools. In addition, AI's automatic verification of data enhances the integrity of research, minimizing the risk of errors and scientific fraud. Key ethical principles include transparency of algorithms, protection of personal data, and integrity in research to ensure that AI supports, rather than replaces, human intuition and judgment. Proper application of these principles will enable significant advances in research, increasing its efficiency and credibility.

Besides, generative artificial intelligence (AI) technologies in scientific research offer great potential, but their ethical use requires clear rules and standards. Researchers should use AI transparently, always attributing authorship to AI when content is generated by these tools, and avoid over-reliance on them in key research processes. Data entered into AI systems must be reliable, and the results generated must be subjected to expert review to prevent unethical use or dissemination of false data. When using AI tools, such as web-accessible applications, compliance with data protection regulations and transparency in the application of licenses are key. In the context of scientific research, AI should support, not replace, the decisions of scientists, and any content it generates must be vetted for scientific integrity. Education and training in the use of AI help researchers use it responsibly, reducing potential biases and errors that could result from inappropriate use of the technology. In the absence of specific legal standards defining the use of generative AI in science, researchers must be guided by ethical principles, including transparency, privacy protection and responsibility for the reliability of research results.

The key issues of opportunities and threats to the development of artificial intelligence technology are described in my article below:

OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND THE NEED FOR NORMATIVE REGULATION OF THIS DEVELOPMENT

Article OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL I...

I invite you to join me in scientific cooperation,

Best wishes,

Dariusz Prokopowicz