Artificial intelligence is no longer a technology universities will react to “someday”; it is already embedded in students’ work, teachers’ toolkits, research and the labour market. That was the message from Eigner György, Dean of the Neumann János Faculty of Informatics at Óbuda University, during a podcast conversation with recurring guest Aczél Petra, communication researcher and professor at Széchenyi István University.
According to the dean, attitudes among professionals are mixed. Some view AI as liberating and efficiency-boosting, delivering results faster with less effort. Others worry that the pace of technological change is hard to follow and raises questions for which neither education nor the labour market are fully prepared.
The core issue: students’ dependence on AI and lack of deep understanding
The problem is particularly visible in computer science education. Students often use AI skillfully — solving exercises, generating code, configuring systems — yet difficulties arise when they must perform without AI support. Eigner gave an example of an exam situation: students who had performed well during the semester with AI assistance got stuck when asked to type several specific commands independently. This is not merely a cheating or prohibition issue; it is about whether durable, abstract understanding is being formed that can be applied in new situations.
AI can help a student get through a task, but that does not guarantee the development of the abstraction skills needed for future problems.
Strategic responses are required: more than IT upgrades
Eigner stresses that universities need strategic responses rather than mere technical upgrades. The question is not whether to have AI, but within what frameworks, with what educational philosophy, and with which human roles it will be used. He likened the necessary transformation in scale to the introduction of the Bologna system, but argued that the process must be faster and more flexible because AI is here to stay and raises the bar for convenience and quality of life, making it progressively harder not to use it.
Juniors and teachers will need different competencies
The change will particularly affect junior IT professionals. Eigner expects juniors will still be needed, but in different roles: routine entry-level tasks may be taken over by AI, so newcomers must be able to understand complex systems, design responsibly, and adopt stronger business and analytical mindsets. It will not be enough to know what the machine writes — they must understand what it does.
At the same time, the role of instructors can change: AI could free teachers to focus on what they arguably should have focused on all along — mentoring, nurturing talent, promoting social mobility and conveying human quality. Technical, repetitive work may increasingly be handled by AI, but human standards, culture and responsibility will remain.
A call for coordinated institutional planning
The dean has proposed that multiple higher education institutions with computer science programs come together to jointly rethink the future of informatics education. He believes a transformation of similar significance to past major reforms is needed, implemented faster and with more flexible frameworks.
Topics covered in the podcast (timestamps)
- What is the mood among IT professionals about AI? (01:04)
- How do students use AI? (05:22)
- Could AI stifle innovation? (09:15)
- AI is no longer the future, it is the present (11:24)
- Why should computing education be rethought now? (12:05)
- What role can universities keep in the AI era? (14:59)
- How might the teacher’s role change? (19:38)
- What will happen to entry-level IT professionals? (24:46)
- How will humans and AI-agents work together? (35:34)
Universities therefore need rapid, well-considered strategies to ensure that the spread of AI strengthens genuine understanding and the social mission of education rather than undermining the essence of learning.
