‘Students are excited about AI, but feel uncertain about the future’

In the context of AI becoming one of the foundational technologies of the digital economy, universities must change training methods to keep up with labor market needs. Share with VnExpress On the sidelines of the South Summit 2026 event on June 3-5 in Madrid, Ms. Rafif Srour, Head of Programs at the School of Science and Technology – IE University (Spain), analyzed the skills students need to equip, the new role of higher education and how to balance technological capabilities with critical thinking in the age of AI.

– Last month, students in the US booed former Google CEO Eric Schmidt’s AI graduation speech, showing growing anxiety about the impact of AI on the future. How is a similar reaction recorded among IE University students?

– We have not seen such a reaction from our students, but are certainly seeing growing concerns around AI and the future of work. Interestingly, anxiety levels vary significantly depending on the stage of a student’s academic journey.

Freshmen are generally quite excited about AI. They see this as an empowering technology that holds many opportunities. However, many children also underestimate the importance of building a solid foundation in mathematics, statistics and algorithmic thinking, because AI tools can now generate answers almost instantly. A major educational challenge of ours is to help students understand the core technical foundations are still very important, maybe even more important than before.

The group with the highest level of anxiety is students preparing to graduate. Although there is no large-scale data yet, through talking with students and businesses, I can sense concerns about the labor market. Entry-level hiring in some technology fields is becoming more competitive, and students are starting to question how their careers will change in an AI-driven economy.

I think the dominant emotion right now is not fear, but uncertainty. Students are excited about the possibilities AI offers, but are also trying to imagine what their role will be in a rapidly changing world.

– You mentioned a world that is changing very quickly, so have universities adapted quickly enough to AI or are they still continuing to train for “a world that no longer exists”?

– I think universities around the world are facing the same problem. It is unrealistic to continue teaching the way it was five years ago.

Most educational institutions have tried to adapt, but there are structural reasons why change in the academic environment has been slower. However, the more flexible a school is, the more competitive it will be in attracting and retaining students. For example, at IE, we update our curriculum every year for data science, applied mathematics and computer science, at both the undergraduate and graduate levels. Topics such as data architecture, large language models (LLM) or agentic AI have been integrated into the curriculum.

That’s just the most visible change, though. The deeper challenge is not simply to add AI-related subjects, but to rethink the entire pedagogical model. AI not only changes what students need to learn, but also changes how they learn, how they work, and how they interact with knowledge.

– AI changes the way students learn and work. What are the most important skills universities of technology need to help them thrive in the next five years?

– Any discussion about the skills of the future needs to start with understanding the characteristics of the current generation of students. Children grow up in an environment of instant answers, short attention spans and constant digital stimulation. So, as educators, we need to reinforce skills that require depth rather than speed.

In-depth analytical thinking, synthesis ability and critical thinking will become increasingly important. Even if AI systems can generate summaries or provide solutions, students still need the ability to evaluate information, identify weak arguments, and connect ideas across different fields.

In the future, AI may become better at solving technical problems, but humans must still ask the right questions, filter out information noise, define clear goals, and make decisions under conditions of uncertainty.

I also believe teamwork, adaptability and systems thinking will become essential skills. Successful people in the AI ​​era do not necessarily remember the most information, but can integrate knowledge, collaborate effectively and work with intelligent systems.

– Faced with that need, what are the common mistakes that universities make when rushing to open AI training programs?

– A big mistake is viewing AI as a temporary trend instead of a field built on a deep technical foundation. There is even a view that computer science could become obsolete because of AI. In my opinion, the opposite is true.

AI is both a tool and a core competency. Implementing AI programs too quickly, without truly understanding the needs of the industry or the long-term skills that students need, often leads to superficial programs that only focus on “hot” applications.

A thorough AI training program needs a solid foundation in computer science, mathematics, statistics, systems engineering and algorithmic thinking. Students need to not only learn how to use AI systems, but understand how to build, test, and critically evaluate them.

I believe that strengthening the computer science foundation while strategically integrating AI engineering is a much more sustainable path to training the next generation of AI workers, including users and developers.

– But even though the training program has changed, if AI can automate work, what opportunities will there be for new graduates to gain experience?

– This is one of the most important questions we need to ask right now, and honestly, one of my biggest concerns.

Previously, when entering the profession, young engineers and analysts could build professional intuition, develop technical depth and learn from practical experience. But if more jobs are automated, we risk facing an expertise gap in the future, as the younger generation may lose the opportunity to develop a deep level of practical understanding.

Through discussions with many business leaders, I realize that this concern is increasingly common. Many people are starting to fear that in the future we will lack engineers who truly understand the underlying system, because so many processes have been automated from the beginning.

In my opinion, the connection between businesses and university training programs must be strongly strengthened. Students need to access the real world sooner through projects, labs, internship programs, applied learning and corporate collaboration. At IE, this is also something we are proactively promoting in our STEM programs.

The challenge is no longer simply to impart knowledge, but to create truly effective pathways for students to gain experience in an AI-enhanced workplace.

– As a leader at a technology school, what do you think about the notion that in the AI ​​era, programming skills may become less important?

– This view is partly correct. Advanced AI systems can produce programming code at a level that surpasses even many junior (basic) engineers. So the role of the programmer will definitely change.

However, that doesn’t mean coding will disappear. Students still need enough technical understanding to verify, interpret, troubleshoot, optimize, and critically evaluate AI-generated solutions. Coding in the future may be less about writing each line of code and more about monitoring, coordinating, and improving intelligent systems.

In many cases, the biggest challenge is no longer solving a technical problem, but identifying the right problem to solve in the first place. The engineer of the future will be someone who combines technical understanding with strategic thinking and interdisciplinary capabilities.

– If you were to design a university from scratch in the AI ​​era, what would you do differently?

– I will rethink three big areas: assessment, teaching methods and the level of engagement with the business.

The evaluation system needs to change fundamentally. The traditional model – which relies heavily on memorization and standardized tests – is gradually becoming less relevant in a world where AI can retrieve and produce information almost instantaneously.

I will also design the program around applied learning, project-based learning, and interdisciplinary learning from the beginning. Students need to continuously work on practical problems instead of separating theory and practice.

Besides, I will integrate business more deeply into the education model. Universities cannot operate in isolation from the pace of technological change. Faculty, research and training programs need to be closely connected to changing market realities.

However, I will still maintain a rigorous training in mathematics, science, and analytical thinking. The AI ​​era does not diminish the importance of core platforms; If anything, it makes them even more important.

– Vietnam has a young student force and is very interested in technology. In your opinion, what is the biggest advantage of emerging markets in the AI ​​race?

– One of the biggest advantages of emerging markets is flexibility.

Many emerging economies are building new digital ecosystems instead of modernizing decades-old systems and infrastructure. They are often less constrained by fragmented organizational structures, legacy processes or outdated technology platforms that can slow innovation in more mature markets.

This creates the opportunity to deploy AI-native systems (designed from the ground up with AI as a core component) faster and more flexibly. In some cases, emerging markets can even “leapfrog” traditional stages of technology development.

There is also a cultural advantage. Young populations in emerging markets often have high adaptability, a strong entrepreneurial spirit and a strong drive to enter technology-based industries. When combined with ever-better access to digital infrastructure and global knowledge platforms, this creates huge potential for high-speed innovation.

Meanwhile, established markets sometimes face greater inertia during transformation, as they must update, automate and reengineer existing systems at scale. In contrast, emerging markets have the opportunity to design completely new systems from the ground up with AI at the core.