AI Hackathon: Professionalization Done Right

From manual labor to high-tech

After my presentation, I walked around and talked with various teams. There was a surprising moment when I was approached by two hands-on teachers who were at their wits’ end. “How on earth can we incorporate AI into our classes, which are mainly about working with your hands and developing skills?” they wondered in despair. The answer lies in a shift in thinking. By combining the power of AI with hands-on skills, we can even take hands-on crafts to a whole new level.

Imagine starting a lesson by trying to create a painting with AI using a specific technique, and then actually creating the result yourself with a paintbrush. Or how about using AI to inspire unique design ideas for art projects, as a brainstorming partner in the creative process? Perhaps even AI-driven feedback on the ergonomics of movements during hands-on tasks. And from the teacher’s perspective: evaluating the artworks created by the students. There are so many possibilities.

More Than Just a Game

A hackathon is not only a powerful format for educational innovation, but also for teacher professional development. Instead of endless meetings about potential AI applications, participants are encouraged in a playful way to take immediate action. The result? Surprising solutions that are directly applicable to real-world situations.

Take, for example, the winning team from the Royal Auris Group. Their tool, FABULA, uses just a few keywords to generate complete stories with matching illustrations, specifically designed for students with language learning difficulties. This team won because of the immediate practicality of their solution and its potential impact on special education. FABULA demonstrates how AI can be used to make education more inclusive and effective. You can read more about their solution here.

The Other Side of the AI Coin

But it’s not always all sunshine and roses. In my presentation, I emphasized that there are also serious risks, particularly as they relate to the learning process. There are three risks that we, as teachers, need to pay explicit attention to:

1. Dependency: We must prevent students from becoming so dependent on AI that they no longer develop essential skills on their own. Sometimes, outsourcing cognitive tasks to technology isn’t such a bad thing. Think about how we used to memorize phone numbers, but now rely entirely on our smartphones. But in other cases, you undermine your own skills—for example, when you rely too heavily on navigation systems, you compromise your natural sense of direction.

1. Bias: AI systems and algorithms are not neutral. They reflect the biases in the data used to train them and the way they are built (and by whom). We must remain critical of potential biases in AI systems and teach our students how to recognize and address them.

1. Bullshit: AI can sound convincing, even when it’s factually incorrect. In fact, research even shows that AI now comes across as more reliable and empathetic than real people. But while it’s designed to sound human, it isn’t necessarily built to tell the truth. We must therefore teach ourselves and our students to always remain critical and to verify the output of AI systems.

From Final Product to Learning Process

In the presentation, I also touched on the challenge of authenticity and integrity. We need to reevaluate our assessment system. Our current paradigm, with its focus on final products and grades, often encourages the path of least resistance. And let’s be honest: student behavior is simply a reflection of what we organize.

By going back to the drawing board and using tools such as the AI Assessment Scale, we can transform our assessment practices—from snapshot evaluations to an integral part of every learning process, from start to finish.

AI in Education: The Practical Side

The great thing is: we don’t have to wait for the future. AI education projects are popping up all over the Netherlands. The Dutch AI Coalition recently published an overview of inspiring projects, and the National Education Lab AI also recently shared an overview of the current state of affairs. The Edu-AI community on WhatsApp has also published a collection of videos showcasing successful applications. These are wonderful use cases in which AI is being used responsibly.

In conclusion

AI in education is not a question of “if,” but of “how.” It’s not about blindly implementing every new tool, but about thoughtfully using AI as a means to achieve our educational goals. Whether it’s grading tests, enriching lessons, or providing real-time feedback during presentations—the possibilities are endless.

So, let’s experiment, learn, and above all: don’t be afraid to fail. Because those failures often contain the seeds of the greatest breakthroughs. The future of education won’t wait—let’s shape it together.