Research-led courses
Current research becomes cases, methods and assignments rather than remaining separate from the classroom.
Teaching & mentorship
Teaching people to build—and question—responsible AI
Research-led education in AI, recommender systems, data science and digital media, from university courses and supervision to executive and public audiences.
AI systemsHow recommendation and personalisation work
Human behaviourHow technologies influence choices and trust
Research methodsHow to design rigorous empirical studies
Responsible practiceHow to evaluate consequences, values and risk
35+university courses
2,000+students taught
35+MSc and PhD theses supervised
BSc–PhDplus executive audiences
Teaching philosophy
Technical competence is not enough
Students and professionals should understand how AI systems are built, how evidence about them is produced, and how their effects on people and society can be evaluated critically.
My teaching connects algorithms and data with behavioural theory, user-centred evaluation and responsible innovation. Learners work with concrete systems and cases while developing the ability to ask better questions: What is being optimised? Who benefits? What evidence supports the claim? What happens when the system meets the real world?
Educational contribution
Four complementary forms of teaching
MSc & PhD supervision
Support from research question and study design through analysis, writing and scholarly contribution.
Project-based learning
Students connect theory with prototypes, datasets, user studies and real organisational problems.
Executive & public education
Complex responsible-AI questions translated into useful frameworks for leadership and professional audiences.
Learning journey
From a good question to a defensible contribution
A recurring structure in courses and supervision is to connect conceptual understanding with practical and empirical work.
Step 1Frame the problemIdentify the human, technical and institutional question that matters.
Step 2Understand the evidenceRead critically, define concepts and distinguish claims from assumptions.
Step 3Design the studySelect methods, data and evaluation criteria that fit the question.
Step 4Build and testDevelop prototypes or analyses and evaluate them with appropriate users or data.
Step 5Communicate responsiblyExplain findings, uncertainty, limitations and implications clearly.
Mentorship in practice
Supporting researchers, not only projects
Finding a coherent direction
I help students turn broad interests into focused, feasible and theoretically meaningful research questions.
Developing rigorous evidence
Supervision connects method choices with the claims a study can responsibly make, including limitations and uncertainty.
Moving toward contribution
Strong student work can develop into publications, prototypes, partner learning or a foundation for further research.
Selected teaching portfolio
Representative topics and courses
Examples from university teaching and invited lectures across Norway, Austria and international settings.
Recommender Systems
Algorithms, evaluation, user experience, bias and responsible personalisation.
Information Systems
How organisations design, manage and evaluate digital technologies and data-driven services.
Data Mining & Predictive Modelling
Models, behavioural data and critical interpretation of predictive performance.
Web Science & Web Technology
Networks, social systems, information access and the behavioural dynamics of the web.
Multimedia Information Systems
Search, recommendation and interaction across multimedia and social information environments.
Research Methods & Scientific Working
Study design, evaluation, scholarly argument and clear communication of evidence.
Current contribution
Supervision, mentorship and invited education
My present educational contribution focuses primarily on doctoral and master’s supervision, research mentorship, invited teaching, guest lectures, executive education and public engagement in responsible AI, recommender systems and computational user behaviour.
The course archive below documents earlier teaching experience. It is retained as a historical record rather than presented as a current semester schedule.
Course archive
Detailed teaching history
Open detailed course record (2010–2021)
2021
- 2021: Recommender Systems at MODUL University Vienna, Lecturer
- 2021: Recommender Systems: "Food Recommender Systems" at the University of Bergen, Invited Lecturer
2020
- 2020: Information Systems at the University of Bergen, Lecturer
- 2020: Recommender Systems at MODUL University Vienna, Lecturer
- 2020: Information Systems Management at MODUL University Vienna, Lecturer
2019
- 2019: Information Systems at the University of Bergen, Lecturer
2018
- 2018: Research Topics in Recommender Systems at the University of Bergen, Lecturer
- 2018: Information Systems at the University of Bergen, Lecturer
- 2018: Information Systems Management at MODUL University Vienna, Lecturer
- 2018: Recommender Systems at Graz University of Technology, Invited Lecturer
2017
- 2017: Information Systems at MODUL University Vienna, Lecturer
- 2017: Information Systems Management at MODUL University Vienna, Lecturer
- 2017: Marketing Intelligence at MODUL University Vienna, Lecturer
- 2017: Emerging Tools for New Media and Information Management at MODUL University Vienna, Lecturer
- 2017: Recommender Systems at Graz University of Technology, Lecturer
2016
- 2016: Web Technology at Graz University of Technology, Lecturer
- 2016: Recommender Systems at Graz University of Technology, Invited Lecturer
- 2016: Master Project at Graz University of Technology, Lecturer
- 2016: Diploma Seminar at Graz University of Technology, Lecturer
2015
- 2015: Multimedia Information Systems at Graz University of Technology, Lecturer
- 2015: Data Mining at NTNU: "Cognitive Models in Recommender Systems", Invited Lecturer
- 2015: Master Project at Graz University of Technology, Lecturer
- 2015: Diploma Seminar at Graz University of Technology, Lecturer
- 2015: Introduction to Scientific Working at Graz University of Technology, Lecturer
2014
- 2014: Master Project at Graz University of Technology, Lecturer
- 2014: Diploma Seminar at Graz University of Technology, Lecturer
- 2014: Introduction to Scientific Working at Graz University of Technology, Lecturer
- 2014: Multimedia Information Systems 1: "Current trends in Social Computing" at Graz University of Technology, Invited Lecturer
- 2014: Evaluation Methodology: "Crowed based evaluation methods" at Graz University of Technology, Invited Lecturer
- 2014: Recommender Systems: "Content & Graph-based recommender systems in social tagging systems" at PUC, Chile, Invited Lecturer
- 2014: Introduction to Knowledge Management: "Rule Based Systems" at Graz University of Technology, Invited lecturer
- 2014: Web Science and Web Technology at Graz University of Technology, Lecturer
2013
- 2013: Introduction to Knowledge Management at Graz University of Technology, Lecturer
2012
- 2012: Web Science and Web Technology: "Selected Topics: Tag-Based Navigation" at Graz University of Technology, Invited Lecturer
2011
- 2011: Web Science and Web Technology: "Current Research on Tagging Systems" at Graz University of Technology, Invited Lecturer
- 2011: Networks Navigability: Theory and Applications at University of Ohrid, Macedonia, Invited Lecturer
2010
- 2010: Databases 1: "Introduction to MySql" at Graz University of Technology, Invited Lecturer
I am happy to provide references for former students whose course or research work I know well. A useful reference requires enough direct interaction for a specific and evidence-based assessment.
Looking for a guest lecture or executive session?
I offer research-grounded sessions on responsible AI, recommender systems, human behaviour and trustworthy technology.