Science and education enrich our pool of knowledge for generations to come and make human existence meaningful. I study the ways we (should) learn while doing science and through education.

Science is a group endeavor of knowledge acquisition directed by interpersonal and global dynamics, as well as by funding policies and new technologies. Social epistemology of science explores group learning in science. Scientific knowledge production is affected by factors such as the organization of a research team, epistemic inclusion of underprivileged groups, the dominant language of science, etc.

Relevant publications

• Epistemic Inclusion as the Key to Benefiting from Cognitive Diversity in Science, Vlasta Sikimić, Social Epistemology, DOI: 10.1080/02691728.2023.2258831, 2023.
• How to Fight Linguistic Injustice in Science: Equity Measures and Mitigating Agents, Aleksandra Vučković and Vlasta Sikimić, Social Epistemology, DOI:10.1080/02691728.2022.2109531, 2022.
• (Dis)satisfaction of Female and Early-Career Researchers with the Academic System in Physics, Vlasta Sikimić, Kaja Damnjanović and Slobodan Perović, Journal of Women and Minorities in Science and Engineering, DOI: 10.1615/JWomenMinorScienEng.2022038712, 2022.
• Modelling Efficient Team Structures in Biology, Vlasta Sikimić and Ole Herud-Sikimić, Journal of Logic and Computation, DOI: 10.1093/logcom/exac021, 2022.
• How to Improve Research Funding in Academia? Lessons From the COVID-19 Crisis, Vlasta Sikimić, Frontiers in Research Metrics and Analytics, DOI: 10.3389/frma.2022.777781, 2022.
• Do Political Attitudes Matter for Epistemic Decisions of Scientists? Vlasta Sikimić, Tijana Nikitović, Miljan Vasić and Vanja Subotić, Review of Philosophy and Psychology, DOI: 10.1007/s13164-020-00504-7, 2020.

AI can, will, and should significantly impact the pool of scientific knowledge and productivity. It will also affect the research process and the way we evaluate science. Together with my team, I explore the potential – accuracy and efficiency, as well as risks – biases and injustice, connected to the algorithmic evaluations in science.

Relevant publications

• Trust in science during global challenges: the pandemic and trustworthy AI, Vlasta Sikimić, Science and Art of Simulation 21 Edition: Trust in Science, Springer, accepted.
• Machine Learning in Scientific Grant Review: Algorithmically Predicting Project Efficiency in High Energy Physics, Vlasta Sikimić and Sandro Radovanović, European Journal for Philosophy of Science, DOI: 10.1007/s13194-022-00478-6, 2022.
• How Theories of Induction Can Streamline Measurements of Scientific Performance, Slobodan Perović and Vlasta Sikimić, Journal for General Philosophy of Science, DOI:10.1007/s10838-019-09468-4, 2019.
• Optimal Research Team Composition: Data Envelopment Analysis of Fermilab Experiments, Slobodan Perović, Sandro Radovanović, Vlasta Sikimić and Andrea Berber, Scientometrics, Vol. 108, Issue 1, pp. 83-111, 2016.

Education has a profound impact on individuals and society. It has an intrinsic value and should be accessible to all. New technologies such as large language models, automated and personalized feedback, algorithmic grading, and the use of virtual reality in classrooms are transforming education. Still, the use of AI in education and, in particular, algorithmic grading of minors are often classified as areas of high risk. The long-term consequences of these measures cannot be fully assessed, and they are prone to manipulation. Moreover, there is the big open research question of establishing which applications of AI in education are outperforming classical teaching methods. For this purpose, thoroughly planned and responsible educational research is required. Finally, the global epistemic impact of new technologies in education needs to be just and distributed fairly.

Relevant research activities

I am collaborating with researchers from the Hector Research Institute of Education Sciences and Psychology at the University of Tübingen and the Leibniz Knowledge Media Research Center. We work on a comprehensive examination of the research on AI in education. The review explores the currently tested AI technologies in education and their ethical implications.  Furthermore, with Aleksandra Vučković from the University of Belgrade, I investigate the worldwide impact of AI in education, with a focus on potential global disparities.