Natural language processing tools driven by artificial intelligence (AI) do not qualify as authors, and the Journal will screen for them in author lists. The use of AI (for example, to help generate content, write code, or process data) should be disclosed both in cover letters to editors and in the Methods or Acknowledgements section of manuscripts.
Contributions by artificial intelligence (AI) tools and technologies to a study or to an article’s contents must be clearly reported in a dedicated section of the Methods, or in the Acknowledgements section for article types lacking a Methods section.
The currently available language models are not fully objective or factual. Authors using generative AI to write their research must make every effort to ensure that the output is factually correct, and the references provided reflect the claims made.
Authors must be aware that using AI-based tools and technologies for article content generation, e.g. large language models (LLMs), generative AI, and chatbots (e.g. ChatGPT), is not in line with our authorship criteria. Where AI tools are used in content generation, they must be acknowledged and documented appropriately in the authored work.
The final decision about whether use of an AIGC tool is appropriate or permissible in the circumstances of a submitted manuscript or a published article lies with the journal’s editor or other party responsible for the publication’s editorial policy.
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Using AI for research in current times, where the research landscape is evolving at an unprecedented rate, can be a game changer. Artificial intelligence can assist, augment, and even revolutionize the way we discover, conduct, and write scientific research.
From the invention of the first wheel for moving around faster to Galileo observing the cosmos using a telescope, there has been no shortage of instances where scientists have used technology to do their work more efficiently. And isn’t that the whole point? Since human faculties can be limiting.
Using AI for research is no different, particularly in current times where the research landscape is evolving at an unprecedented rate. New scientific domains are sprouting frequently, millions of papers are being published every year, and there are vast amounts of data needing to be synthesized.
This is where artificial intelligence can assist, augment, and even revolutionize the way we discover, conduct, and write scientific research. Generative AI has proven itself to be more than a simple buzzword to the point where it can provide real useful value to a researcher at any level.
If you go about conducting a manual literature review, you’re talking about countless days of dedicated effort in search and reading. On the other hand, AI can significantly reduce the time and effort it takes to conduct a literature review.
There are AI search engines in plenty that comb through vast databases of research papers, identify relevant papers, and even summarize key findings. This can help you speed up paper analysis, find trends or gaps in the literature, and discover a research question faster.
Most of these AI research assistants and ChatPDF tools help you discover new research articles based on a more accurate semantic search. Even if you don’t have the right keyword, you will still be able to find the correct papers.
AI can make academic papers easier to read and understand by simplifying jargon and complex topics in research papers. They can also summarise long papers into shorter reads so that you save quite some time while going through heaps of scientific articles.
Some AI assistants let you interact with papers, meaning you can essentially have a conversation with the PDF you’re reading. You can enter prompts like a simple question to get an answer or even ask to create a presentation. The AI tool will read the paper and give the output.
Maintaining academic integrity is a non-negotiable while submitting papers. AI tools can help detect plagiarism or the presence of AI in your writing. These tools scan your work, compare it to an extensive database of academic and online content, and flag potential instances of plagiarism.
Combine the importance of AI in modern-day research life with this huge wave of generative AI and ChatGPT in the past couple of years, it should come as no surprise that there is a host of extremely helpful AI tools for research.
SciSpace is an AI platform specifically made for researchers that eases research discovery, reading, and writing. It sits on top of a repository of 270 million+ papers and offers a spectrum of AI tools, including a literature review tool to find relevant information about scientific papers and an AI research assistant called SciSpace Copilot to answer questions about any PDF document. There is also a Copilot Chrome browser extension that can help you understand academic articles on any website.
Litmaps is a handy discovery tool that assists researchers in navigating through scientific literature. It generates interactive literature maps consisting of articles related to a specific journal article or research topic. These maps enable researchers to find appropriate papers, connect the pattern between them, and exchange their knowledge about a particular field of study. The tool is both free and paid
Research EngineEndNote is a reference managing tool that assists you in sorting your bibliographies and references while writing essays, reports, and journal articles. It allows you to create a personal database of references and files, as well as insert references into a Word document and automatically format them in your preferred citation style.
One of the more widely known free productivity software, Notion lets you jot down notes, arrange thoughts, and handle tasks and projects efficiently. During research, Notion can be an excellent tool when you’re collaborating with teams as your team members can comment on the documents, create dynamic content like tables, graphs, etc., and use its AI assistant to complete their tasks.
Otter.ai is a boon while you’re in meetings or recording audio while you work. The AI tool automatically transcribes everything you’re saying and generates live captions during meetings. You can also connect Otter.ai with popular meeting apps like Zoom or Google Meet.
Before you start to analyze data using AI, take a moment to consider the quality of your input. Because if you feed low-quality data to a machine, you can't expect a high-quality output. That just isn’t how it works.
AI cannot think for itself in the same way humans do. At best, it can learn from everything it has been fed and predict an output. So, make sure your data is premium, representative, and, most importantly, unbiased. Biased data can lead to skewed results and questionable conclusions.
It’s always better to ensure your research complies with ethical standards and you use necessary plagiarism and AI detection tools before submitting your paper. How you write a paper is a reflection of who you are as a professional.
Sometimes, AI systems can generate results that seem plausible but are entirely incorrect or generate complete gibberish. There are also times when an AI might give you the correct answer but fake its sources. This is popularly known as a hallucination.
The academic world has undergone a profound change in the last few years thanks to AI. For some, it’s an invaluable resource from streamlining literature reviews to supercharging data analysis and academic writing. For others, it’s a grey area and presents some real concerns relating to academic integrity and watering down of content.
But the fact remains that AI, in most cases does help researchers around the world become more efficient, thus producing good-quality work in less time. As language models develop more and more, the use of AI for research will become even more prominent.
Be it on genetic research, climate change, or scientific research, UNESCO has delivered global standards to maximize the benefits of the scientific discoveries, while minimizing the downside risks, ensuring they contribute to a more inclusive, sustainable, and peaceful world. It has also identified frontier challenges in areas such as the ethics of neurotechnology, on climate engineering, and the internet of things.
However, these rapid changes also raise profound ethical concerns. These arise from the potential AI systems have to embed biases, contribute to climate degradation, threaten human rights and more. Such risks associated with AI have already begun to compound on top of existing inequalities, resulting in further harm to already marginalised groups.
In no other field is the ethical compass more relevant than in artificial intelligence. These general-purpose technologies are re-shaping the way we work, interact, and live. The world is set to change at a pace not seen since the deployment of the printing press six centuries ago. AI technology brings major benefits in many areas, but without the ethical guardrails, it risks reproducing real world biases and discrimination, fueling divisions and threatening fundamental human rights and freedoms.
However, what makes the Recommendation exceptionally applicable are its extensive Policy Action Areas, which allow policymakers to translate the core values and principles into action with respect to data governance, environment and ecosystems, gender, education and research, and health and social wellbeing, among many other spheres.
The ethical deployment of AI systems depends on their transparency & explainability (T&E). The level of T&E should be appropriate to the context, as there may be tensions between T&E and other principles such as privacy, safety and security.
EIA is a structured process which helps AI project teams, in collaboration with the affected communities, to identify & assess the impacts an AI system may have. It allows to reflect on its potential impact & to identify needed harm prevention actions.
UNESCO's Women4Ethical AI is a new collaborative platform to support governments and companies’ efforts to ensure that women are represented equally in both the design and deployment of AI. The platform’s members will also contribute to the advancement of all the ethical provisions in the Recommendation on the Ethics of AI.
