Artificial intelligence (AI) has undoubtedly become the buzzword across multiple industries, capturing the attention of both professionals and everyday users. From enhancing our work productivity to reshaping the way we interact with the world around us, AI is now an integral part of our lives. But beyond the latest tools and innovations, we start to think about how AI quietly transforms how we live, shaping our decisions and habits in both personal and professional spheres. As we navigate this AI-driven world in the scientific field, the question arises—can we trust AI-driven research?
A study by Springer Nature revealed that 51% of researchers use AI tools to analyse large datasets, and 32% use AI for writing or drafting academic papers, indicating AI’s role in both data analysis and content creation. Also, an EU survey from 2024 revealed that while 38% of respondents trust scientific research and discoveries created with the help of AI, a quarter (25%) remain sceptical. The debate rages on about how much we should rely on AI when shaping the future of research.
We explored the opportunities and challenges of AI in academia and beyond with Dr. Eran Vigoda-Gadot, a researcher at the Vytautas Magnus University Vytautas Kavolis Transdisciplinary Research Institute (Lithuania) and professor at the University of Haifa, Israel. The scientist, who recently visited Kaunas and held a workshop on AI tools in social research, discusses in this interview how artificial intelligence is poised to impact the world of scientific research.
How do you think the researcher’s daily work routine has changed with AI tools? Has yours changed?
In essence, AI is transforming the researcher’s role from a data processor to a data interpreter and innovator. My work has also been affected by many transformations. Today, it is no longer a question of “should” we use AI in research. Instead, I focus on “how” to make better use of it. So, it definitely affects almost every part of my research and affects my students as well. It’s more than another step in the evolution of science. It’s undoubtedly a revolution.
Can you provide specific examples of how AI has reshaped your research design, method or approach? Have you adopted any new AI tools in your research?
Two examples of using AI tools in my recent studies include Open AI systems to analyse text and create visual manipulation in survey and lab experiments. In the first study, we try to conduct a systematic literature review on the impacts of digital transformation on the discipline of public administration. Together with my students, we use PDF analysis tools like Elicit or Litmaps to examine existing literature and better (and faster) digest past findings and studies to create collective knowledge and meta-analysis reports. In another study, we built visual manipulations using AI platforms for language translation, voice transformations, and video creation based, for example, on Adobe Express and Sora. Obviously, there are tens and maybe hundreds more types of AI tools that each researcher can use for his/her specific needs. I reviewed some of them in my workshop, but they seem to grow in numbers with every passing day. The revolution is moving very fast.
In what ways do you believe AI tools have transformed traditional methodologies in social science research? Has it made the research more effective? More reasonable? Or more accurate? In general, have AI tools transformed social science in any way?
No doubt that the ongoing AI revolution in social science research has made the process of generating new knowledge more effective. It also made it more reasonable because “reasoning” now covers more aspects of rationality and makes better use of interdisciplinary knowledge. But it also has merit beyond simple effectiveness or better reasoning. It dramatically changes the way we think about research and the values that we seek to promote out of these new technologies. It’s about bringing intelligence to new high levels. Traditional methodology has always been based on a single researcher (and his/her team) who needs to be qualified in so many procedures, techniques, and tools. They need to be trained, learn, practice, and properly use them. Intelligence has thus been so far mostly human-based with some support of technology. Advancement over the years opened gates for more collective ways to conduct studies, using knowledge gained by other humans, in different places around the world and with various interdisciplinarity skills.
The digital transformation, and mainly the AI revolution, added another ground-breaking element which has never been available until our times. It’s the machine intelligence that elevates human intelligence into new spheres. I tried to discuss this change in my recent book “Can governance be intelligent? An interdisciplinary approach and evolutionary modeling for intelligent governance in the digital age,” published in 2024 at Cambridge University Press Elements. Social science methodologies today are much more sophisticated, thorough, and of a higher level of rationality and make more sense to those who are sensible. All in all, AI is not merely another transformation in research. It’s a new language – a revolution of basic assumptions and paradigms.
Do you think researchers are ready to use AI tools for their social science research? Are they encouraged to use AI tools in their research?
Researchers were born (as well as raised and trained) to accept new tools and methods, even if, at times, some resistance to change is evident. Such resistance is mostly based on natural fears of the unknown. Many are reluctant to “rocking the boat of science”, but as history teaches us, nothing can stop technological progress. That said, such progress is no guarantee for the progress of values and the creation of public good. Most of the current fears from AI and its aftermath stem from simple human aspiration to minimise losses. AI, on the other hand, allows maximising profits (mostly knowledge). Humanity has never been stopped by those seeking the minimisation of risks. Modernity comes only when people look for progress and improvement beyond what is already known. That is why fears of AI will not hold back social science research. Researchers are ready to use it, and those who still hesitate will join with time. We will, however, witness an increase in better platforms that help us avoid the risks of AI (e.g., using wrong data, making wrong interpretations and implications, fostering truth over fake-truth, etc.).
Should we blindly trust AI tools and AI-generated questions and answers in our research?
Good researchers are trained to never be blind. They are encouraged to work with open eyes and with criticism. This should direct us even in the era of AI in SS. We can, therefore, trust those systems but at the same time, also keep checking them for accuracy and pitfalls. I assume that dedicated AI tools will be offered in the future for “critical AI tools” that double-check the outcomes of fellow AI tools.
How do you navigate ethical dilemmas arising from using AI in your research?
I believe that this field of ethical considerations in AI-based research is far from fully captured. When we use AI systems in our study, we first need to be transparent and declare our use of them, with as much information as possible. It allows others to look at our studies with a critical perspective. Researchers should be trained to be aware of such dilemmas, to recognise them, and to handle them. Ethical issues are a natural part of every study in social science and other disciplines. AI brings another set of problems created by machines we cannot blame for “misconduct” or human failure. So, the final word about ethics (which is “to do the right thing”) remains solely in the hands of the researcher. That, of course, until ethical machines will step into our lives. But that is more of science fiction and not science…
How do you envision the future landscape of social science research evolving as AI tools continue to develop? Will AI tools lead our scientific future?
AI tools already lead much of our scientific future. The use of these tools by an incredible number of scholars around the world, with impressive intensity and endless scope, makes it a revolution per se. Many of our existing rules, routines, and traditions in conventional science will need to be redefined. In the quest for better knowledge for societies and humans, machines and algorithms, in the form of AI, are strong new partners. We should learn how to make the best use of this new friendship and simultaneously also overcome its weaknesses and risks.
Dr. Eran Vigoda-Gadot conducted a workshop on AI tools as part of the project “Strengthening of R&D activities of the Vytautas Kavolis Transdisciplinary Institute of Social and Humanities Sciences (SOCMTEP).”
Photo by Igor Omilaev on Unsplash
