Artificial intelligence is reshaping not only office work, programming and media, but also the fundamental role of universities. Dr. Kovács Ákos, head of department at Széchenyi István Egyetem, says institutions should not pretend the traditional educational model remains sufficient; instead, they must teach responsible AI use. AI systems can summarize books, explain concepts, generate code and produce exam questions within moments, so universities need to reconsider what they actually provide to students.
Prompting and critical skills
According to Kovács Ákos, universities cannot compete with AI in the raw speed of information delivery; machines will win that contest. Real value arises when humans know what to ask and how to verify the answers. Prompting — crafting effective queries — has become a key skill, but it is not enough on its own. Contextual understanding, goal orientation, the ability to fact‑check outputs, and ethical and legal awareness are also required. Users must understand what information they share with systems and how generated responses will be used.
Language and cultural bias
Aczél Petra, communication researcher and professor at Széchenyi István Egyetem, highlighted that the language used to query AI matters. International platforms may perform worse in Hungarian, and responses can reflect cultural differences: the same task may receive differently nuanced answers depending on language and cultural context. In education and research it is especially important to recognize that an AI response is not necessarily neutral but can carry linguistic and cultural assumptions. If unnoticed, students or instructors may treat a partially biased AI answer as objective knowledge.
Instructors, programmers and the changing labour market
The conversation also covered ways instructors can use AI — not as a replacement but as a tool to increase efficiency and extend pedagogical possibilities. Programming is among the most exposed professions because AI already can write code, find bugs and produce documentation. Kovács Ákos argues AI can be an efficiency booster for experienced programmers: those who understand the processes can work faster with it. The greater risk is when someone uses generated code without understanding how it works — this is not professional development but a shortcut that can cause long‑term problems.
The labour market impact will likely be uneven: some companies may initially reduce headcount, but if they fail to hire juniors, there will be a shortage when experienced staff retire, leave or change careers. Therefore a fluctuating transformation is expected as AI spreads.
Why attend university today?
One central question was why students should still attend university when AI provides accessible knowledge. The answer lies not in competing on speed, but in teaching critical thinking, methods of questioning and verification, ethical and legal frameworks, and the professional depth that AI alone cannot replace.
Further discussion points
Key timestamps and topics from the conversation were:
- Why do students primarily use AI? (01:43)
- What could be a university's mission in the AI era? (03:29)
- Which types of knowledge can AI replace most? (06:40)
- How can instructors use AI? (10:18)
- Why does the language used to ask matter? (12:27)
- Why should students attend university if AI exists? (14:01)
- What is the future of programmers alongside AI? (22:43)
- Is university still needed if the labour market demands faster AI training? (27:57)
- Could software be entirely written by AI in ten years? (35:26)
Listeners can consult further podcast episodes for a deeper exploration of these topics.
