Another professional professional will not be able to tell you what to do on the machine. Evening understand when it pays off and when it doesn’t.
For years, the conversation about technology education revolved around a pretty simple idea: learn to program, automate and control hardware. This logic is useful, but not enough. The emergence of generative artificial intelligence has changed the order of priorities. Now many companies are not only looking for technical files. There are people who can interpret results, spot errors, formulate good questions, and follow their own criteria.
This is because AI makes things faster, but it doesn’t replace understanding. You can write text, summarize a form, or generate code in a sequence, but you can’t decide for yourself if the result is heartfelt, if it matches the real goal, or if you present a message that may be very important. This is exactly where it appears a skill that once seemed secondary is now becoming central: AI literacy.
Learning literacy in IA does not mean learning how to use a spelling tool. I understand what the model does, what it doesn’t, where the misunderstanding is and why it gets stuck in use. It also includes knowing how to differentiate between automating a task and delegating a task. This distinction arises with the emphasis of the European Commission, which further considers the level of training, experience and context of use before requiring sufficient use of these systems.
In education, the debate should not only focus on whether or not artificial intelligence enters the classroom. Here I am. The relevant question now is the following: The type of students we form know how to work critically with it.
The OECD and the European Commission are heading in this direction with their AI Literacy Mark integrate the topic into different materials and not as tired aislado.
This change is not just for the computer. It affects marketing, health, finance, education and business management. In all these areas, AI intervenes in the areas of analysis, redaction, classification or recommendation. Therefore, understanding it becomes a specialty and becomes a basic skill. Just as many years ago we began to consider it necessary to deal with computer hardware, we now need to understand how an AI system works and what its limitations are.
It also changes the idea of implementation. The Future of Jobs 2025 report highlights that analytical thinking is the most valuable competency for companies, and that the capabilities associated with artificial intelligence and big data will grow the most by 2030. This points to a fairly simple idea: the market is not just more technology, more ability to think with technology.
There is a clear opportunity here for universities, technical schools and advanced training centers. They learn to understand AI before they use it, they learn with confidence, in differentiation and in realization. Don’t just add a form above the prompts. There is a culture of use: clear criteria, concrete examples, teacher training, evaluation and transparency of how technology is integrated every day.
In Europe, this discussion has an old dimension. The European Commission is supporting AI literacy with its Talent, Skills and Competitiveness Strategy and has announced measures to expand training tailored to sectors and job profiles. If the region wants to support its own model, it requires capable talents combine technical criteria, responsibility and critical thinking. And don’t improvise with speeches. It is constructed in classrooms, on study plans and in the way of assessment.
In the next decade, the well-prepared person will not be the one who remembers things best, until he is the one who knows best how to think with them without stopping to think about it in the same way. And an educational institution or company will not be surprised when it decides to use AI until to show that he knows how to train people to use it wisely.
*** Alessandro Orru He is the Director of Marketing and Communications at IMMUNE Technology Institute.
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