Interview Fit for AI: How to Harness Artificial Intelligence and Machine Learning
Dr. Steffen Schneider explains the opportunities of AI in science – and why it is crucial to start the discussion in schools. With 'KI macht Schule' he brings AI literacy into the classroom.
Dr. Steffen Schneider explains the opportunities of AI in science – and why it is crucial to start the discussion in schools
“My goal is to empower everyone to use AI with confidence and inspire the next generation to shape its future!"
Dr. Steffen Schneider, Head of the Dynamical Inference Lab, Helmholtz Munich
Dr. Steffen Schneider researches the use of artificial intelligence (AI) in neuroscience and is also committed to promoting diversity and educational equity. In 2019, he founded the initiative “KI macht Schule” and has since been bringing artificial intelligence into classrooms. Through courses, professional development for teachers, and an open platform featuring lesson materials and AI tools, he empowers students and educators to become “Fit for AI.” For his commitment, he was honored with several awards, including the 2025 academics Young Talent Award.
You founded the initiative “KI macht Schule” – why?
StS: As early as 2019, it became clear that AI, as a cross-cutting technology, would fundamentally transform our society. That year saw a boom in scientific publications. Along with a group of students and recent graduates, I laid the foundation for “KI macht Schule” to ensure early access to solid AI knowledge.
Two years later, we founded it as a nonprofit – and at the time, it was probably the only German initiative focused on AI education. When ChatGPT launched three years later, demand for our offerings exploded.
My goal is for everyone to handle AI safely – and to spark excitement in students to actively shape this technology. That’s even more important now, because AI presents real challenges to the education system, and schools are the best way to reach everyone directly, regardless of educational background.
“I advocate for science communication so that research is noticed.“
Dr. Steffen Schneider
How else do you bring AI knowledge into society?
StS: AI education doesn’t end with high school. At the university level, I also think it’s crucial to expose as many students as possible to AI. In both learning and research, it's essential to be able to use AI tools.
To that end, we’re launching a BMBF-funded supplement program on AI & Health in collaboration with the DZD this year.
I’m also involved in doctoral education. In my research group, I talk about the many ways to engage and encourage efforts in impactful science communication – because without it, scientific achievements stay hidden.
Why is public engagement important to you?
©smolaw11 - stock.adobe.comStS: I believe it’s essential for people from all educational backgrounds to make informed decisions with the help of AI. In matters of regulation and AI governance, it’s not scientists who decide – it’s the public, through democratic processes.
Back in 2018, during a fellowship with the German National Academic Foundation, we conducted street surveys in Cologne. We quickly saw that most people knew very little about AI. And why would they? There was no widespread media coverage yet – ChatGPT didn’t exist. Meanwhile, in the scientific community, AI had already been a hot topic for over six years. I believe it’s crucial to inform the public early about such turning points in technology.
“With generative AI, we humans shift more into a delegating role. But that’s a skill we need to learn first!“
Dr. Steffen Schneider
What ethical and social challenges come with using AI?
StS: There are many! In Europe, we need to think carefully about how we want to position ourselves compared to the U.S. and China and their AI strategies. Which areas of application should be encouraged, and which regulated?
In education, the biggest challenge is that learning itself must change fundamentally. Generative AI moves us toward a more delegating approach to work. That’s a process that must be learned – but the current system often doesn’t prepare us for it.
What are the benefits of learning AI skills in school?
Christian Schwier - stock.adobe.comStS: Education is the foundation for making Europe and Germany long-term leaders in this future technology. In addition to excellent research and innovative companies, we need passionate young people who are excited about tech and bring a European perspective to the global race for the best AI models.
The biggest lever, in my view, is to focus more on project-based learning and interdisciplinary skills in schools.
"Education is the foundation for making Europe and Germany long-term leaders in this future technology.“
Dr. Steffen Schneider
What are the benefits of linking an initiative like “KI macht Schule” to Helmholtz Munich?
StS: Research centers already invest in science communication and use public funds for it. Over the past five years, we’ve seen in countless conversations with stakeholders that many innovative concepts and high-quality formats are developed – but often only benefit participants of isolated events.
Initiatives like “KI macht Schule” bundle and amplify that work. Because we operate for the public good, it’s not about making money—it’s about maximizing impact, especially when public funding is involved.
That’s why we see huge potential in connecting scientific institutions with public-interest initiatives like “KI macht Schule” – and I’m glad Helmholtz Munich strongly supports this work.
You were named “Early Career Scientist of the Year 2024” by academics. What does this award mean to you?
StS: I was incredibly honored to receive this recognition – especially because it acknowledges the combination of scientific research and public outreach.
I’m very grateful to academics for the visibility this award gives to both our research at Helmholtz Munich and “KI macht Schule.”
academics Young Talent Award
The academics Young Talent Award honors early-career researchers who not only contribute visionary research but also show exemplary dedication and volunteerism in support of science. It is being presented for the 18th consecutive year.
How would you explain your research to someone without a science background?
StS: Imagine Leonie, who just received a bicycle for her fourth birthday. After some practice, she figures out how it works and how to use it to reach her goal – getting from point A to point B.
We, as scientists, are similarly interested in how Leonie understands, models, and controls the dynamics of the “bicycle system” to achieve her goal. Of course, we’re not studying bicycles – we’re studying cell cultures, model organisms, and other experimental setups – in other words, dynamic biological processes that unfold over time.
We collect data from these processes and figure out how to influence them – to steer the system toward a desired state. Just like Leonie anticipates what will happen when she pushes the pedals.
What dynamic biological processes are you studying
Helmholtz Munich | © N.N.StS: I believe the most fascinating dynamic system is the brain itself. It controls our bodies and, in turn, affects our environment. With our research, we aim to better understand this interplay.
The data we work with may include, for instance, the activity of neurons in the brain or the behavior of a biological model in an experiment.
Which diseases could benefit from your new approaches?
StS: Our methods aren’t tied to a specific application – they’re useful whenever data unfolds over time: the behavior of a biological model, neuronal activity, or the interaction of cells in a petri dish over the course of a day.
These are complex systems, and we aim to describe them. Diseases can be seen as deviations from normal system behavior.
We collaborate with other research groups investigating neurodegenerative diseases like Alzheimer’s, or studying how chronic pain is perceived. There’s also great potential in basic medical research. Together with the German Mouse Clinic at Helmholtz Munich, we’re developing better methods to analyze time-based data and more precisely describe mouse models – a crucial step in preclinical research.
Lila Patel - stock.adobe.com„We develop reliable and interpretable machine learning methods to understand how biological systems process information. It’s like reverse engineering for biology."
Steffen Schneider
What potential does AI have specifically for neurodegenerative diseases?
StS: In neuroscience, we can now record vast amounts of data. Depending on the model organism, that could include the entire brain and body state – or at least high-resolution data from various brain regions.
Yet there are still too few ways to extract real insight from this data. Especially for brain disorders, I think it’s crucial to accurately model the “normal state” first – only then can we recognize, describe, and eventually treat deviations.
That’s why we’re building the right methods – developed and applied in close collaboration with researchers who are addressing specific questions in both model organisms and human studies.
Expert Knowledge: Machine Learning (ML)
Machine learning is a subfield of artificial intelligence. It describes the ability of computers to learn from data and improve automatically without being explicitly programmed.
Basic principle:
An ML model is trained on data to identify patterns and relationships. Once trained, it can make predictions or analyze new data.
Find Out More About Dr. Steffen Schneider
Award: Dr. Steffen Schneider Named Early Career Scientist of the Year 2024
Visit: Schneider Lab
Contact: steffen.schneider@helmholtz-munich.de
Swiss Society for Neuroscience Best Publication Award 2025
Dr. Steffen Schneider received the Best Publication Award 2025 from the Swiss Society for Neuroscience for his publication:
Learnable latent embeddings for joint behavioural and neural analysis
Research & News
- "KI macht Schule"
- "Dynamical Inference Lab"
- "CEBRA"
- Graduate School of Systemic Neurosciences (GSN)
- Munich Center for Machine Learning
- Learn more about Foundation Models
Latest update: May 2025.