Inspiring the next generation of AI experts through education

Artificial intelligence is advancing rapidly. Concepts such as large language models and deep learning increasingly shape public debate. But for many students, these topics can also feel abstract, highly technical, and difficult to navigate.
Good teaching makes the difference between simply using AI tools and truly understanding how they work, where their limitations lie, and why trustworthiness matters.

Recognition from students

Prof. Ivan Habernal has been nominated for the Excellent Teaching Award (ETA) 2026 at the Faculty of Computer Science at Ruhr University Bochum. He was nominated in the category of elective modules for his course Natural Language Processing with Deep Learning.
The nomination carries particular significance because the process is driven by students themselves. Students first propose lecturers for the award, after which a student jury evaluates the submissions, reviews teaching concepts, and selects the finalists and winners.
The ETA has been awarded since 2024 to recognize outstanding contributions to teaching across lectures, seminars, labs, and practical courses.

Making modern NLP accessible

Natural language processing has become one of the most visible areas of AI research. From chatbots to automated translation and generative AI systems, language technologies increasingly influence how people search for information, communicate, and work with digital systems.
Teaching these topics, however, requires more than explaining technical models. Students must learn how such systems are trained, how they process information, and why issues such as bias, transparency, and privacy matter in practice.
This connection between technical depth and critical reflection is central to Ivan Habernal’s work in teaching and research alike.

From trustworthy AI research into the classroom

Ivan Habernal is Professor of Trustworthy Human Language Technologies at Ruhr University Bochum and a member of the Research Center Trustworthy Data Science and Security (RC Trust), where he leads the Trustworthy Human Language Technologies (TrustHLT) group.
His research focuses on privacy-preserving NLP and legal NLP, with the broader goal of making language technologies more trustworthy, transparent, and reliable in sensitive domains.
The nomination for the ETA reflects another side of this work: translating complex ideas into teaching that resonates with students and prepares them to critically engage with modern AI systems.

Celebrating teaching and community

The winners of the Excellent Teaching Award will be announced during the Grand Celebration 2026 of the Faculty of Computer Science on June 19, 2026. The event combines the annual graduation ceremony with a summer celebration for students, researchers, and staff.
Alongside academic achievements, the celebration highlights the people who support and shape learning at the faculty–from teaching and mentoring to research and community engagement.
For RC Trust, the nomination also underlines an important idea: trustworthy AI does not only depend on good research. It also depends on how the next generation of researchers and developers is taught to understand and question these technologies responsibly.