About Christian Cahannes and SteP-met in Switzerland
SteP-met grew out of educational practice. As a teacher, I initially focused on personalised teaching materials, and later on, AI-powered learning aids. These experiences led on to a concept that now extends far beyond the classroom: securely connecting existing, authorised information with context-based AI.
From classroom practice to the idea for SteP-met
The idea for SteP-met emerged from my work as a teacher. It began with creating personalised teaching materials. Everyday school life showed me how important it is to not merely provide learners with generalised content, but to take their own individual starting points and specific learning contexts into account.
This approach later led to the development of AI-supported learning aids, particularly in the field of learning German as a second language (DaZ). The work helped me to focus on a fundamental question: Precisely how can AI provide targeted support to learners when it can use not just general knowledge but also relevant information from the actual learning context?
Context relevance rather than disconnected generative AI
My practical work with AI learning aids led me to a key insight: The real value of AI lies not just in generating new content. It becomes especially valuable when relevant information from a specific context is intentionally used as the basis for its support, analysis and recommendations.
This idea is the common thread running through SteP-met today. The system is not intended to operate in isolation. Instead, existing, authorised information from established data sources and platforms is made available for a defined usage strategy.
This basic principle can be summarised as follows:
Data & systems → secure integration → context → AI → targeted support
The focus is therefore not on AI alone, but on the meaningful linking of data, integration, context with a clearly defined function.
A pedagogical approach becomes versatile
The original educational context evolved into a more broadly applicable SteP-met concept. Today, this approach is designed and intended for use in education, corporate training, sports, business and customer support.
In educational scenarios, for example, learning progress, assignments and learning pathways may form part of the relevant context, whereas in corporate training, the focus is on existing training data and competency models. Elsewhere, in sports, performance, match and training data can be created and utilised, while in business, support, knowledge bases, products, processes and support information can provide the essential foundation.
Though the application areas differ, the underlying principle remains the same: Existing data and systems are connected to context-based AI via suitable interfaces.
SteP-met as a partner for clearly defined AI projects
SteP-met is aimed at businesses and institutions that want to make more targeted use of their existing information. These include: educational institutions ranging from secondary schools and vocational education to higher professional education and universities, as well as sports clubs, businesses and corporate training providers.
For collaborative ventures, the initial focus is on clearly defined pilot projects. Together, we consider a specific usage strategy with a defined data source, limited scope, a suitable user group, and a clear timeframe. This makes it possible to accurately assess the practical value context-based AI can create in the respective environment.
Turn a specific need into a meaningful AI usage strategy