After Digital Literacy comes AI Literacy

Whenever the topic of social media and a possible social media ban comes up, the same call is heard time and again: «the solution lies not in bans, but in greater literacy».

We are now seeing exactly the same pattern when concerns about AI are discussed. AI literacy, much like digital literacy, is particularly often called for when people wish to oppose further regulation. It is therefore hardly surprising that many business stakeholders are also calling for literacy in these debates.

We are constantly asked what we actually mean by AI literacy or digital literacy in general. As digital literacy has been one of our key focus areas for around eight years, we are taking the Federal Council’s latest report and the ongoing discussion on AI literacy as an opportunity to explain why our relationship with this term is an ambivalent one.

Application competence are not the same as literacy

Before we delve into AI literacy, it is worth clearing up a common misunderstanding: application competence and literacy are two different things.

Application competence means being able to operate a tool. Literacy means understanding a system (its mechanics, its incentives, its consequences) in such a way that one can make one’s own decisions, rather than merely reacting. It is not a question of «I can use the tool», but rather «I understand what the tool does to me when I use it, and can form an position based on that».

This distinction was already apparent around ten years ago, when there were widespread calls for all children to learn to code. In principle, there is nothing wrong with this, but it remains a matter of application competence. Coding was, in fact, not the guarantee of job security that had been predicted at the time. On the contrary: precisely because of new technological developments such as AI, it is likely that fewer coders will be needed. Digital literacy, on the other hand, requires not only learning how to use technology, but also critically reflecting on digitalisation. Our guiding principle here is that digitalisation should serve society, not the other way round.

Literacy also has a temporal dimension which is not inherent in the word itself, but which resonates within the context.

A simple example: the ability to read is stable; a book from 1990 is read in the same way as one from 2026. Digital or AI systems, on the other hand, change whilst one is trying to understand them. Literacy here therefore means not only understanding how something works, but also being able to apply a cultural technique that makes these modes of operation tangible even as they change.

Literacy cannot, therefore, be a fixed body of knowledge. How does the algorithm work that shows or generates something for me? Who profits from what when I use it? Who owns my data as soon as I tap «Accept»? The answers change. The ability to keep asking these questions remains.

The Federal Council report as an example

It is precisely this gap between competence and digital literacy that is exemplified in the Federal Council’s latest report on the «digital divide in Switzerland» .

The report distinguishes between three levels of digital inequality: access to devices (the «access divide»), existing competencies (the «skills divide») and the benefits an individual derives from them (the «usage divide»). A fourth level is also mentioned: «the individual’s ability to adapt to the ever-faster and more far-reaching changes brought about by the development of digital technologies».

However, this is then clarified in the same paragraph: the fourth level is «understood as a skills issue and, as such, is classified under the second level (Skills Divide) of the digital divide».

For us, it is precisely this fourth level that is key. In our view, it falls into the category of literacy, not competencies. A competence issue can be resolved through courses and training, whereas a cultural technique must be continually relearned. Anyone who treats the fourth level as a purely skills-based problem underestimates the fact that the very foundation of understanding is constantly shifting, and consequently devises inappropriate measures.

And now, AI literacy

With AI literacy, we see the same pattern as with digital literacy, only at a faster pace. The tools change more rapidly and the interests behind them are often difficult to fathom.

AI application competence therefore means knowing how a prompt works and what AI can and cannot do. AI literacy, on the other hand, means understanding why AI executives have claimed that, in a few years’ time, AI will have the power to wipe out the entire human race.

Another example: AI literacy also means understanding why certain AI tools are offered for free or at a particularly low cost, and what dependencies this creates. Anyone who uses a tool without knowing its business model will find it difficult to put their own use of it into perspective.

Why we are committed to literacy

In our work, we focus on literacy for one simple reason: application competence alone is not enough, and others are better at advocacy. Literacy is the lever we have left to achieve a form of digitalisation that serves society rather than the other way round.

We help society to understand what it can and wants to expect from digitalisation. An informed civil society can therefore engage more critically in political processes such as referendums and, for example, better assess calls for bans.

It is precisely this approach that we consistently apply in our projects – not by explaining tools, but by creating spaces where people learn to constantly question systems anew. This remains our benchmark for everything that will be discussed in future regarding digital and AI literacy.