A student submits an exceptionally well-written paper. But does it still reliably demonstrate how much they actually understand? Artificial intelligence can help a researcher search for and process information far more quickly, yet it can just as convincingly produce a reference to a source that does not exist. Meanwhile, some of the tasks that once marked the beginning of a young professional’s career can already be performed by technology.
As higher education, research and the labour market continue to change, an increasingly important question arises: should our understanding of quality in higher education – and the way we assess it – change as well?
This question will also be central to the anniversary event of the Magna Charta Universitatum, to be held in Vilnius on 14–16 October and co-hosted by Mykolas Romeris University (MRU) and the Magna Charta Observatory (MCO). This year’s conference theme is “Beyond Resilience: Universities as Active Value-Driven Agents in a Changing World.”
The conference themes reflect the challenges identified by universities within the Magna Charta community itself. These include technology and artificial intelligence (AI), quality enhancement and assurance, safeguarding academic integrity, public trust in universities, and universities’ social responsibility.
The connection between these issues is also reflected in the conference programme. Following the discussion “The AI Challenge: Approaches and Outcomes,” one of the parallel sessions will focus on rethinking quality in higher education and ask whether quality assurance systems are evolving fast enough to keep pace with a changing world.
How is this transformation already unfolding within universities, and what does it mean for the very concept of quality in higher education? Professor Marius Laurinaitis of MRU approaches the issue through changes in teaching and learning, research, and the competencies graduates will need in the future. MRU Vice-Rector Associate Professor Saulius Spurga considers the criteria by which university quality is measured and asks whether they still capture what matters most.
When the Transfer of Information Is No Longer Enough
Professor Laurinaitis says that AI in universities is no longer a matter for the future. Students already use it every day: to explain a difficult topic, summarise an article, translate a text, or make complex information easier to understand.
This is also changing what happens in the classroom. Students increasingly arrive already familiar with a topic, but an understanding shaped by AI may be either very good or completely wrong.
“A lecture in which I spend an hour and a half simply delivering information is losing its purpose. A student can obtain information in a matter of seconds. The value of a university lies elsewhere – in discussion, case analysis, problem-solving and the ability to understand,” says Professor Laurinaitis.
Prof. Dr. Marius Laurinaitis
AI is also increasingly used in research to search the literature, analyse data, write code and edit texts. It reduces language barriers and can take over some administrative tasks. Speed, however, does not guarantee reliability. AI can still produce a highly convincing reference to an article, book or court judgment that does not exist. For this reason, Professor Laurinaitis stresses, ultimate responsibility for the result remains with the human user.
Associate Professor Spurga notes that the question of what constitutes quality in higher education predates generative AI by decades. As higher education expanded in the second half of the twentieth century and became accessible to much larger parts of society, the pressure to demonstrate measurable outcomes also increased. Greater emphasis was placed on graduates’ readiness for the labour market, academic publications, formal performance indicators and university rankings.
Assoc. Prof. Dr. Saulius Spurga
Yet what is easiest to measure does not necessarily capture the full quality of higher education. Spurga points out that rankings ultimately reduce highly diverse university activities – including teaching and learning, research, projects and an institution’s role within a particular country – to a single number. Nor are all formal criteria equally appropriate across different academic disciplines, while publications in languages other than English may be undervalued.
AI did not create these problems. But it has made the need to reconsider what we assess, and how we assess it, even more apparent.
A Good Result Does Not Always Reveal How It Was Achieved
This is particularly evident in the assessment of student work. For a long time, a well-written paper was a reasonably reliable indication that a student understood the subject, had identified relevant sources and could formulate an argument independently. With the emergence of generative AI, that direct connection can no longer be taken for granted.
According to Professor Laurinaitis, it is therefore becoming increasingly important to assess not only the final product, but also the process behind it. How did the student arrive at the conclusion? Why did they choose a particular solution? Which sources did they verify? Did they recognise where AI had made a mistake? Can they explain and defend their work?
This may mean more oral defences, discussions, in-class practical assignments and analysis of real-life cases. The concept of academic integrity is changing as well. Plagiarism alone is no longer sufficient as a framework, while systems designed to detect AI-generated content can themselves make mistakes. Students need clear guidance on when AI may be used for a particular task and when it may not. Ultimately, however, responsibility for the submitted work remains with the student.
New questions are also emerging in research. According to Associate Professor Spurga, AI is forcing the academic community to rethink authorship and originality, while also creating new challenges for peer review. As technology develops faster than the systems designed to govern it, higher education faces the demanding task of developing a new understanding of quality and new criteria by which it can be assessed.
What Kind of Graduate Will the Future Require?
Professor Laurinaitis rejects the idea that more advanced AI will mean people need less knowledge. In his view, the opposite is true: the more powerful the tool, the greater the expertise required to evaluate the results it produces.
“If I don’t know the law myself, how will I know when AI gets it wrong?” he asks.
Simply knowing how to use AI will therefore not be enough for the graduate of the future. More important will be the ability to define a problem, ask the right question, distinguish fact from error, verify sources, assess risk and make informed decisions.
At the same time, new questions of responsibility and equality are emerging. Student papers, personal data, unpublished research or manuscripts under review cannot be uploaded to commercial AI tools without careful consideration. Access to technology also varies. The most advanced models come at a cost and, Professor Laurinaitis argues, unequal access to them may become a new form of academic inequality.
He also sees another significant change emerging in the labour market. AI is likely to take over some of the more routine tasks first – yet these are precisely the kinds of tasks through which young professionals have traditionally begun their careers. They learned by reading documents, searching for information and preparing their first drafts and projects.
“If AI does the beginner’s work, how does the beginner become an expert?” Professor Laurinaitis asks.
If some of this early professional experience disappears from entry-level jobs, universities may have to provide more of it themselves through real-life cases, simulations and practical decision-making.
This is where technological change directly intersects with the question of quality. If what graduates need to know is changing, and if professional experience is acquired in different ways, then our expectations of high-quality university education must change as well.
Are We Assessing Universities by What Is Easiest to Count?
Associate Professor Spurga points out that higher education is already exploring different approaches to assessing quality. These include open science and a growing emphasis on the real-world impact universities have on society.
Graduate employment rates and citation metrics provide valuable information, but they do not fully capture a university’s significance. At the same time, real-world impact is much harder to assess. It often takes longer to become visible, requires more complex data and frequently calls for qualitative evaluation. The role of a university also varies depending on its social, national and regional context.
Nevertheless, Spurga believes that societal impact should become a more significant component of university quality assessment and could, in the future, carry greater weight in accreditation. For that to happen, however, universities themselves cannot be the only ones to change. The agencies that evaluate them must evolve as well.
This question is also closely linked to the Magna Charta’s understanding of the role of the university. Academic freedom and institutional autonomy are inseparable from universities’ responsibility to society. It is therefore no coincidence that, in a survey of universities within the MCO community, public trust and social responsibility emerged alongside technology and quality as some of the most pressing issues facing universities today.
The discussion in Vilnius this October about a new understanding of quality in higher education therefore goes far beyond the question of how universities should adapt to AI. Technological change is, rather, bringing a much older and more fundamental question into sharper focus: does what we know how to measure still correspond to what we truly value in a university?
Professor Laurinaitis summarises the university’s role in this transformation simply:
“We should not try to compete with AI over who can deliver information to students faster. We have already lost that race. Our value lies elsewhere. We must teach people to think, question, verify, discuss and make decisions.”
The Magna Charta Observatory (MCO) brings together universities around the world that are committed to the principles of the Magna Charta Universitatum. Its work is grounded in strengthening academic freedom, institutional autonomy and universities’ responsibility to society. More than 1,000 universities from 94 countries have signed the Magna Charta Universitatum.