Innovation in der digitalen Bildungslandschaft

Das Interview mit Prof. Dr. Mutlu Cukurova

Let’s start with your personal approach to what you are doing professionally. What drew you to this field? Why is education important to you?

Oh wow! It’s been a while since I reflected on these questions. But I guess it would be fair to say that I have always been interested in how humans learn and how intelligence works. My background is originally in science and engineering, and then I did my Master’s in analytics before pursuing a PhD in learning sciences. I have been designing and developing various computational and adaptive learning support systems, and that has remained my central focus. Of course, education is much broader than just human learning, but my initial interest in education stemmed from my fascination with learning itself. I was always fascinated by the idea of computationally modeling human learning – not only to understand it better but also to support it more effectively. I believe that is at the core of education.

Let’s stay with a pragmatic perspective on learning for a moment. How would you define successful education in this new landscape where digitality, AI, and other emerging conditions shape the learning environment?

Well, that’s a billion-dollar question. My argument in these discussions is that there are fundamental differences between human intelligence and artificial intelligence. AI, or digital technologies in general, do not process emotions, moral reasoning, or context in the same way humans do. That is to say that human vulnerabilities and limitations shape the way we process information – and, in turn, the way we make decisions.

I start with these differences because, for me, understanding them is key to defining successful education. In my view, education should go beyond straightforward knowledge acquisition and simple information processing tasks. Instead, it should cultivate uniquely human capacities: empathy, ethical and moral reasoning, theory of mind (the ability to consider the perspective of another), as well as our values and virtues. These are the fundamental qualities that shape how we think about the world, and I believe their development should be at the heart of a meaningful education. So, in essence, I see successful education as one that helps people nurture and refine these uniquely human qualities. Does that make sense?

It does! What I feel compelled to ask here is: Should we, in the context of education, consciously focus on what remains uniquely human? AI cannot enjoy fresh air or feel the bark of a tree nor have similar tactile or aesthetic experiences. This also reminds me of Nicholas Maxwell’s wisdom narrative – the idea that we should value more than knowledge and ought to prioritize “wisdom” over mere accumulation of information. Do you think we should educate for wisdom rather than just knowledge?

Yeah, that’s an interesting question. Many modern human learning theories – including those rooted in constructivist approaches – view knowledge construction as a highly subjective, shared process rather than as an isolated, intracranial activity where individuals simply accumulate information stacks in their brains. Constructivist learning theories generally emphasize the importance of embodied learning experiences – like the example you gave, where interaction with the physical world plays a key role. These theories would also resist reducing core educational constructs, such as wisdom, into simplistic, reductionist representations. Instead, they highlight the value of lived learning experiences as the foundation for developing essential competencies and skills. Through real-world, open-ended problem-solving and social interactions, individuals cultivate these abilities. So, if we consider wisdom as something that emerges from lived experiences, then it should indeed be at the core of education. Most constructivist learning theories would support this idea.

The challenge, however, is that we still lack well-established methods to measure progress in these areas. And if something cannot be effectively measured, it becomes difficult for education systems to implement it at scale. Bringing the discussion back to AI, the reality is that most digital learning technologies today focus on the aspects of learning that are easiest to automate – things like content acquisition in intelligent tutoring systems or AI-driven content creation tools that help teachers save time. However, innovative constructivist pedagogies supported by AI are still rare and much harder to implement effectively.

You make an interesting point on the necessity to measure learning progress. Given that we have to rank people – to assign degrees, admit students to universities, and qualify people for jobs – there is a lively discourse around the knowledge and skills that should actually be taught. Many argue that the rapid pace of change makes it nearly impossible to determine what students should learn. What kinds of knowledge and skills do you think will remain essential?

That’s a question I think about a lot – both in my role as a professor and from a research perspective. As a university lecturer, I constantly ask myself: What will still be relevant in five years for the students I’m teaching today? I teach Design and Use of AI in Education at UCL, and in the past few years, I have never used the exact same course materials from one year to the next. The field is evolving so quickly that constant updates are necessary. So yes, it’s incredibly difficult to predict what specific knowledge students will need in the future. But there are a few things I’m quite certain about:

First, as we discussed earlier, human learning is fundamentally constructive – we build new knowledge by connecting it to what we already know. Unlike machines, which can process isolated pieces of information, humans don’t learn in a vacuum. This means we cannot justify eliminating foundational knowledge just because new, more “relevant” information emerges. Take mathematics as an example. Just because we have calculators, we have not stopped teaching basic arithmetic. Students need to understand multiplication and basic mathematical operations to build toward more advanced topics like calculus. So, whenever people argue that education should only focus on the most up-to-date knowledge, I think it’s important to push back. We still need to teach fundamental concepts that allow students to engage with future developments in a meaningful way.

Now, from a skills perspective, this question becomes even more interesting. To me, the most critical skills are those that support learning itself – things like learning how to learn, adaptability, and cognitive flexibility. Given the rapid changes in technology and the job market, people will constantly need to acquire new knowledge and adjust to new environments. That is why self-regulation skills, social regulation skills, and metacognitive skills are so important. The ability to reflect on one’s own learning process, adapt strategies, and work collaboratively will only become more valuable. But these competencies need to be integrated into education systems as core components– not just as optional “nice-to-haves.”