Digital technologies, automation and artificial intelligence are reshaping how we learn, work and produce new knowledge. But how profound are these changes, and who is affected most? The Programme Jan Amos Comenius (OP JAK) research project “New Technologies and Transformations of Education, Research and the Labour Market,” led by Prof. Štěpán Jurajda of CERGE-EI, examines how technology is entering education, research and the economy, and how it is changing the skills people will need in the future. One of the project’s key questions is whether digital transformation may deepen existing gender inequalities—or create new ones—in access to technological skills and in the ways those skills are used.

You are the principal investigator of the Programme Jan Amos Comenius (OP JAK) research project New Technologies and Transformations of Education, Research and Labour Market. You have led many teams and also served as director of CERGE-EI. What makes this project distinctive?

Let me first mention what is distinctive about the OP JAK funding scheme. It is one of the few large schemes in recent decades to focus on the social sciences and humanities. These fields are very important for our economic development and democracy, yet they have been losing their share of public funding to natural sciences over decades, within multiple funding schemes. So, I think we should be very appreciative that this particular EU-funded call exists. It allows the social sciences and humanities to address important societal questions.

Exploring Technology’s Impact on Education, Research and Work

Could you briefly introduce the project New Technologies and Transformations of Education, Research and Labour Market?

The project focuses on the importance, introduction, and effects of new technologies, from digital technologies, robots and automation, to machine learning and AI. The question is how these technologies affect the way we learn, the way we conduct research, and the labour market and economy more broadly.

A cross-cutting sub-theme of the project is whether there are gender differences in how competencies and skills related to new, digital technologies are developed and used, and whether the growing importance of these technologies creates new gender gaps in society.

Could you give us some examples of the research questions addressed by the project?

Does the use of robots affect employment and wages? How does the adoption of AI spread through an economy? Can AI tools affect student outcomes other than grades?

Combining Quantitative and Qualitative Research

To illustrate the breadth of the project, could you introduce the partner institutions and explain their respective roles?

Like other OP JAK projects in the social sciences and humanities, the project is a consortium of multiple partner institutions across different disciplines. Our overarching goal is to combine expertise across different fields of the social sciences.

The Economics Institute of the Academy of Sciences, where the project’s principal investigator is based, offers expertise in quantitative, data-driven analysis, causal inference, the operation of the economy, and the effects of incentives on behavior.

The next partner is the Institute of Sociology of the Czech Academy of Sciences, which contributes to the project largely through qualitative research. Qualitative research is based on interviews and other forms of qualitative evidence, as opposed to numerical, so-called hard data. Ideally, qualitative and quantitative evidence should complement each other and offer a more convincing view of how the world works.

“A cross-cutting sub-theme of the project is whether there are gender differences in how competencies and skills related to new, digital technologies are developed and used.”

The other two partners, PAQ Research and Scio Research, are smaller but active research organizations focused on education. They are mainly involved in the part of the project that studies how education works and how new technologies affect the way students learn in schools.

You have identified three key areas of research. Let us begin with education. Given the current state of the world, what are the main limitations and potential risks associated with the use of AI in education?

Let us divide this into two parts. For well over a decade, there has been a general tendency to introduce new digital tools into education. These include tablets, the gamification of education, and interactive screen-based evaluation tools that help identify what students have learned.

“While AI offers immediate benefits, its use may make people less receptive and open to new ideas, and less creative, because they rely on a black box that provides answers to everything.”

The first work package of our project, which focuses on education, begins with an overview based on international surveys of the extent to which teachers use these technologies. It also examines whether their use improves learning.

Early evidence was somewhat positive, but more recent evidence may be more neutral regarding how effective these tools are in supporting learning. It’s too early to discuss the definitive findings.

The second part of this area focuses specifically on AI. This is a more recent and potentially highly consequential education technology. Every parent is noticing that their children are using AI much more. At the same time, even as a researcher, you wonder whether relying on AI, while it has immediate benefits, might have long-term costs in terms of how you operate. It may make people less receptive, less open to new ideas, and less creative because they rely on a black box that gives them answers to everything.

Testing Whether AI Can Support Creativity

So how does the project approach this particular issue?

The second part of the first work package involves a large-scale field pilot program in which Czech schools where students use AI, offer students a specific tailored form of ChatGPT. The aim is to support creativity and test the idea that AI can be used in a particular way to make people more creative, open to new ideas. This is compared with the standard form of AI use.

Ultimately, the only way to scientifically learn about the effects of these technologies is to conduct a field pilot, the social-science sibling of a randomized clinical trial (RCT). That is what we are doing.

“The qualitative arm of this work package, which focuses on skills and learning, examines gender differences in the formation and transfer of digital capital.”

We are, of course, making sure that we follow all ethical and other rules for working with students. Everything takes place under the supervision of teachers, with the students’ consent and their parents being informed. Data collection is currently being finalized.

It is too early to discuss the results, but this is one of the larger parts of the project because running a large-scale evaluation pilot of this type requires a great deal of energy and support.

The qualitative arm of this work package, which focuses on skills and learning, examines gender differences in the formation and transfer of digital capital. That is a broad term, so we will not go into detail here. The question is whether young female students are equally eager to learn about technology and digital tools, and whether schools help reduce gender differences.

Digital Skills, Gender Gaps and Equal Opportunities

Why do you find this area of research important?

This is important because the role of new technologies is likely to grow further in the near future. If young female students engage less with technology and are less interested in using and learning about it, this may create barriers to their future career growth. We certainly do not want to create new gender gaps in the labour market, so this is an important area of research.

The final part of the first work package looks at women’s participation in higher education in STEM fields—science, technology, engineering, and mathematics. It focuses specifically on mathematics and physics education at Charles University. During the pandemic, there were several changes in the organisation of university entrance examinations. The question is whether these changes, which made the programmes more or less accessible, affected the gender composition and success of women enrolling in mathematics, physics, and STEM fields more generally.

Ultimately, this kind of research is important for understanding how universities can select students and maximize the use of talent available in the society without creating unnecessary gender gaps.

How Research Funding Shapes Scientific Behaviour

Turning to the second area of the project, how does the research examine the effects of performance-based funding and bibliometric evaluation on scientists’ behaviour?

The second work package focuses on using digitised datasets to help countries support the performance of publicly funded research. It examines performance-based funding schemes that may be based on peer review or bibliometric formulas, under which funding may depend on the number of papers a researcher publishes, to put it simply.

In the Czech Republic, we have already moved away from the latter so-called coffee-grinder approach to research funding.

Yes, I was personally involved in the reform that moved us away from it. However, the open empirical question is what causal effect bibliometric funding had on scientists’ behaviour.

Did the effects differ by gender? Were they different in STEM and other technology-related fields compared with the humanities? Once the country returned to peer-review funding schemes, did scientists’ behavior return to where it had been before the coffee-grinder reform, or did the reform have longer-lasting effects? This is an ongoing research project, so it is too early to discuss the findings.

Another part of this work package examines potential gender biases in how researchers are evaluated and grants are awarded. It also examines the so-called motherhood penalty: whether the cost of motherhood, including leaving the labour market, is higher for women in certain fields or industries, particularly those undergoing rapid technological change.

This is another example of the interaction between new technologies, changes in the labour market and the economy, and the effect of regulation on potential gender differences.

Robots, Employment and the Future of Manufacturing

The impact of new technologies on the labour market is the central focus of the third part of the project. How does the adoption of robots affect manufacturing-based economies?

An example of our research in this area is a study examining whether the use of robots affects employment and wages in an economy that relies heavily on manufacturing. The typical finding in the literature, including our study on Slovakia, is that firms which adopt robots do not generally displace or dismiss their workers. Firms that actively adopt new technologies often succeed in retaining their workers and may even hire new ones.

However, firms that do not adopt new technologies, such as robots, may eventually be squeezed out of the market. The loss of employment and wages may therefore occur elsewhere, although this takes time to become visible.

So far, greater robot adoption has not meant lower wages or lower employment, even in an economy such as Slovakia, which has a large manufacturing sector and automotive industry where robots are particularly important.

In related research, we examine the effect of public subsidies that encourage firms to adopt digital technologies. We ask whether they increase research and development, productivity, or employment, and whether these subsidies are effective. We also look at taxation policies, including which investments can be written off or deducted from taxes, and how such policies affect investment and research and development. This is an important area in which public policy may influence the creation and adoption of new technologies.

What about the use of AI tools and the labour market? Does any part of the project focus on that area?

In one ongoing project, we examine whether AI-assisted systems can help unemployed people search for jobs more efficiently. In other words, we are exploring whether machine learning can be used directly to help people in the labour market.

We also examine how investment in human capital interacts with business cycles. Are people more inclined to invest in their education, including technical and STEM fields, when the labour market is experiencing a boom or a downturn? The third work package therefore focuses on labour markets and the operation of firms and employers.

Could you be a little more specific and give us some examples of the types of questions your research on emerging technologies and labour market examines?

Which firms in developed countries are the first to adopt AI? Can we identify patterns of adoption in administrative data from countries where such data are accessible?

An important challenge for the project is that, unlike in Germany, Denmark, or even Slovakia, gaining access to data already collected by the Czech government is often very difficult, even when the data can easily be anonymized and used for research.

“The size of the motherhood penalty and its interaction with new technologies is an important research topic.”

Why Access to Public Data Matters

You have mentioned difficulties in accessing administrative data in the Czech Republic. Is this one of the project’s main sources of frustration?

 Yes. Before the project began, various Czech ministries promised us access to these datasets, including under memoranda of understanding. In practice, however, obtaining access has proved challenging. Using these data would enable us to produce policy-relevant empirical evidence that could ultimately improve the effectiveness of public policy.

Czech ministries have often yet to fully recognize the importance of data-driven, evidence-based policymaking. They remain highly protective of the data they hold, even when those data are fully anonymized and pose no meaningful risk to students or employees.

Which part of the project are you involved in as a researcher?

I work in the second work package, which focuses on bibliometric funding formulas and their effects on scientists. Fortunately for us, almost all of these data are publicly available.

However, when researchers want to track students’ trajectories through elementary, secondary, and tertiary education, including which fields of study young men and women choose and why, accessing the relevant data is surprisingly difficult, even though the data exist.

“In the Czech Republic, however, we lag far behind the EU15 norm in producing evidence about what works.”

Similarly, it takes a long time to access data on how public policy interacts with motherhood and the industry in which a person was previously employed. We will see whether access is ultimately granted.

The size of the motherhood penalty and its interaction with new technologies is an important research topic. If you leave the labour market for three years, you may return to a very different form of employment that no longer uses, or is no longer suited to, your skills. Studying this requires anonymized administrative data, as it would in any other country.

This type of data is used in every EU15 country with which I am familiar. Our students at CERGE-EI have worked with such data from France, Germany, the Netherlands, Sweden, Denmark, Slovakia, and other countries. In the Czech Republic, however, we lag far behind the EU15 norm in producing evidence about what works. It is comparable to not conducting studies with patient data to determine whether a particular treatment improves their health.

So far, the Czech Republic has not been particularly friendly towards this kind of research. However, I hope the situation will improve, especially now that the reform package—the so-called law on public data, Act No. 60/2026 Coll. on Data Management and Controlled Access to Data—has been passed. We will see whether it is implemented in a functional way. It may come too late for this project, but it is certainly an important long-term factor in shaping our policymaking.

The project New Technologies and Transformations in Education, Research, and the Labour Market, registration number CZ.02.01.01/00/23_025/0008693, led by Professor Štěpán Jurajda, Ph.D., is funded by the Programme Jan Amos Comenius (OP JAK). It received funding under the call “Social Sciences and Humanities: People and Humanity in the Global Challenges of the Present” and will run until the end of 2028.