AI in education, science and research: comprehensive overview

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Spheres of research that use generative AI are expanding rapidly both across the world and through almost all science’s discipline. Experts are of the opinion that the process is not only accelerating but basically transforming scientific knowledge and the work of education providers. University World News has published a series of articles on AI and research exploring numerous ways in which AI is involved in higher education.

This article is aimed at showing our readers the updated assessments of publications in the University World News, UWN during April-May 2024 devoted to pressing modern issue – the educational revolution and the role of digitalisation in it.
Some short reviews of the articles in UWN follow, reflecting main strands in this complicated sphere of education, digital growth and teaching supplemented by references for those wanting to go deeper into the issues.
The UWN series of articles will culminate in a special briefing in June.

General reference to the series in:

Masters in Emotion AI
An emerging subset of artificial intelligence that interprets and responds to human emotions, so-called “emotional AI” as a master degree kicks off during 2025 across eight universities in six European countries. The master’s course is a blended and trans-disciplinary approach which will spin off modules for mass AI upskilling.
Emotion AI – also known as affective computing or emotional AI program – emerged in the 1990s, spurred by academics from the American MIT’s Affective Computing department, which issues a book about how to give computers emotional intelligence skills.
Emotion AI has gained importance with recent developments in AI and large language models.

AI in science
In the society of acceleration, where research and innovation are developing fast, we need to ensure that science education does its part and supports future professionals who will work in science-related jobs. Understanding how science works in the age of AI will be enriched if it includes a holistic approach to science. The dizzying pace of developments of AI use in science is raising questions about how science education in universities is incorporating these recent developments. Some important questions are emerging for science education in universities: Is science changing because of AI use? How can science teaching and learning in universities reflect the recent developments in science?
Such questions may seem to only concern future research scientists but in fact, when science is considered in all its might, education of a whole set of professionals is at stake because science is a complex endeavor that concerns much more than research outputs like scientific knowledge and research processes like scientific methods.
Bottom line: science is also a culture, it has institutions, and scientists possess social values.

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AI in publication
Both authors and publishers need to get a better account of the research results: they have to be assured that academic papers are free from serious mistakes – regardless of whether the mistakes are caused by careless use of AI or sloppy human scholarship.
This is more achievable than policing AI usage, and will also improve research standards as a whole. A recent study examined the frequency of certain words in academic writing (such as ‘commendable’, ‘meticulously’ and ‘intricate’), and found they became far more common after the launch of ChatGPT – so much so that already about one percent of all journal articles published in 2023 may have contained AI-generated text.

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In an article published in May 2024 a study is analyzed that has found some interesting results: e.g. that specific artificial intelligence content’s detectors and experienced human reviewers can accurately identify AI-generated academic articles, even after they have been paraphrased. The author recommends that publishers should use both AI detectors and top-notch reviewers to help protect academic integrity in scientific publishing.
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Human-AI collaboration
The growing integration of AI tools is catalyzing a new era of ‘human-AI collaboration’ in research, signaling a profound shift in how academics approach their scholarly work. This shift is not just about increasing productivity and scale – it represents a fundamental change in the research paradigm.
As artificial intelligence tools continue to evolve, their potential to transform academic research is becoming increasingly apparent. From facilitating comprehensive literature reviews and identifying hidden research gaps to analysing massive datasets and visually presenting complex information, AI is empowering researchers to tackle tasks once thought insurmountable.
Amidst the excitement and promise of this new frontier, it is crucial to acknowledge the challenges and ethical considerations it presents. Reimagining the dynamic between human and artificial intelligence requires a fundamental recalibration of researchers’ roles and responsibilities and the cultivation of a collaborative system that integrates human ingenuity with AI capabilities.
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AI in Latin America and the Caribbean
A new continental collaboration around AI in science and research was the unanimous outcome of a gathering of experts from countries across Latin America and the Caribbean, led by the International Science Council. Only by listening to all regions can there be a proper global conversation about generative AI.
An early step in the collaboration kicked off by the experts from 11 countries will be to share and map developments and discussions, strategies and actions involving AI in science and research systems across the region, and to keep updating this resource.
The author acknowledges that hopes for active collaborative AI initiatives for the region might face political and funding challenges, but the will is there.

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AI and sociology of science: studies in Denmark
In two major studies in Denmark, experts in computer science and in the sociology of science are exploring how researchers are using generative AI – and how rapidly evolving AI tools might change ways in which scientific knowledge is produced and diffused. The research will map the growing influence of AI on science and how AI technology is spreading across scientific communities.
The studies will analyse AI’s impacts currently and in the future by combining computational social science methods with controlled experiments. “Development is moving so fast at the moment. But right now, we have a window of opportunity where we can compare the knowledge produced by humans with the work of AI; ultimately, the aim of the project is to give us all a much clearer picture of the new and unexpected consequences of AI-infused science”, said Professor Roberta Sinatra of the University of Copenhagen.


Global cooperation in AI
There is ample policy and strategic action around generative AI and research but scant exchange of knowledge between the world’s countries. A study by the International Science Council aims to bridge this isolation, increase collective knowledge and identify key issues and initiatives. There is a healthy appetite for engaging with other countries, to exchange knowledge and compare experiences, said Dr Mathieu Denis, head of the Centre for Science Futures at the council and one of the authors of the study report, published in 2024 “Preparing National Research Ecosystems for AI: Strategies and progress in 2024”.
Dr. M. Denis notes that “more collaboration and coordination of AI strategies for science would increase our collective capacity to use AI for the benefit of science and society and to address global challenges such as climate change”.
Among other important findings are: disconnect between discussions about the impacts of AI at the global versus national levels, and gnarly technical policy questions that need an international approach. A survey of 12 mostly small-to medium-sized countries revealed great variation in AI approaches and surprisingly ambitious AI strategies in some.

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AI in universities: Japan’s example
Universities are playing an active role in the Japanese government’s plan to lead global development of ‘responsible’ generative artificial intelligence. Political support for university research on generative AI is primarily aimed at finding viable solutions to domestic issues, in particular a looming labour shortage.
“The focus is on establishing reliable and high-performance AI knowledge that is critical for labour-strapped Japan and other national goals,” said the head of the government’s AI Strategy Council and professor in the Center for Engineering at the University of Tokyo. He is also chair of the Deep Learning Association, serving as an outside director of SoftBank Group, a leading telecommunications company.
The Strategy Council, launched last June, described AI as the “arrival of a great opportunity for Japan”, which includes corporate executives, university researchers and a lawyer and is tasked with formulating regulations and legislation to minimize risks related to generative AI. It also serves as an accreditation body to certify companies that promote responsible AI. Generative AI refers to tools such as ChatGPT and Large Language Models (LLMs). These can power chatbots that can analyse data, enable language translation, develop curricula and can also be used to devise strategies for organisations.
In the start of 2024, the Cabinet Office decided to invest JPY 12 billion (US $79 million) for AI development during the year to strengthen Japan’s AI research capabilities in universities. The new funds are seen by experts as an opportunity for developing cutting-edge technologies to prevent misuse, as well as for pulling ahead in generative AI research and development, where Japan has been lagging globally. The government’s action plan for new policies and organisations to promote AI has identified greater use of AI in medicine and healthcare, education, finance, manufacturing and administrative work while ensuring it can be controlled by humans, according to the Cabinet Office website.

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